From f170c935a1393bdf7332f2c558c43068619cd511 Mon Sep 17 00:00:00 2001 From: swagger-coder <1581148245@qq.com> Date: Thu, 5 Dec 2024 21:32:35 +0800 Subject: [PATCH] =?UTF-8?q?=E3=80=90PPMix=20No.35=E3=80=91add=20diffsinger?= =?UTF-8?q?=20(#860)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: luyao-cv <1367355728@qq.com> --- paddlemix/config/diffsinger/acoustic.yaml | 135 +++ paddlemix/config/diffsinger/base.yaml | 94 ++ .../diffsinger/templates/config_acoustic.yaml | 102 +++ .../diffsinger/templates/config_variance.yaml | 123 +++ paddlemix/config/diffsinger/variance.yaml | 143 +++ paddlemix/examples/diffsinger/README.md | 26 + .../examples/diffsinger/requirements.txt | 12 + paddlemix/examples/diffsinger/run_predict.py | 196 +++++ paddlemix/examples/diffsinger/run_predict.sh | 15 + ...64\345\206\215\350\247\201\345\225\212.ds" | 362 ++++++++ ...1_\351\200\215\351\201\245\344\273\231.ds" | 146 +++ ...00\345\215\212\344\270\200\345\215\212.ds" | 698 +++++++++++++++ ...22\345\250\207\345\205\253\350\277\236.ds" | 14 + .../samples/04_\344\273\231\347\221\266.ds" | 122 +++ ...5_\346\201\213\344\272\272\345\277\203.ds" | 14 + ...6_\344\270\215\350\260\223\344\276\240.ds" | 418 +++++++++ ...37\350\212\261\346\234\210\345\244\234.ds" | 828 ++++++++++++++++++ .../examples/diffsinger/samples/credits.txt | 42 + paddlemix/models/__init__.py | 1 + .../diffsinger/basics/base_augmentation.py | 46 + .../diffsinger/basics/base_binarizer.py | 330 +++++++ .../models/diffsinger/basics/base_dataset.py | 67 ++ .../models/diffsinger/basics/base_exporter.py | 72 ++ .../models/diffsinger/basics/base_module.py | 34 + paddlemix/models/diffsinger/basics/base_pe.py | 18 + .../diffsinger/basics/base_svs_infer.py | 149 ++++ .../models/diffsinger/basics/base_vocoder.py | 37 + .../diffsinger/inference/ds_acoustic.py | 256 ++++++ .../models/diffsinger/modules/__init__.py | 13 + 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.../diffsinger/modules/metrics/curve.py | 48 + .../diffsinger/modules/metrics/duration.py | 110 +++ .../diffsinger/modules/nsf_hifigan/env.py | 46 + .../diffsinger/modules/nsf_hifigan/models.py | 380 ++++++++ .../diffsinger/modules/nsf_hifigan/nvSTFT.py | 104 +++ .../diffsinger/modules/nsf_hifigan/utils.py | 27 + .../models/diffsinger/modules/pe/__init__.py | 32 + paddlemix/models/diffsinger/modules/pe/pm.py | 30 + paddlemix/models/diffsinger/modules/pe/pw.py | 36 + .../diffsinger/modules/pe/rmvpe/__init__.py | 19 + .../diffsinger/modules/pe/rmvpe/constants.py | 21 + .../diffsinger/modules/pe/rmvpe/deepunet.py | 194 ++++ .../diffsinger/modules/pe/rmvpe/inference.py | 80 ++ .../diffsinger/modules/pe/rmvpe/model.py | 54 ++ .../models/diffsinger/modules/pe/rmvpe/seq.py | 30 + .../diffsinger/modules/pe/rmvpe/spec.py | 65 ++ .../diffsinger/modules/pe/rmvpe/utils.py | 54 ++ .../models/diffsinger/modules/toplevel.py | 323 +++++++ .../diffsinger/modules/vocoders/__init__.py | 15 + .../diffsinger/modules/vocoders/ddsp.py | 176 ++++ .../modules/vocoders/nsf_hifigan.py | 138 +++ .../diffsinger/modules/vocoders/registry.py | 34 + paddlemix/models/diffsinger/utils/__init__.py | 345 ++++++++ .../diffsinger/utils/binarizer_utils.py | 241 +++++ .../diffsinger/utils/decomposed_waveform.py | 333 +++++++ paddlemix/models/diffsinger/utils/hparams.py | 153 ++++ .../diffsinger/utils/indexed_datasets.py | 109 +++ .../models/diffsinger/utils/infer_utils.py | 115 +++ .../diffsinger/utils/multiprocess_utils.py | 63 ++ .../models/diffsinger/utils/paddle_aux.py | 215 +++++ .../models/diffsinger/utils/phoneme_utils.py | 102 +++ .../models/diffsinger/utils/pitch_utils.py | 41 + paddlemix/models/diffsinger/utils/plot.py | 145 +++ .../models/diffsinger/utils/text_encoder.py | 64 ++ 82 files changed, 11386 insertions(+) create mode 100644 paddlemix/config/diffsinger/acoustic.yaml create mode 100644 paddlemix/config/diffsinger/base.yaml create mode 100644 paddlemix/config/diffsinger/templates/config_acoustic.yaml create mode 100644 paddlemix/config/diffsinger/templates/config_variance.yaml create mode 100644 paddlemix/config/diffsinger/variance.yaml create mode 100644 paddlemix/examples/diffsinger/README.md create mode 100644 paddlemix/examples/diffsinger/requirements.txt create mode 100644 paddlemix/examples/diffsinger/run_predict.py create mode 100644 paddlemix/examples/diffsinger/run_predict.sh create mode 100644 "paddlemix/examples/diffsinger/samples/00_\346\210\221\345\244\232\346\203\263\350\257\264\345\206\215\350\247\201\345\225\212.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/01_\351\200\215\351\201\245\344\273\231.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/02_\344\270\200\345\215\212\344\270\200\345\215\212.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/03_\346\222\222\345\250\207\345\205\253\350\277\236.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/04_\344\273\231\347\221\266.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/05_\346\201\213\344\272\272\345\277\203.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/06_\344\270\215\350\260\223\344\276\240.ds" create mode 100644 "paddlemix/examples/diffsinger/samples/07_\346\230\245\346\261\237\350\212\261\346\234\210\345\244\234.ds" create mode 100644 paddlemix/examples/diffsinger/samples/credits.txt create mode 100644 paddlemix/models/diffsinger/basics/base_augmentation.py create mode 100644 paddlemix/models/diffsinger/basics/base_binarizer.py create mode 100644 paddlemix/models/diffsinger/basics/base_dataset.py create mode 100644 paddlemix/models/diffsinger/basics/base_exporter.py create mode 100644 paddlemix/models/diffsinger/basics/base_module.py create mode 100644 paddlemix/models/diffsinger/basics/base_pe.py create mode 100644 paddlemix/models/diffsinger/basics/base_svs_infer.py create mode 100644 paddlemix/models/diffsinger/basics/base_vocoder.py create mode 100644 paddlemix/models/diffsinger/inference/ds_acoustic.py create mode 100644 paddlemix/models/diffsinger/modules/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/aux_decoder/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/aux_decoder/convnext.py create mode 100644 paddlemix/models/diffsinger/modules/backbones/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/backbones/lynxnet.py create mode 100644 paddlemix/models/diffsinger/modules/backbones/wavenet.py create mode 100644 paddlemix/models/diffsinger/modules/commons/common_layers.py create mode 100644 paddlemix/models/diffsinger/modules/commons/espnet_positional_embedding.py create mode 100644 paddlemix/models/diffsinger/modules/compat.py create mode 100644 paddlemix/models/diffsinger/modules/core/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/core/ddpm.py create mode 100644 paddlemix/models/diffsinger/modules/core/reflow.py create mode 100644 paddlemix/models/diffsinger/modules/fastspeech/acoustic_encoder.py create mode 100644 paddlemix/models/diffsinger/modules/fastspeech/param_adaptor.py create mode 100644 paddlemix/models/diffsinger/modules/fastspeech/tts_modules.py create mode 100644 paddlemix/models/diffsinger/modules/fastspeech/variance_encoder.py create mode 100644 paddlemix/models/diffsinger/modules/hnsep/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/hnsep/vr/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/hnsep/vr/layers.py create mode 100644 paddlemix/models/diffsinger/modules/hnsep/vr/nets.py create mode 100644 paddlemix/models/diffsinger/modules/metrics/curve.py create mode 100644 paddlemix/models/diffsinger/modules/metrics/duration.py create mode 100644 paddlemix/models/diffsinger/modules/nsf_hifigan/env.py create mode 100644 paddlemix/models/diffsinger/modules/nsf_hifigan/models.py create mode 100644 paddlemix/models/diffsinger/modules/nsf_hifigan/nvSTFT.py create mode 100644 paddlemix/models/diffsinger/modules/nsf_hifigan/utils.py create mode 100644 paddlemix/models/diffsinger/modules/pe/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/pe/pm.py create mode 100644 paddlemix/models/diffsinger/modules/pe/pw.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/constants.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/deepunet.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/inference.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/model.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/seq.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/spec.py create mode 100644 paddlemix/models/diffsinger/modules/pe/rmvpe/utils.py create mode 100644 paddlemix/models/diffsinger/modules/toplevel.py create mode 100644 paddlemix/models/diffsinger/modules/vocoders/__init__.py create mode 100644 paddlemix/models/diffsinger/modules/vocoders/ddsp.py create mode 100644 paddlemix/models/diffsinger/modules/vocoders/nsf_hifigan.py create mode 100644 paddlemix/models/diffsinger/modules/vocoders/registry.py create mode 100644 paddlemix/models/diffsinger/utils/__init__.py create mode 100644 paddlemix/models/diffsinger/utils/binarizer_utils.py create mode 100644 paddlemix/models/diffsinger/utils/decomposed_waveform.py create mode 100644 paddlemix/models/diffsinger/utils/hparams.py create mode 100644 paddlemix/models/diffsinger/utils/indexed_datasets.py create mode 100644 paddlemix/models/diffsinger/utils/infer_utils.py create mode 100644 paddlemix/models/diffsinger/utils/multiprocess_utils.py create mode 100644 paddlemix/models/diffsinger/utils/paddle_aux.py create mode 100644 paddlemix/models/diffsinger/utils/phoneme_utils.py create mode 100644 paddlemix/models/diffsinger/utils/pitch_utils.py create mode 100644 paddlemix/models/diffsinger/utils/plot.py create mode 100644 paddlemix/models/diffsinger/utils/text_encoder.py diff --git a/paddlemix/config/diffsinger/acoustic.yaml b/paddlemix/config/diffsinger/acoustic.yaml new file mode 100644 index 000000000..a0a880a6d --- /dev/null +++ b/paddlemix/config/diffsinger/acoustic.yaml @@ -0,0 +1,135 @@ +base_config: + - configs/base.yaml + +task_cls: training.acoustic_task.AcousticTask +num_spk: 1 +speakers: + - opencpop +spk_ids: [] +test_prefixes: [ + '2044', + '2086', + '2092', + '2093', + '2100', +] + +vocoder: NsfHifiGAN +vocoder_ckpt: /home/ypf/workspace2/code/Paddle_test/DiffSinger_infer/checkpoints/nsf_hifigan_44.1k_hop512_128bin_2024.02_pd/model.ckpt +audio_sample_rate: 44100 +audio_num_mel_bins: 128 +hop_size: 512 # Hop size. +fft_size: 2048 # FFT size. +win_size: 2048 # FFT size. +fmin: 40 +fmax: 16000 + +binarization_args: + shuffle: true + num_workers: 0 +augmentation_args: + random_pitch_shifting: + enabled: false + range: [-5., 5.] + scale: 0.75 + fixed_pitch_shifting: + enabled: false + targets: [-5., 5.] + scale: 0.5 + random_time_stretching: + enabled: false + range: [0.5, 2.] + scale: 0.75 + +raw_data_dir: 'data/opencpop/raw' +binary_data_dir: 'data/opencpop/binary' +binarizer_cls: preprocessing.acoustic_binarizer.AcousticBinarizer +dictionary: dictionaries/opencpop-extension.txt +spec_min: [-12] +spec_max: [0] +mel_vmin: -14. +mel_vmax: 4. +mel_base: 'e' +energy_smooth_width: 0.12 +breathiness_smooth_width: 0.12 +voicing_smooth_width: 0.12 +tension_smooth_width: 0.12 + +use_spk_id: false +use_energy_embed: false +use_breathiness_embed: false +use_voicing_embed: false +use_tension_embed: false +use_key_shift_embed: false +use_speed_embed: false + +diffusion_type: reflow +time_scale_factor: 1000 +timesteps: 1000 +max_beta: 0.02 +rel_pos: true +sampling_algorithm: euler +sampling_steps: 20 +diff_accelerator: ddim +diff_speedup: 10 +hidden_size: 256 +backbone_type: 'lynxnet' +backbone_args: + num_channels: 1024 + num_layers: 6 + kernel_size: 31 + dropout_rate: 0.0 +main_loss_type: l2 +main_loss_log_norm: false +schedule_type: 'linear' + +# shallow diffusion +use_shallow_diffusion: true +T_start: 0.4 +T_start_infer: 0.4 +K_step: 400 +K_step_infer: 400 + +shallow_diffusion_args: + train_aux_decoder: true + train_diffusion: true + val_gt_start: false + aux_decoder_arch: convnext + aux_decoder_args: + num_channels: 512 + num_layers: 6 + kernel_size: 7 + dropout_rate: 0.1 + aux_decoder_grad: 0.1 + +lambda_aux_mel_loss: 0.2 + +# train and eval +num_sanity_val_steps: 1 +optimizer_args: + lr: 0.0006 +lr_scheduler_args: + step_size: 10000 + gamma: 0.75 +max_batch_frames: 50000 +max_batch_size: 64 +dataset_size_key: 'lengths' +val_with_vocoder: true +val_check_interval: 2000 +num_valid_plots: 10 +max_updates: 160000 +num_ckpt_keep: 5 +permanent_ckpt_start: 80000 +permanent_ckpt_interval: 20000 + +finetune_enabled: false +finetune_ckpt_path: null + +finetune_ignored_params: + - model.fs2.encoder.embed_tokens + - model.fs2.txt_embed + - model.fs2.spk_embed +finetune_strict_shapes: true + +freezing_enabled: false +frozen_params: [] diff --git a/paddlemix/config/diffsinger/base.yaml b/paddlemix/config/diffsinger/base.yaml new file mode 100644 index 000000000..3108c0c34 --- /dev/null +++ b/paddlemix/config/diffsinger/base.yaml @@ -0,0 +1,94 @@ +# task +task_cls: null + +############# +# dataset +############# +sort_by_len: true +raw_data_dir: null +binary_data_dir: null +binarizer_cls: null +binarization_args: + shuffle: false + num_workers: 0 + +audio_sample_rate: 44100 +hop_size: 512 +win_size: 2048 +fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter +sampler_frame_count_grid: 6 +ds_workers: 4 +dataloader_prefetch_factor: 2 + +######### +# model +######### +hidden_size: 256 +dropout: 0.1 +use_pos_embed: true +enc_layers: 4 +num_heads: 2 +enc_ffn_kernel_size: 9 +ffn_act: gelu +use_spk_id: false + +########### +# optimization +########### +optimizer_args: + optimizer_cls: torch.optim.AdamW + lr: 0.0004 + beta1: 0.9 + beta2: 0.98 + weight_decay: 0 +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.StepLR + step_size: 50000 + gamma: 0.5 +clip_grad_norm: 1 + +########### +# train and eval +########### +num_ckpt_keep: 5 +accumulate_grad_batches: 1 +log_interval: 100 +num_sanity_val_steps: 1 # steps of validation at the beginning +val_check_interval: 2000 +max_updates: 120000 +max_batch_frames: 32000 +max_batch_size: 100000 +max_val_batch_frames: 60000 +max_val_batch_size: 1 +pe: rmvpe #parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' +f0_min: 65 +f0_max: 1100 +num_valid_plots: 10 + +########### +# pytorch lightning +# Read https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api for possible values +########### +pl_trainer_accelerator: 'auto' +pl_trainer_devices: [4,5,6,7] # 'auto' +pl_trainer_precision: '16-mixed' +pl_trainer_num_nodes: 1 +pl_trainer_strategy: + name: auto + process_group_backend: nccl + find_unused_parameters: false +nccl_p2p: true + +########### +# finetune +########### +finetune_enabled: false +finetune_ckpt_path: null +finetune_ignored_params: [] +finetune_strict_shapes: true + +freezing_enabled: false +frozen_params: [] diff --git a/paddlemix/config/diffsinger/templates/config_acoustic.yaml b/paddlemix/config/diffsinger/templates/config_acoustic.yaml new file mode 100644 index 000000000..a9453a368 --- /dev/null +++ b/paddlemix/config/diffsinger/templates/config_acoustic.yaml @@ -0,0 +1,102 @@ +base_config: configs/acoustic.yaml + +raw_data_dir: + - data/xxx1/raw + - data/xxx2/raw +speakers: + - speaker1 + - speaker2 +spk_ids: [] +test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 +dictionary: dictionaries/opencpop-extension.txt +binary_data_dir: data/xxx/binary +binarization_args: + num_workers: 0 +pe: parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' +vocoder: NsfHifiGAN +vocoder_ckpt: checkpoints/nsf_hifigan_44.1k_hop512_128bin_2024.02/model.ckpt + +use_spk_id: false +num_spk: 1 + +# NOTICE: before enabling variance embeddings, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#choosing-variance-parameters +use_energy_embed: false +use_breathiness_embed: false +use_voicing_embed: false +use_tension_embed: false + +use_key_shift_embed: true +use_speed_embed: true + +augmentation_args: + random_pitch_shifting: + enabled: true + range: [-5., 5.] + scale: 0.75 + fixed_pitch_shifting: + enabled: false + targets: [-5., 5.] + scale: 0.5 + random_time_stretching: + enabled: true + range: [0.5, 2.] + scale: 0.75 + +# diffusion and shallow diffusion +diffusion_type: reflow +use_shallow_diffusion: true +T_start: 0.4 +T_start_infer: 0.4 +K_step: 300 +K_step_infer: 300 +backbone_type: 'lynxnet' +backbone_args: + num_channels: 1024 + num_layers: 6 + kernel_size: 31 + dropout_rate: 0.0 +#backbone_type: 'wavenet' +#backbone_args: +# num_channels: 512 +# num_layers: 20 +# dilation_cycle_length: 4 +shallow_diffusion_args: + train_aux_decoder: true + train_diffusion: true + val_gt_start: false + aux_decoder_arch: convnext + aux_decoder_args: + num_channels: 512 + num_layers: 6 + kernel_size: 7 + dropout_rate: 0.1 + aux_decoder_grad: 0.1 +lambda_aux_mel_loss: 0.2 + +optimizer_args: + lr: 0.0006 +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.StepLR + step_size: 10000 + gamma: 0.75 +max_batch_frames: 50000 +max_batch_size: 64 +max_updates: 160000 + +num_valid_plots: 10 +val_with_vocoder: true +val_check_interval: 2000 +num_ckpt_keep: 5 +permanent_ckpt_start: 120000 +permanent_ckpt_interval: 20000 +pl_trainer_devices: 'auto' +pl_trainer_precision: '16-mixed' diff --git a/paddlemix/config/diffsinger/templates/config_variance.yaml b/paddlemix/config/diffsinger/templates/config_variance.yaml new file mode 100644 index 000000000..daa8e15dc --- /dev/null +++ b/paddlemix/config/diffsinger/templates/config_variance.yaml @@ -0,0 +1,123 @@ +base_config: + - configs/variance.yaml + +raw_data_dir: + - data/xxx1/raw + - data/xxx2/raw +speakers: + - speaker1 + - speaker2 +spk_ids: [] +test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 +dictionary: dictionaries/opencpop-extension.txt +binary_data_dir: data/xxx/binary +binarization_args: + num_workers: 0 + +pe: parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' + +use_spk_id: false +num_spk: 1 +# NOTICE: before enabling variance modules, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#mutual-influence-between-variance-modules +predict_dur: false +predict_pitch: false +# NOTICE: before enabling variance predictions, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#choosing-variance-parameters +predict_energy: false +predict_breathiness: false +predict_voicing: false +predict_tension: false + +energy_db_min: -96.0 +energy_db_max: -12.0 + +breathiness_db_min: -96.0 +breathiness_db_max: -20.0 + +voicing_db_min: -96.0 +voicing_db_max: -12.0 + +tension_logit_min: -10.0 +tension_logit_max: 10.0 + +hidden_size: 256 +dur_prediction_args: + arch: fs2 + hidden_size: 512 + dropout: 0.1 + num_layers: 5 + kernel_size: 3 + log_offset: 1.0 + loss_type: mse + lambda_pdur_loss: 0.3 + lambda_wdur_loss: 1.0 + lambda_sdur_loss: 3.0 + +use_melody_encoder: false +melody_encoder_args: + hidden_size: 128 + enc_layers: 4 +use_glide_embed: false +glide_types: [up, down] +glide_embed_scale: 11.313708498984760 # sqrt(128) + +diffusion_type: reflow + +pitch_prediction_args: + pitd_norm_min: -8.0 + pitd_norm_max: 8.0 + pitd_clip_min: -12.0 + pitd_clip_max: 12.0 + repeat_bins: 64 + backbone_type: 'wavenet' + backbone_args: + num_layers: 20 + num_channels: 256 + dilation_cycle_length: 5 +# backbone_type: 'lynxnet' +# backbone_args: +# num_layers: 6 +# num_channels: 512 + +variances_prediction_args: + total_repeat_bins: 48 + backbone_type: 'wavenet' + backbone_args: + num_layers: 10 + num_channels: 192 + dilation_cycle_length: 4 +# backbone_type: 'lynxnet' +# backbone_args: +# num_layers: 6 +# num_channels: 384 + +lambda_dur_loss: 1.0 +lambda_pitch_loss: 1.0 +lambda_var_loss: 1.0 + +optimizer_args: + lr: 0.0006 +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.StepLR + step_size: 10000 + gamma: 0.75 +max_batch_frames: 80000 +max_batch_size: 48 +max_updates: 160000 + +num_valid_plots: 10 +val_check_interval: 2000 +num_ckpt_keep: 5 +permanent_ckpt_start: 80000 +permanent_ckpt_interval: 10000 +pl_trainer_devices: 'auto' +pl_trainer_precision: '16-mixed' diff --git a/paddlemix/config/diffsinger/variance.yaml b/paddlemix/config/diffsinger/variance.yaml new file mode 100644 index 000000000..e9a7764f2 --- /dev/null +++ b/paddlemix/config/diffsinger/variance.yaml @@ -0,0 +1,143 @@ +base_config: + - configs/base.yaml + +task_cls: training.variance_task.VarianceTask +num_spk: 1 +speakers: + - opencpop +spk_ids: [] +test_prefixes: [ + '2044', + '2086', + '2092', + '2093', + '2100', +] + +audio_sample_rate: 44100 +hop_size: 512 # Hop size. +fft_size: 2048 # FFT size. +win_size: 2048 # FFT size. +midi_smooth_width: 0.06 # in seconds + +binarization_args: + shuffle: true + num_workers: 0 + prefer_ds: false + +raw_data_dir: 'data/opencpop_variance/raw' +binary_data_dir: 'data/opencpop_variance/binary' +binarizer_cls: preprocessing.variance_binarizer.VarianceBinarizer +dictionary: dictionaries/opencpop-extension.txt + +use_spk_id: false + +rel_pos: true +hidden_size: 256 + +predict_dur: true +predict_pitch: true +predict_energy: false +predict_breathiness: false +predict_voicing: false +predict_tension: false + +dur_prediction_args: + arch: fs2 + hidden_size: 512 + dropout: 0.1 + num_layers: 5 + kernel_size: 3 + log_offset: 1.0 + loss_type: mse + lambda_pdur_loss: 0.3 + lambda_wdur_loss: 1.0 + lambda_sdur_loss: 3.0 + +use_melody_encoder: false +melody_encoder_args: + hidden_size: 128 + enc_layers: 4 +use_glide_embed: false +glide_types: [up, down] +glide_embed_scale: 11.313708498984760 # sqrt(128) + +pitch_prediction_args: + pitd_norm_min: -8.0 + pitd_norm_max: 8.0 + pitd_clip_min: -12.0 + pitd_clip_max: 12.0 + repeat_bins: 64 + backbone_type: 'wavenet' + backbone_args: + num_layers: 20 + num_channels: 256 + dilation_cycle_length: 5 + +energy_db_min: -96.0 +energy_db_max: -12.0 +energy_smooth_width: 0.12 + +breathiness_db_min: -96.0 +breathiness_db_max: -20.0 +breathiness_smooth_width: 0.12 +voicing_db_min: -96.0 +voicing_db_max: -12.0 +voicing_smooth_width: 0.12 + +tension_logit_min: -10.0 +tension_logit_max: 10.0 +tension_smooth_width: 0.12 + +variances_prediction_args: + total_repeat_bins: 48 + backbone_type: 'wavenet' + backbone_args: + num_layers: 10 + num_channels: 192 + dilation_cycle_length: 4 + +lambda_dur_loss: 1.0 +lambda_pitch_loss: 1.0 +lambda_var_loss: 1.0 + +diffusion_type: reflow # ddpm +time_scale_factor: 1000 +schedule_type: 'linear' +K_step: 1000 +timesteps: 1000 +max_beta: 0.02 +main_loss_type: l2 +main_loss_log_norm: true +sampling_algorithm: euler +sampling_steps: 20 +diff_accelerator: ddim +diff_speedup: 10 + +# train and eval +num_sanity_val_steps: 1 +optimizer_args: + lr: 0.0006 +lr_scheduler_args: + step_size: 10000 + gamma: 0.75 +max_batch_frames: 80000 +max_batch_size: 48 +dataset_size_key: 'lengths' +val_check_interval: 2000 +num_valid_plots: 10 +max_updates: 160000 +num_ckpt_keep: 5 +permanent_ckpt_start: 80000 +permanent_ckpt_interval: 10000 + +finetune_enabled: false +finetune_ckpt_path: null +finetune_ignored_params: + - model.spk_embed + - model.fs2.txt_embed + - model.fs2.encoder.embed_tokens +finetune_strict_shapes: true + +freezing_enabled: false +frozen_params: [] diff --git a/paddlemix/examples/diffsinger/README.md b/paddlemix/examples/diffsinger/README.md new file mode 100644 index 000000000..e666d17e2 --- /dev/null +++ b/paddlemix/examples/diffsinger/README.md @@ -0,0 +1,26 @@ +## 1. DiffSinger 模型介绍 + +该模型是 [DiffSinger（由 OpenVPI 维护的版本）](https://github.com/openvpi/DiffSinger) 的 Paddle 实现。 + +[DiffSinger](https://arxiv.org/abs/2105.02446) 是目前最先进的歌声合成（Singing Voice Synthesis, SVS）模型。OpenVPI 维护的版本对其进行了进一步优化，增加了更多的功能。 + +本仓库目前仅支持推理功能。 + + +## 2. 环境准备 + +1） [安装PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP?tab=readme-ov-file#%E5%AE%89%E8%A3%85) + +2）[安装 PaddleMix 环境依赖包](https://github.com/PaddlePaddle/PaddleMIX/tree/b4f97ff859e1964c839fc5fab94f7ba63b1e5959?tab=readme-ov-file#%E5%AE%89%E8%A3%85) + +3）使用 pip 安装依赖: + ```bash + pip install -r requirements.txt + ``` + +## 4. 快速开始 +完成环境准备后，运行以下脚本： + +```bash +bash run_predict.sh +``` diff --git a/paddlemix/examples/diffsinger/requirements.txt b/paddlemix/examples/diffsinger/requirements.txt new file mode 100644 index 000000000..b4cdd2658 --- /dev/null +++ b/paddlemix/examples/diffsinger/requirements.txt @@ -0,0 +1,12 @@ +click +h5py +librosa<0.10.0 +matplotlib +MonkeyType==23.3.0 +numpy<2.0.0 +praat-parselmouth==0.4.3 +pyworld==0.3.4 +PyYAML +resampy +scipy>=1.10.0 +tqdm diff --git a/paddlemix/examples/diffsinger/run_predict.py b/paddlemix/examples/diffsinger/run_predict.py new file mode 100644 index 000000000..42d2e878a --- /dev/null +++ b/paddlemix/examples/diffsinger/run_predict.py @@ -0,0 +1,196 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import sys +from dataclasses import dataclass, field +from pathlib import Path +from typing import List, Optional, Tuple + +from paddlenlp.trainer import PdArgumentParser + +parent_path = os.path.abspath(os.path.join(__file__, *([".."] * 4))) +sys.path.insert(0, parent_path) +from paddlemix.models.diffsinger.inference.ds_acoustic import DiffSingerAcousticInfer +from paddlemix.models.diffsinger.utils.hparams import hparams, set_hparams +from paddlemix.models.diffsinger.utils.infer_utils import ( + parse_commandline_spk_mix, + trans_key, +) + +root_dir = Path(__file__).resolve().parent.parent +os.environ["PYTHONPATH"] = str(root_dir) +sys.path.insert(0, str(root_dir)) + + +@dataclass +class DiffsingerArguments: + """ + Arguments for DiffSinger inference + """ + + # Input/Output arguments + proj: str = field(metadata={"help": "Path to the DS_FILE for inference"}) + exp: str = field(metadata={"help": "Selection of model"}) + out: Optional[str] = field(default="./output", metadata={"help": "Path of the output folder"}) + title: Optional[str] = field(default=None, metadata={"help": "Title of output file"}) + + # Model arguments + ckpt: Optional[int] = field(default=None, metadata={"help": "Selection of checkpoint training steps"}) + spk: Optional[str] = field(default=None, metadata={"help": "Speaker name or mixture of speakers"}) + num: int = field(default=1, metadata={"help": "Number of runs"}) + key: int = field(default=0, metadata={"help": "Key transition of pitch"}) + gender: Optional[float] = field(default=None, metadata={"help": "Formant shifting (gender control)"}) + seed: int = field(default=-1, metadata={"help": "Random seed of the inference"}) + depth: Optional[float] = field(default=None, metadata={"help": "Shallow diffusion depth"}) + steps: Optional[int] = field(default=None, metadata={"help": "Diffusion sampling steps"}) + mel: bool = field(default=False, metadata={"help": "Save intermediate mel format instead of waveform"}) + + +def find_exp(exp: str) -> str: + """Find ckpt file in the specified directory""" + exp_path = Path(exp) + + if not exp_path.exists(): + raise ValueError(f"The specified path '{exp}' does not exist.") + + # Find ckpt file in the directory + ckpt_files = list(exp_path.glob("*.ckpt")) + if not ckpt_files: + raise ValueError(f"No .ckpt file found in directory '{exp}'") + if len(ckpt_files) > 1: + print(f"| Warning: Multiple ckpt files found, using the first one: {ckpt_files[0]}") + + print(f"| found ckpt file: {ckpt_files[0]}") + return exp + + +def process_hparams(hparams: dict, args: DiffsingerArguments, params: List[dict]) -> Tuple[dict, List[dict]]: + """Process and update hyperparameters""" + # Validate vocoder checkpoint + assert ( + args.mel or Path(hparams["vocoder_ckpt"]).exists() + ), f"Vocoder ckpt '{hparams['vocoder_ckpt']}' not found. Please put it to the checkpoints directory to run inference." + + # Update speedup parameters + if "diff_speedup" not in hparams and "pndm_speedup" in hparams: + hparams["diff_speedup"] = hparams["pndm_speedup"] + + # Set default parameters if not present + if "T_start" not in hparams: + hparams["T_start"] = 1 - hparams["K_step"] / hparams["timesteps"] + + if "T_start_infer" not in hparams: + hparams["T_start_infer"] = 1 - hparams["K_step_infer"] / hparams["timesteps"] + + if "sampling_steps" not in hparams: + if hparams["use_shallow_diffusion"]: + hparams["sampling_steps"] = hparams["K_step_infer"] // hparams["diff_speedup"] + else: + hparams["sampling_steps"] = hparams["timesteps"] // hparams["diff_speedup"] + + if "time_scale_factor" not in hparams: + hparams["time_scale_factor"] = hparams["timesteps"] + + # Process depth parameter + if args.depth is not None: + assert ( + args.depth <= 1 - hparams["T_start"] + ), f"Depth should not be larger than 1 - T_start ({1 - hparams['T_start']})" + hparams["K_step_infer"] = round(hparams["timesteps"] * args.depth) + hparams["T_start_infer"] = 1 - args.depth + + # Process steps parameter + if args.steps is not None: + if hparams["use_shallow_diffusion"]: + step_size = (1 - hparams["T_start_infer"]) / args.steps + if "K_step_infer" in hparams: + hparams["diff_speedup"] = round(step_size * hparams["K_step_infer"]) + elif "timesteps" in hparams: + hparams["diff_speedup"] = round(hparams["timesteps"] / args.steps) + hparams["sampling_steps"] = args.steps + + # Process speaker mixture + + spk_mix = parse_commandline_spk_mix(args.spk) if hparams["use_spk_id"] and args.spk is not None else None + + # Update parameters + for param in params: + if args.gender is not None and hparams["use_key_shift_embed"]: + param["gender"] = args.gender + if spk_mix is not None: + param["spk_mix"] = spk_mix + + return params, spk_mix + + +def run_acoustic_inference(args: DiffsingerArguments): + """Run acoustic model inference""" + # Process input/output paths + proj_path = Path(args.proj) + name = proj_path.stem if not args.title else args.title + out_path = Path(args.out) if args.out else proj_path.parent + print(proj_path) + # Load parameters + with open(proj_path, "r", encoding="utf-8") as f: + params = json.load(f) + if not isinstance(params, list): + params = [params] + if len(params) == 0: + print("The input file is empty.") + return + + # Process key transition + if args.key != 0: + params = trans_key(params, args.key) + if not args.title: + name += f"{args.key:+d}key" + print(f"| key transition: {args.key:+d}") + + # Setup model + sys.argv = [sys.argv[0], "--exp_name", args.exp, "--infer"] + + set_hparams() + + # Process hyperparameters + + params, spk_mix = process_hparams(hparams, args, params) + + # Run inference + infer_ins = DiffSingerAcousticInfer(load_vocoder=not args.mel, ckpt_steps=args.ckpt) + print(f"| Model: {type(infer_ins.model)}") + + try: + infer_ins.run_inference( + params, out_dir=out_path, title=name, num_runs=args.num, spk_mix=spk_mix, seed=args.seed, save_mel=args.mel + ) + except KeyboardInterrupt: + sys.exit(-1) + + +def main(): + """Main entry point""" + parser = PdArgumentParser(DiffsingerArguments) + args = parser.parse_args_into_dataclasses()[0] + + # Validate and process experiment name + args.exp = find_exp(args.exp) + + # Run inference + run_acoustic_inference(args) + + +if __name__ == "__main__": + main() diff --git a/paddlemix/examples/diffsinger/run_predict.sh b/paddlemix/examples/diffsinger/run_predict.sh new file mode 100644 index 000000000..a6c8e68d2 --- /dev/null +++ b/paddlemix/examples/diffsinger/run_predict.sh @@ -0,0 +1,15 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +CUDA_VISIBLE_DEVICES=4 python run_predict.py --proj ./samples/00_我多想说再见啊.ds --exp openvpi/diffsinger_xxx \ No newline at end of file diff --git "a/paddlemix/examples/diffsinger/samples/00_\346\210\221\345\244\232\346\203\263\350\257\264\345\206\215\350\247\201\345\225\212.ds" "b/paddlemix/examples/diffsinger/samples/00_\346\210\221\345\244\232\346\203\263\350\257\264\345\206\215\350\247\201\345\225\212.ds" new file mode 100644 index 000000000..509f21c41 --- /dev/null +++ "b/paddlemix/examples/diffsinger/samples/00_\346\210\221\345\244\232\346\203\263\350\257\264\345\206\215\350\247\201\345\225\212.ds" @@ -0,0 +1,362 @@ +[ + { + "offset": 7.0, + "text": "AP 试 着 SP 掬 一 把 星 辰 SP 在 手 心 SP", + "ph_seq": "AP sh ir zh e SP j v y i b a x in ch en SP z ai sh ou x in SP", + "ph_dur": "0.3947 0.209 0.2554 0.1509 0.5921 0.1045 0.1045 0.3019 0.0929 0.3019 0.0929 0.2438 0.1625 0.1045 0.0929 0.4063 0.0697 0.1277 0.2206 0.1741 0.3599 0.2438 0.9985 0.0464", + "ph_num": "2 2 1 2 2 2 2 2 1 2 2 2 1 1", + "note_seq": "rest D#3 C4 rest D#4 C4 A#3 C4 C4 rest D#3 G3 G#3 rest", + "note_dur": "0.6 0.4 0.6 0.2 0.4 0.4 0.4 0.2 0.4 0.2 0.4 0.6 1.0 0.05", + "note_slur": "0 0 0 0 0 0 0 0 0 0 0 0 0 0", + "f0_seq": "160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.7 156.2 156.7 157.3 157.9 158.5 159.1 159.7 160.3 160.9 161.4 161.0 160.3 158.6 156.5 154.8 153.2 151.7 150.1 149.5 149.5 149.6 149.7 150.0 150.5 151.0 151.5 151.9 152.4 152.9 153.4 154.0 154.3 154.3 154.3 155.0 155.5 156.0 156.0 155.8 156.1 156.3 156.2 155.9 155.7 155.4 155.2 155.0 154.8 154.7 154.7 154.6 154.6 154.9 155.2 155.5 155.7 155.8 155.9 156.0 156.0 155.2 154.4 153.0 151.3 149.6 148.4 148.2 148.7 150.9 153.4 155.7 157.1 158.7 161.7 165.1 168.4 171.7 175.5 179.1 182.9 186.8 189.6 193.0 197.6 200.4 204.3 208.7 213.4 218.5 223.3 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"0.005" + } +] \ No newline at end of file diff --git "a/paddlemix/examples/diffsinger/samples/04_\344\273\231\347\221\266.ds" "b/paddlemix/examples/diffsinger/samples/04_\344\273\231\347\221\266.ds" new file mode 100644 index 000000000..c26165cf2 --- /dev/null +++ "b/paddlemix/examples/diffsinger/samples/04_\344\273\231\347\221\266.ds" @@ -0,0 +1,122 @@ +[ + { + "offset": 1.569, + "text": "SP 你 是 不 食 烟 火 的 仙 瑶 AP 舞 凤 翔 鸾 相 交 的 SP 窈 窕 SP", + "ph_seq": "SP n i sh ir b u sh ir y En h uo d e x ian y ao AP w u f eng x iang l uan x iang j iao d e SP y ao t iao SP", + "ph_dur": "0.465 0.135 0.241 0.12 0.272 0.09 0.527 0.195 0.678 0.045 0.242 0.12 0.136 0.045 0.362 0.18 0.316 0.045 1.446 0.678 0.045 0.256 0.105 0.242 0.12 0.603 0.12 0.572 0.15 0.272 0.09 0.136 0.045 0.723 0.135 0.045 0.422 0.12 0.904 0.4", + "ph_num": "2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 1 1", + "note_seq": "rest A4 G4 E4 B4 A4 D5 D5 F#4 G4 rest A4 G4 E4 B4 A4 D5 E5 rest A4 B4 rest", + "note_dur": "0.6 0.361 0.362 0.722 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xie yue chen chen cang hai wu SP", + "ph_seq": "SP x ie y ve ch en ch en c ang h ai w u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest A3 E4 E4 G4 A4 A4 B4 A4 A4 G4 A4 G4 E4 rest", + "note_dur": "0.2935 0.4110 0.4110 0.4110 0.4110 0.8219 0.2055 0.2055 0.4110 1.2329 0.1027 0.1027 0.2055 1.0274 0.1000", + "note_slur": "0 0 0 0 0 0 0 1 1 0 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 225.79383561643837, + "text": "SP jie shi xiao xiang wu xian lu SP", + "ph_seq": "SP j ie sh ir x iao x iang w u x ian l u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest D4 E4 G4 E4 D4 E4 A3 D4 E4 D4 E4 D4 B3 rest", + "note_dur": "0.2336 0.4110 0.4110 0.4110 0.4110 0.2055 0.6164 0.4110 0.2055 1.4384 0.1027 0.1027 0.2055 1.0274 0.1000", + "note_slur": "0 0 0 0 0 0 1 0 0 1 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 232.38972602739727, + "text": "SP bu zhi cheng yue ji ren gui SP", + "ph_seq": "SP b u zh ir ch eng y ve j i r en g ui SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest D4 A3 A3 C#4 D4 E4 F4 A4 C5 B4 A4 B4 A4 rest", + "note_dur": "0.2130 0.2055 0.2055 0.4110 0.2055 0.2055 0.4110 0.8219 0.2055 0.6164 1.2329 0.1027 0.1027 0.8219 0.1000", + "note_slur": "0 0 1 0 0 1 0 0 0 1 0 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 238.95308219178082, + "text": "SP luo yue yao qing man jiang shu SP", + "ph_seq": "SP l uo y ve y ao q ing m an j iang sh u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest A4 B4 B4 B4 B4 B4 C5 D5 E5 rest", + "note_dur": "0.2250 0.2055 0.2055 0.4110 0.4110 1.2329 0.4110 0.4110 0.2055 6.3699 0.1000", + "note_slur": "0 0 1 0 0 0 0 0 0 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + } +] \ No newline at end of file diff --git a/paddlemix/examples/diffsinger/samples/credits.txt b/paddlemix/examples/diffsinger/samples/credits.txt new file mode 100644 index 000000000..d1eb0ca29 --- /dev/null +++ b/paddlemix/examples/diffsinger/samples/credits.txt @@ -0,0 +1,42 @@ +## 00_我多想说再见啊 + +MIDI: 班超BanC +Pitch reference: Synthesizer V AI Mai +Tuning: 飞弦p https://space.bilibili.com/5347090; YQ之神 https://space.bilibili.com/102844209 + + +## 01_逍遥仙 + +Tuning: 赤松_Akamatsu https://space.bilibili.com/29902857; YQ之神 https://space.bilibili.com/102844209 + + +## 02_一半一半 + +Tuning: 笛鹿FlutyDeer https://space.bilibili.com/386270936 + + +## 03_撒娇八连 + +Pitch reference: 瑶摆桃桃烤地瓜 https://space.bilibili.com/23346333 + + +## 04_仙瑶 + +Tuning: 笛鹿FlutyDeer https://space.bilibili.com/386270936 + + +## 05_恋人心 + +Tuning: 笛鹿FlutyDeer https://space.bilibili.com/386270936 + + +## 06_不谓侠 + +MIDI: 帝国妖月 +Tuning: ZhiBinShyu with PitchDiffusion https://www.bilibili.com/video/BV12T411t7Qg + + +## 07_春江花月夜 + +Source: https://www.vsqx.top/project/vn8437 +MIDI: 何念生 https://www.vsqx.top/space/2960 diff --git a/paddlemix/models/__init__.py b/paddlemix/models/__init__.py index 5a3c3ce61..5fe525794 100644 --- a/paddlemix/models/__init__.py +++ b/paddlemix/models/__init__.py @@ -29,6 +29,7 @@ from .qwen_vl import * from .minicpm_v import * from .janus import * +from .diffsinger import * import pkg_resources diff --git a/paddlemix/models/diffsinger/basics/base_augmentation.py b/paddlemix/models/diffsinger/basics/base_augmentation.py new file mode 100644 index 000000000..4380d47e4 --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_augmentation.py @@ -0,0 +1,46 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from utils.hparams import hparams + + +class BaseAugmentation: + """ + Base class for data augmentation. + All methods of this class should be thread-safe. + 1. *process_item*: + Apply augmentation to one piece of data. + """ + + def __init__(self, data_dirs: list, augmentation_args: dict): + self.raw_data_dirs = data_dirs + self.augmentation_args = augmentation_args + self.timestep = hparams["hop_size"] / hparams["audio_sample_rate"] + + def process_item(self, item: dict, **kwargs) -> dict: + raise NotImplementedError() + + +def require_same_keys(func): + def run(*args, **kwargs): + item: dict = args[1] + res: dict = func(*args, **kwargs) + assert set(item.keys()) == set( + res.keys() + ), f"""Item keys mismatch after augmentation. +Before: {sorted(item.keys())} +After: {sorted(res.keys())}""" + return res + + return run diff --git a/paddlemix/models/diffsinger/basics/base_binarizer.py b/paddlemix/models/diffsinger/basics/base_binarizer.py new file mode 100644 index 000000000..03548c3e4 --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_binarizer.py @@ -0,0 +1,330 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import pathlib +import pickle +import random +import shutil +import warnings +from copy import deepcopy + +import numpy as np +import paddle +from tqdm import tqdm +from utils.hparams import hparams +from utils.indexed_datasets import IndexedDatasetBuilder +from utils.multiprocess_utils import chunked_multiprocess_run +from utils.phoneme_utils import build_phoneme_list, locate_dictionary +from utils.plot import distribution_to_figure +from utils.text_encoder import TokenTextEncoder + + +class BinarizationError(Exception): + pass + + +class BaseBinarizer: + """ + Base class for data processing. + 1. *process* and *process_data_split*: + process entire data, generate the train-test split (support parallel processing); + 2. *process_item*: + process singe piece of data; + 3. *get_pitch*: + infer the pitch using some algorithm; + 4. *get_align*: + get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263). + 5. phoneme encoder, voice encoder, etc. + + Subclasses should define: + 1. *load_metadata*: + how to read multiple datasets from files; + 2. *train_item_names*, *valid_item_names*, *test_item_names*: + how to split the dataset; + 3. load_ph_set: + the phoneme set. + """ + + def __init__(self, data_dir=None, data_attrs=None): + if data_dir is None: + data_dir = hparams["raw_data_dir"] + if not isinstance(data_dir, list): + data_dir = [data_dir] + self.raw_data_dirs = [pathlib.Path(d) for d in data_dir] + self.binary_data_dir = pathlib.Path(hparams["binary_data_dir"]) + self.data_attrs = [] if data_attrs is None else data_attrs + self.binarization_args = hparams["binarization_args"] + self.augmentation_args = hparams.get("augmentation_args", {}) + self.device = str("cuda" if paddle.device.cuda.device_count() >= 1 else "cpu").replace("cuda", "gpu") + self.spk_map = None + self.spk_ids = hparams["spk_ids"] + self.speakers = hparams["speakers"] + self.build_spk_map() + self.items = {} + self.item_names: list = None + self._train_item_names: list = None + self._valid_item_names: list = None + self.phone_encoder = TokenTextEncoder(vocab_list=build_phoneme_list()) + self.timestep = hparams["hop_size"] / hparams["audio_sample_rate"] + + def build_spk_map(self): + assert isinstance(self.speakers, list), "Speakers must be a list" + assert len(self.speakers) == len( + self.raw_data_dirs + ), "Number of raw data dirs must equal number of speaker names!" + if len(self.spk_ids) == 0: + self.spk_ids = list(range(len(self.raw_data_dirs))) + else: + assert len(self.spk_ids) == len( + self.raw_data_dirs + ), "Length of explicitly given spk_ids must equal the number of raw datasets." + assert ( + max(self.spk_ids) < hparams["num_spk"] + ), f"Index in spk_id sequence {self.spk_ids} is out of range. All values should be smaller than num_spk." + self.spk_map = {} + for spk_name, spk_id in zip(self.speakers, self.spk_ids): + if spk_name in self.spk_map and self.spk_map[spk_name] != spk_id: + raise ValueError( + f"Invalid speaker ID assignment. Name '{spk_name}' is assigned with different speaker IDs: {self.spk_map[spk_name]} and {spk_id}." + ) + self.spk_map[spk_name] = spk_id + print("| spk_map: ", self.spk_map) + + def load_meta_data(self, raw_data_dir: pathlib.Path, ds_id, spk_id): + raise NotImplementedError() + + def split_train_valid_set(self, item_names): + """ + Split the dataset into training set and validation set. + :return: train_item_names, valid_item_names + """ + prefixes = {str(pr): (1) for pr in hparams["test_prefixes"]} + valid_item_names = {} + for prefix in deepcopy(prefixes): + if prefix in item_names: + valid_item_names[prefix] = 1 + prefixes.pop(prefix) + for prefix in deepcopy(prefixes): + matched = False + for name in item_names: + if name.split(":")[-1] == prefix: + valid_item_names[name] = 1 + matched = True + if matched: + prefixes.pop(prefix) + for prefix in deepcopy(prefixes): + matched = False + for name in item_names: + if name.startswith(prefix): + valid_item_names[name] = 1 + matched = True + if matched: + prefixes.pop(prefix) + for prefix in deepcopy(prefixes): + matched = False + for name in item_names: + if name.split(":")[-1].startswith(prefix): + valid_item_names[name] = 1 + matched = True + if matched: + prefixes.pop(prefix) + if len(prefixes) != 0: + warnings.warn( + f"The following rules in test_prefixes have no matching names in the dataset: {', '.join(prefixes.keys())}", + category=UserWarning, + ) + warnings.filterwarnings("default") + valid_item_names = list(valid_item_names.keys()) + assert len(valid_item_names) > 0, "Validation set is empty!" + train_item_names = [x for x in item_names if x not in set(valid_item_names)] + assert len(train_item_names) > 0, "Training set is empty!" + return train_item_names, valid_item_names + + @property + def train_item_names(self): + return self._train_item_names + + @property + def valid_item_names(self): + return self._valid_item_names + + def meta_data_iterator(self, prefix): + if prefix == "train": + item_names = self.train_item_names + else: + item_names = self.valid_item_names + for item_name in item_names: + meta_data = self.items[item_name] + yield item_name, meta_data + + def process(self): + for ds_id, spk_id, data_dir in zip(range(len(self.raw_data_dirs)), self.spk_ids, self.raw_data_dirs): + self.load_meta_data(pathlib.Path(data_dir), ds_id=ds_id, spk_id=spk_id) + self.item_names = sorted(list(self.items.keys())) + self._train_item_names, self._valid_item_names = self.split_train_valid_set(self.item_names) + if self.binarization_args["shuffle"]: + random.shuffle(self.item_names) + self.binary_data_dir.mkdir(parents=True, exist_ok=True) + spk_map_fn = self.binary_data_dir / "spk_map.json" + with open(spk_map_fn, "w", encoding="utf-8") as f: + json.dump(self.spk_map, f) + shutil.copy(locate_dictionary(), self.binary_data_dir / "dictionary.txt") + self.check_coverage() + try: + self.process_dataset("valid") + self.process_dataset( + "train", + num_workers=int(self.binarization_args["num_workers"]), + apply_augmentation=any(args["enabled"] for args in self.augmentation_args.values()), + ) + except KeyboardInterrupt: + exit(-1) + + def check_coverage(self): + ph_required = set(build_phoneme_list()) + phoneme_map = {} + for ph in ph_required: + phoneme_map[ph] = 0 + ph_occurred = [] + for item_name in self.items: + ph_occurred += self.items[item_name]["ph_seq"] + if len(ph_occurred) == 0: + raise BinarizationError(f"Empty tokens in {item_name}.") + for ph in ph_occurred: + if ph not in ph_required: + continue + phoneme_map[ph] += 1 + ph_occurred = set(ph_occurred) + print("===== Phoneme Distribution Summary =====") + for i, key in enumerate(sorted(phoneme_map.keys())): + if i == len(ph_required) - 1: + end = "\n" + elif i % 10 == 9: + end = ",\n" + else: + end = ", " + print(f"'{key}': {phoneme_map[key]}", end=end) + x = sorted(phoneme_map.keys()) + values = [phoneme_map[k] for k in x] + plt = distribution_to_figure( + title="Phoneme Distribution Summary", + x_label="Phoneme", + y_label="Number of occurrences", + items=x, + values=values, + ) + filename = self.binary_data_dir / "phoneme_distribution.jpg" + plt.savefig(fname=filename, bbox_inches="tight", pad_inches=0.25) + print(f"| save summary to '{filename}'") + if ph_occurred != ph_required: + unrecognizable_phones = ph_occurred.difference(ph_required) + missing_phones = ph_required.difference(ph_occurred) + raise BinarizationError( + f"""transcriptions and dictionary mismatch. + (+) {sorted(unrecognizable_phones)} + (-) {sorted(missing_phones)}""" + ) + + def process_dataset(self, prefix, num_workers=0, apply_augmentation=False): + args = [] + builder = IndexedDatasetBuilder(self.binary_data_dir, prefix=prefix, allowed_attr=self.data_attrs) + total_sec = {k: (0.0) for k in self.spk_map} + total_raw_sec = {k: (0.0) for k in self.spk_map} + extra_info = {"names": {}, "spk_ids": {}, "spk_names": {}, "lengths": {}} + max_no = -1 + for item_name, meta_data in self.meta_data_iterator(prefix): + args.append([item_name, meta_data, self.binarization_args]) + aug_map = self.arrange_data_augmentation(self.meta_data_iterator(prefix)) if apply_augmentation else {} + + def postprocess(_item): + nonlocal total_sec, total_raw_sec, extra_info, max_no + if _item is None: + return + item_no = builder.add_item(_item) + max_no = max(max_no, item_no) + for k, v in _item.items(): + if isinstance(v, np.ndarray): + if k not in extra_info: + extra_info[k] = {} + extra_info[k][item_no] = tuple(v.shape)[0] + extra_info["names"][item_no] = _item["name"].split(":", 1)[-1] + extra_info["spk_ids"][item_no] = _item["spk_id"] + extra_info["spk_names"][item_no] = _item["spk_name"] + extra_info["lengths"][item_no] = _item["length"] + total_raw_sec[_item["spk_name"]] += _item["seconds"] + total_sec[_item["spk_name"]] += _item["seconds"] + for task in aug_map.get(_item["name"], []): + aug_item = task["func"](_item, **task["kwargs"]) + aug_item_no = builder.add_item(aug_item) + max_no = max(max_no, aug_item_no) + for k, v in aug_item.items(): + if isinstance(v, np.ndarray): + if k not in extra_info: + extra_info[k] = {} + extra_info[k][aug_item_no] = tuple(v.shape)[0] + extra_info["names"][aug_item_no] = aug_item["name"].split(":", 1)[-1] + extra_info["spk_ids"][aug_item_no] = aug_item["spk_id"] + extra_info["spk_names"][aug_item_no] = aug_item["spk_name"] + extra_info["lengths"][aug_item_no] = aug_item["length"] + total_sec[aug_item["spk_name"]] += aug_item["seconds"] + + try: + if num_workers > 0: + for item in tqdm( + chunked_multiprocess_run(self.process_item, args, num_workers=num_workers), + total=len(list(self.meta_data_iterator(prefix))), + ): + postprocess(item) + else: + for a in tqdm(args): + item = self.process_item(*a) + postprocess(item) + for k in extra_info: + assert set(extra_info[k]) == set(range(max_no + 1)), f"Item numbering is not consecutive." + extra_info[k] = list(map(lambda x: x[1], sorted(extra_info[k].items(), key=lambda x: x[0]))) + except KeyboardInterrupt: + builder.finalize() + raise + builder.finalize() + if prefix == "train": + extra_info.pop("names") + extra_info.pop("spk_names") + with open(self.binary_data_dir / f"{prefix}.meta", "wb") as f: + pickle.dump(extra_info, f) + if apply_augmentation: + print(f"| {prefix} total duration (before augmentation): {sum(total_raw_sec.values()):.2f}s") + print( + f"| {prefix} respective duration (before augmentation): " + + ", ".join(f"{k}={v:.2f}s" for k, v in total_raw_sec.items()) + ) + print( + f"| {prefix} total duration (after augmentation): {sum(total_sec.values()):.2f}s ({sum(total_sec.values()) / sum(total_raw_sec.values()):.2f}x)" + ) + print( + f"| {prefix} respective duration (after augmentation): " + + ", ".join(f"{k}={v:.2f}s" for k, v in total_sec.items()) + ) + else: + print(f"| {prefix} total duration: {sum(total_raw_sec.values()):.2f}s") + print(f"| {prefix} respective duration: " + ", ".join(f"{k}={v:.2f}s" for k, v in total_raw_sec.items())) + + def arrange_data_augmentation(self, data_iterator): + """ + Code for all types of data augmentation should be added here. + """ + raise NotImplementedError() + + def process_item(self, item_name, meta_data, binarization_args): + raise NotImplementedError() diff --git a/paddlemix/models/diffsinger/basics/base_dataset.py b/paddlemix/models/diffsinger/basics/base_dataset.py new file mode 100644 index 000000000..d4066f2aa --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_dataset.py @@ -0,0 +1,67 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import pickle + +import paddle +from utils.hparams import hparams +from utils.indexed_datasets import IndexedDataset + + +class BaseDataset(paddle.io.Dataset): + """ + Base class for datasets. + 1. *sizes*: + clipped length if "max_frames" is set; + 2. *num_frames*: + unclipped length. + + Subclasses should define: + 1. *collate*: + take the longest data, pad other data to the same length; + 2. *__getitem__*: + the index function. + """ + + def __init__(self, prefix, size_key="lengths", preload=False): + super().__init__() + self.prefix = prefix + self.data_dir = hparams["binary_data_dir"] + with open(os.path.join(self.data_dir, f"{self.prefix}.meta"), "rb") as f: + self.metadata = pickle.load(f) + self.sizes = self.metadata[size_key] + self._indexed_ds = IndexedDataset(self.data_dir, self.prefix) + if preload: + self.indexed_ds = [self._indexed_ds[i] for i in range(len(self._indexed_ds))] + del self._indexed_ds + else: + self.indexed_ds = self._indexed_ds + + def __getitem__(self, index): + return {"_idx": index, **self.indexed_ds[index]} + + def __len__(self): + return len(self.sizes) + + def num_frames(self, index): + return self.sizes[index] + + def size(self, index): + """Return an example's size as a float or tuple. This value is used when + filtering a dataset with ``--max-positions``.""" + return self.sizes[index] + + def collater(self, samples): + return {"size": len(samples), "indices": paddle.to_tensor(data=[s["_idx"] for s in samples], dtype="int64")} diff --git a/paddlemix/models/diffsinger/basics/base_exporter.py b/paddlemix/models/diffsinger/basics/base_exporter.py new file mode 100644 index 000000000..251899495 --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_exporter.py @@ -0,0 +1,72 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +from pathlib import Path +from typing import Union + +import paddle +from utils.hparams import hparams + + +class BaseExporter: + def __init__( + self, device: Union[str, (paddle.CPUPlace, paddle.CUDAPlace, str)] = None, cache_dir: Path = None, **kwargs + ): + self.device = ( + device + if device is not None + else str("cuda" if paddle.device.cuda.device_count() >= 1 else "cpu").replace("cuda", "gpu") + ) + self.cache_dir: Path = ( + cache_dir.resolve() if cache_dir is not None else Path(__file__).parent.parent / "deployment" / "cache" + ) + self.cache_dir.mkdir(parents=True, exist_ok=True) + + def build_spk_map(self) -> dict: + if hparams["use_spk_id"]: + with open(Path(hparams["work_dir"]) / "spk_map.json", "r", encoding="utf8") as f: + spk_map = json.load(f) + assert isinstance(spk_map, dict) and len(spk_map) > 0, "Invalid or empty speaker map!" + assert len(spk_map) == len(set(spk_map.values())), "Duplicate speaker id in speaker map!" + return spk_map + else: + return {} + + def build_model(self) -> paddle.nn.Layer: + """ + Creates an instance of nn.Module and load its state dict on the target device. + """ + raise NotImplementedError() + + def export_model(self, path: Path): + """ + Exports the model to ONNX format. + :param path: the target model path + """ + raise NotImplementedError() + + def export_attachments(self, path: Path): + """ + Exports related files and configs (e.g. the dictionary) to the target directory. + :param path: the target directory + """ + raise NotImplementedError() + + def export(self, path: Path): + """ + Exports all the artifacts to the target directory. + :param path: the target directory + """ + raise NotImplementedError() diff --git a/paddlemix/models/diffsinger/basics/base_module.py b/paddlemix/models/diffsinger/basics/base_module.py new file mode 100644 index 000000000..83fdd01d1 --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_module.py @@ -0,0 +1,34 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle + + +class CategorizedModule(paddle.nn.Layer): + @property + def category(self): + raise NotImplementedError() + + def check_category(self, category): + if category is None: + raise RuntimeError( + """Category is not specified in this checkpoint. +If this is a checkpoint in the old format, please consider migrating it to the new format via the following command: +python scripts/migrate.py ckpt """ + ) + elif category != self.category: + raise RuntimeError( + f"""Category mismatches! +This checkpoint is of the category '{category}', but a checkpoint of category '{self.category}' is required.""" + ) diff --git a/paddlemix/models/diffsinger/basics/base_pe.py b/paddlemix/models/diffsinger/basics/base_pe.py new file mode 100644 index 000000000..54b3461d5 --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_pe.py @@ -0,0 +1,18 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class BasePE: + def get_pitch(self, waveform, samplerate, length, *, hop_size, f0_min=65, f0_max=1100, speed=1, interp_uv=False): + raise NotImplementedError() diff --git a/paddlemix/models/diffsinger/basics/base_svs_infer.py b/paddlemix/models/diffsinger/basics/base_svs_infer.py new file mode 100644 index 000000000..4e30e80cb --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_svs_infer.py @@ -0,0 +1,149 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Tuple + +import numpy as np +import paddle + +from paddlemix.models.diffsinger.utils import hparams +from paddlemix.models.diffsinger.utils.infer_utils import resample_align_curve + + +class BaseSVSInfer: + """ + Base class for SVS inference models. + Subclasses should define: + 1. *build_model*: + how to build the model; + 2. *run_model*: + how to run the model (typically, generate a mel-spectrogram and + pass it to the pre-built vocoder); + 3. *preprocess_input*: + how to preprocess user input. + 4. *infer_once* + infer from raw inputs to the final outputs + """ + + def __init__(self, device=None): + if device is None: + device = "gpu" if paddle.device.cuda.device_count() >= 1 else "cpu" + self.device = device + self.timestep = hparams["hop_size"] / hparams["audio_sample_rate"] + self.spk_map = {} + self.model: paddle.nn.Layer = None + + def build_model(self, ckpt_steps=None) -> paddle.nn.Layer: + raise NotImplementedError() + + def load_speaker_mix( + self, param_src: dict, summary_dst: dict, mix_mode: str = "frame", mix_length: int = None + ) -> Tuple[paddle.Tensor, paddle.Tensor]: + """ + + :param param_src: param dict + :param summary_dst: summary dict + :param mix_mode: 'token' or 'frame' + :param mix_length: total tokens or frames to mix + :return: spk_mix_id [B=1, 1, N], spk_mix_value [B=1, T, N] + """ + assert mix_mode == "token" or mix_mode == "frame" + param_key = "spk_mix" if mix_mode == "frame" else "ph_spk_mix" + summary_solo_key = "spk" if mix_mode == "frame" else "ph_spk" + spk_mix_map = param_src.get(param_key) + dynamic = False + if spk_mix_map is None: + for name in self.spk_map.keys(): + spk_mix_map = {name: 1.0} + break + else: + for name in spk_mix_map: + assert name in self.spk_map, f"Speaker '{name}' not found." + if len(spk_mix_map) == 1: + summary_dst[summary_solo_key] = list(spk_mix_map.keys())[0] + elif any([isinstance(val, str) for val in spk_mix_map.values()]): + print_mix = "|".join(spk_mix_map.keys()) + summary_dst[param_key] = f"dynamic({print_mix})" + dynamic = True + else: + print_mix = "|".join([f"{n}:{'%.3f' % spk_mix_map[n]}" for n in spk_mix_map]) + summary_dst[param_key] = f"static({print_mix})" + spk_mix_id_list = [] + spk_mix_value_list = [] + if dynamic: + for name, values in spk_mix_map.items(): + spk_mix_id_list.append(self.spk_map[name]) + if isinstance(values, str): + if mix_mode == "token": + cur_spk_mix_value = values.split() + assert ( + len(cur_spk_mix_value) == mix_length + ), "Speaker mix checks failed. In dynamic token-level mix, number of proportion values must equal number of tokens." + cur_spk_mix_value = paddle.to_tensor(data=np.array(cur_spk_mix_value, "float32")).to( + self.device + )[None] + else: + cur_spk_mix_value = paddle.to_tensor( + data=resample_align_curve( + np.array(values.split(), "float32"), + original_timestep=float(param_src["spk_mix_timestep"]), + target_timestep=self.timestep, + align_length=mix_length, + ) + ).to(self.device)[None] + assert paddle.all( + x=cur_spk_mix_value >= 0.0 + ), f"""Speaker mix checks failed. +Proportions of speaker '{name}' on some {mix_mode}s are negative.""" + else: + assert ( + values >= 0.0 + ), f"""Speaker mix checks failed. +Proportion of speaker '{name}' is negative.""" + cur_spk_mix_value = paddle.full(shape=(1, mix_length), fill_value=values, dtype="float32") + spk_mix_value_list.append(cur_spk_mix_value) + spk_mix_id = paddle.to_tensor(data=spk_mix_id_list, dtype="int64").to(self.device)[None, None] + spk_mix_value = paddle.stack(x=spk_mix_value_list, axis=2) + spk_mix_value_sum = paddle.sum(x=spk_mix_value, axis=2, keepdim=True) + assert paddle.all( + x=spk_mix_value_sum > 0.0 + ), f"""Speaker mix checks failed. +Proportions of speaker mix on some frames sum to zero.""" + spk_mix_value /= spk_mix_value_sum + else: + for name, value in spk_mix_map.items(): + spk_mix_id_list.append(self.spk_map[name]) + assert ( + value >= 0.0 + ), f"""Speaker mix checks failed. +Proportion of speaker '{name}' is negative.""" + spk_mix_value_list.append(value) + spk_mix_id = paddle.to_tensor(data=spk_mix_id_list, dtype="int64").to(self.device)[None, None] + spk_mix_value = paddle.to_tensor(data=spk_mix_value_list, dtype="float32").to(self.device)[None, None] + spk_mix_value_sum = spk_mix_value.sum() + assert ( + spk_mix_value_sum > 0.0 + ), f"""Speaker mix checks failed. +Proportions of speaker mix sum to zero.""" + spk_mix_value /= spk_mix_value_sum + return spk_mix_id, spk_mix_value + + def preprocess_input(self, param: dict, idx=0) -> Dict[str, paddle.Tensor]: + raise NotImplementedError() + + def forward_model(self, sample: Dict[str, paddle.Tensor]): + raise NotImplementedError() + + def run_inference(self, params, **kwargs): + raise NotImplementedError() diff --git a/paddlemix/models/diffsinger/basics/base_vocoder.py b/paddlemix/models/diffsinger/basics/base_vocoder.py new file mode 100644 index 000000000..36dbf0c15 --- /dev/null +++ b/paddlemix/models/diffsinger/basics/base_vocoder.py @@ -0,0 +1,37 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class BaseVocoder: + def to_device(self, device): + """ + + :param device: torch.device or str + """ + raise NotImplementedError() + + def get_device(self): + """ + + :return: device: torch.device or str + """ + raise NotImplementedError() + + def spec2wav(self, mel, **kwargs): + """ + + :param mel: [T, 80] + :return: wav: [T'] + """ + raise NotImplementedError() diff --git a/paddlemix/models/diffsinger/inference/ds_acoustic.py b/paddlemix/models/diffsinger/inference/ds_acoustic.py new file mode 100644 index 000000000..f5f22e645 --- /dev/null +++ b/paddlemix/models/diffsinger/inference/ds_acoustic.py @@ -0,0 +1,256 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import pathlib +import sys +from collections import OrderedDict +from typing import Dict + +import numpy as np + +# import paddle_aux +import paddle +import tqdm + +from paddlemix.models.diffsinger.basics.base_svs_infer import BaseSVSInfer +from paddlemix.models.diffsinger.modules.fastspeech.param_adaptor import ( + VARIANCE_CHECKLIST, +) +from paddlemix.models.diffsinger.modules.fastspeech.tts_modules import LengthRegulator +from paddlemix.models.diffsinger.modules.toplevel import ( + DiffSingerAcoustic, + ShallowDiffusionOutput, +) +from paddlemix.models.diffsinger.modules.vocoders.registry import VOCODERS +from paddlemix.models.diffsinger.utils import load_ckpt +from paddlemix.models.diffsinger.utils.hparams import hparams +from paddlemix.models.diffsinger.utils.infer_utils import ( + cross_fade, + resample_align_curve, + save_wav, +) +from paddlemix.models.diffsinger.utils.phoneme_utils import build_phoneme_list +from paddlemix.models.diffsinger.utils.text_encoder import TokenTextEncoder + + +class DiffSingerAcousticInfer(BaseSVSInfer): + def __init__(self, device=None, load_model=True, load_vocoder=True, ckpt_steps=None): + super().__init__(device=device) + if load_model: + self.variance_checklist = [] + self.variances_to_embed = set() + if hparams.get("use_energy_embed", False): + self.variances_to_embed.add("energy") + if hparams.get("use_breathiness_embed", False): + self.variances_to_embed.add("breathiness") + if hparams.get("use_voicing_embed", False): + self.variances_to_embed.add("voicing") + if hparams.get("use_tension_embed", False): + self.variances_to_embed.add("tension") + self.ph_encoder = TokenTextEncoder(vocab_list=build_phoneme_list()) + if hparams["use_spk_id"]: + with open(pathlib.Path(hparams["work_dir"]) / "spk_map.json", "r", encoding="utf8") as f: + self.spk_map = json.load(f) + assert isinstance(self.spk_map, dict) and len(self.spk_map) > 0, "Invalid or empty speaker map!" + assert len(self.spk_map) == len(set(self.spk_map.values())), "Duplicate speaker id in speaker map!" + self.model = self.build_model(ckpt_steps=ckpt_steps) + self.lr = LengthRegulator().to(self.device) + if load_vocoder: + self.vocoder = self.build_vocoder() + + def build_model(self, ckpt_steps=None): + model = DiffSingerAcoustic(vocab_size=len(self.ph_encoder), out_dims=hparams["audio_num_mel_bins"]) + model.eval() + model = model.to(self.device) + load_ckpt( + model, hparams["work_dir"], ckpt_steps=ckpt_steps, prefix_in_ckpt="model", strict=True, device=self.device + ) + return model + + def build_vocoder(self): + if hparams["vocoder"] in VOCODERS: + vocoder = VOCODERS[hparams["vocoder"]]() + else: + vocoder = VOCODERS[hparams["vocoder"].split(".")[-1]]() + vocoder.to_device(self.device) + return vocoder + + def preprocess_input(self, param, idx=0): + """ + :param param: one segment in the .ds file + :param idx: index of the segment + :return: batch of the model inputs + """ + batch = {} + summary = OrderedDict() + txt_tokens = paddle.to_tensor(data=[self.ph_encoder.encode(param["ph_seq"])], dtype="int64").to(self.device) + batch["tokens"] = txt_tokens + ph_dur = paddle.to_tensor(data=np.array(param["ph_dur"].split(), np.float32)).to(self.device) + ph_acc = paddle.round(paddle.cumsum(x=ph_dur, axis=0) / self.timestep + 0.5).astype(dtype="int64") + durations = paddle.diff(x=ph_acc, axis=0, prepend=paddle.to_tensor(data=[0], dtype="int64").to(self.device))[ + None + ] + mel2ph = self.lr(durations, txt_tokens == 0) + batch["mel2ph"] = mel2ph + length = mel2ph.shape[1] + summary["tokens"] = txt_tokens.shape[1] + summary["frames"] = length + summary["seconds"] = "%.2f" % (length * self.timestep) + if hparams["use_spk_id"]: + spk_mix_id, spk_mix_value = self.load_speaker_mix( + param_src=param, summary_dst=summary, mix_mode="frame", mix_length=length + ) + batch["spk_mix_id"] = spk_mix_id + batch["spk_mix_value"] = spk_mix_value + batch["f0"] = paddle.to_tensor( + data=resample_align_curve( + np.array(param["f0_seq"].split(), np.float32), + original_timestep=float(param["f0_timestep"]), + target_timestep=self.timestep, + align_length=length, + ) + ).to(self.device)[None] + for v_name in VARIANCE_CHECKLIST: + if v_name in self.variances_to_embed: + batch[v_name] = paddle.to_tensor( + data=resample_align_curve( + np.array(param[v_name].split(), np.float32), + original_timestep=float(param[f"{v_name}_timestep"]), + target_timestep=self.timestep, + align_length=length, + ) + ).to(self.device)[None] + summary[v_name] = "manual" + if hparams["use_key_shift_embed"]: + shift_min, shift_max = hparams["augmentation_args"]["random_pitch_shifting"]["range"] + gender = param.get("gender") + if gender is None: + gender = 0.0 + if isinstance(gender, (int, float, bool)): + summary["gender"] = f"static({gender:.3f})" + key_shift_value = gender * shift_max if gender >= 0 else gender * abs(shift_min) + batch["key_shift"] = paddle.to_tensor(data=[key_shift_value], dtype="float32").to(self.device)[:, None] + else: + summary["gender"] = "dynamic" + gender_seq = resample_align_curve( + np.array(gender.split(), np.float32), + original_timestep=float(param["gender_timestep"]), + target_timestep=self.timestep, + align_length=length, + ) + gender_mask = gender_seq >= 0 + key_shift_seq = gender_seq * (gender_mask * shift_max + (1 - gender_mask) * abs(shift_min)) + batch["key_shift"] = paddle.clip( + x=paddle.to_tensor(data=key_shift_seq.astype(np.float32)).to(self.device)[None], + min=shift_min, + max=shift_max, + ) + if hparams["use_speed_embed"]: + if param.get("velocity") is None: + summary["velocity"] = "default" + batch["speed"] = paddle.to_tensor(data=[1.0], dtype="float32").to(self.device)[:, None] + else: + summary["velocity"] = "manual" + speed_min, speed_max = hparams["augmentation_args"]["random_time_stretching"]["range"] + speed_seq = resample_align_curve( + np.array(param["velocity"].split(), np.float32), + original_timestep=float(param["velocity_timestep"]), + target_timestep=self.timestep, + align_length=length, + ) + batch["speed"] = paddle.clip( + x=paddle.to_tensor(data=speed_seq.astype(np.float32)).to(self.device)[None], + min=speed_min, + max=speed_max, + ) + print(f"[{idx}]\t" + ", ".join(f"{k}: {v}" for k, v in summary.items())) + return batch + + @paddle.no_grad() + def forward_model(self, sample): + txt_tokens = sample["tokens"] + variances = {v_name: sample.get(v_name) for v_name in self.variances_to_embed} + if hparams["use_spk_id"]: + spk_mix_id = sample["spk_mix_id"] + spk_mix_value = sample["spk_mix_value"] + spk_mix_embed = paddle.sum( + x=self.model.fs2.spk_embed(spk_mix_id) * spk_mix_value.unsqueeze(axis=3), axis=2, keepdim=False + ) + else: + spk_mix_embed = None + mel_pred: ShallowDiffusionOutput = self.model( + txt_tokens, + mel2ph=sample["mel2ph"], + f0=sample["f0"], + **variances, + key_shift=sample.get("key_shift"), + speed=sample.get("speed"), + spk_mix_embed=spk_mix_embed, + infer=True, + ) + return mel_pred.diff_out + + @paddle.no_grad() + def run_vocoder(self, spec, **kwargs): + y = self.vocoder.spec2wav_torch(spec, **kwargs) + return y[None] + + def run_inference( + self, + params, + out_dir: pathlib.Path = None, + title: str = None, + num_runs: int = 1, + spk_mix: Dict[str, float] = None, + seed: int = -1, + save_mel: bool = False, + ): + batches = [self.preprocess_input(param, idx=i) for i, param in enumerate(params)] + out_dir.mkdir(parents=True, exist_ok=True) + suffix = ".wav" if not save_mel else ".mel.pt" + for i in range(num_runs): + if save_mel: + result = [] + else: + result = np.zeros(0) + current_length = 0 + for param, batch in tqdm.tqdm(zip(params, batches), desc="infer segments", total=len(params)): + if "seed" in param: + paddle.seed(seed=param["seed"] & 4294967295) + elif seed >= 0: + paddle.seed(seed=seed & 4294967295) + mel_pred = self.forward_model(batch) + if save_mel: + result.append({"offset": param.get("offset", 0.0), "mel": mel_pred.cpu(), "f0": batch["f0"].cpu()}) + else: + waveform_pred = self.run_vocoder(mel_pred, f0=batch["f0"])[0].cpu().numpy() + silent_length = round(param.get("offset", 0) * hparams["audio_sample_rate"]) - current_length + if silent_length >= 0: + result = np.append(result, np.zeros(silent_length)) + result = np.append(result, waveform_pred) + else: + result = cross_fade(result, waveform_pred, current_length + silent_length) + current_length = current_length + silent_length + tuple(waveform_pred.shape)[0] + if num_runs > 1: + filename = f"{title}-{str(i).zfill(3)}{suffix}" + else: + filename = title + suffix + save_path = out_dir / filename + if save_mel: + print(f"| save mel: {save_path}") + paddle.save(obj=result, path=save_path) + else: + print(f"| save audio: {save_path}") + save_wav(result, save_path, hparams["audio_sample_rate"]) diff --git a/paddlemix/models/diffsinger/modules/__init__.py b/paddlemix/models/diffsinger/modules/__init__.py new file mode 100644 index 000000000..fd05a9208 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlemix/models/diffsinger/modules/aux_decoder/__init__.py b/paddlemix/models/diffsinger/modules/aux_decoder/__init__.py new file mode 100644 index 000000000..fe5e33268 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/aux_decoder/__init__.py @@ -0,0 +1,89 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle +import paddle_aux + +from paddlemix.models.diffsinger.utils import filter_kwargs + +from .convnext import ConvNeXtDecoder + +AUX_DECODERS = {"convnext": ConvNeXtDecoder} +AUX_LOSSES = {"convnext": paddle.nn.L1Loss} + + +def build_aux_decoder(in_dims: int, out_dims: int, aux_decoder_arch: str, aux_decoder_args: dict) -> paddle.nn.Layer: + decoder_cls = AUX_DECODERS[aux_decoder_arch] + kwargs = filter_kwargs(aux_decoder_args, decoder_cls) + return AUX_DECODERS[aux_decoder_arch](in_dims, out_dims, **kwargs) + + +def build_aux_loss(aux_decoder_arch): + return AUX_LOSSES[aux_decoder_arch]() + + +class AuxDecoderAdaptor(paddle.nn.Layer): + def __init__( + self, + in_dims: int, + out_dims: int, + num_feats: int, + spec_min: list, + spec_max: list, + aux_decoder_arch: str, + aux_decoder_args: dict, + ): + super().__init__() + self.decoder = build_aux_decoder( + in_dims=in_dims, + out_dims=out_dims * num_feats, + aux_decoder_arch=aux_decoder_arch, + aux_decoder_args=aux_decoder_args, + ) + self.out_dims = out_dims + self.n_feats = num_feats + if spec_min is not None and spec_max is not None: + spec_min = paddle.to_tensor(data=spec_min, dtype="float32")[None, None, :].transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=spec_min, dtype="float32")[None, None, :].ndim, -3, -2 + ) + ) + spec_max = paddle.to_tensor(data=spec_max, dtype="float32")[None, None, :].transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=spec_max, dtype="float32")[None, None, :].ndim, -3, -2 + ) + ) + self.register_buffer(name="spec_min", tensor=spec_min, persistable=False) + self.register_buffer(name="spec_max", tensor=spec_max, persistable=False) + + def norm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2.0 + b = (self.spec_max + self.spec_min) / 2.0 + return (x - b) / k + + def denorm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2.0 + b = (self.spec_max + self.spec_min) / 2.0 + return x * k + b + + def forward(self, condition, infer=False): + x = self.decoder(condition, infer=infer) + if self.n_feats > 1: + x = x.reshape(-1, tuple(x.shape)[1], self.n_feats, self.out_dims) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + if infer: + x = self.denorm_spec(x) + return x diff --git a/paddlemix/models/diffsinger/modules/aux_decoder/convnext.py b/paddlemix/models/diffsinger/modules/aux_decoder/convnext.py new file mode 100644 index 000000000..82b5ad846 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/aux_decoder/convnext.py @@ -0,0 +1,103 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +from typing import Optional + +import paddle +import paddle_aux + + +class ConvNeXtBlock(paddle.nn.Layer): + """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal. + + Args: + dim (int): Number of input channels. + intermediate_dim (int): Dimensionality of the intermediate layer. + layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling. + Defaults to None. + """ + + def __init__( + self, dim: int, intermediate_dim: int, layer_scale_init_value: Optional[float] = None, drop_out: float = 0.0 + ): + super().__init__() + self.dwconv = paddle.nn.Conv1D(in_channels=dim, out_channels=dim, kernel_size=7, padding=3, groups=dim) + self.norm = paddle.nn.LayerNorm(normalized_shape=dim, epsilon=1e-06) + self.pwconv1 = paddle.nn.Linear(in_features=dim, out_features=intermediate_dim) + self.act = paddle.nn.GELU() + self.pwconv2 = paddle.nn.Linear(in_features=intermediate_dim, out_features=dim) + self.gamma = ( + paddle.base.framework.EagerParamBase.from_tensor( + tensor=layer_scale_init_value * paddle.ones(shape=dim), trainable=True + ) + if layer_scale_init_value > 0 + else None + ) + self.drop_path = paddle.nn.Identity() + self.dropout = paddle.nn.Dropout(p=drop_out) if drop_out > 0.0 else paddle.nn.Identity() + + def forward(self, x: paddle.Tensor) -> paddle.Tensor: + residual = x + x = self.dwconv(x) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + x = self.dropout(x) + x = residual + self.drop_path(x) + return x + + +class ConvNeXtDecoder(paddle.nn.Layer): + def __init__(self, in_dims, out_dims, /, *, num_channels=512, num_layers=6, kernel_size=7, dropout_rate=0.1): + super().__init__() + self.inconv = paddle.nn.Conv1D( + in_channels=in_dims, + out_channels=num_channels, + kernel_size=kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + self.conv = paddle.nn.LayerList( + sublayers=( + ConvNeXtBlock( + dim=num_channels, + intermediate_dim=num_channels * 4, + layer_scale_init_value=1e-06, + drop_out=dropout_rate, + ) + for _ in range(num_layers) + ) + ) + self.outconv = paddle.nn.Conv1D( + in_channels=num_channels, + out_channels=out_dims, + kernel_size=kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + + def forward(self, x, infer=False): + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + x = self.inconv(x) + for conv in self.conv: + x = conv(x) + x = self.outconv(x) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + return x diff --git a/paddlemix/models/diffsinger/modules/backbones/__init__.py b/paddlemix/models/diffsinger/modules/backbones/__init__.py new file mode 100644 index 000000000..d8f30fc82 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/backbones/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle +from modules.backbones.lynxnet import LYNXNet +from modules.backbones.wavenet import WaveNet +from utils import filter_kwargs + +BACKBONES = {"wavenet": WaveNet, "lynxnet": LYNXNet} + + +def build_backbone(out_dims: int, num_feats: int, backbone_type: str, backbone_args: dict) -> paddle.nn.Layer: + backbone = BACKBONES[backbone_type] + kwargs = filter_kwargs(backbone_args, backbone) + return BACKBONES[backbone_type](out_dims, num_feats, **kwargs) diff --git a/paddlemix/models/diffsinger/modules/backbones/lynxnet.py b/paddlemix/models/diffsinger/modules/backbones/lynxnet.py new file mode 100644 index 000000000..79abd725b --- /dev/null +++ b/paddlemix/models/diffsinger/modules/backbones/lynxnet.py @@ -0,0 +1,188 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle +import paddle_aux + +from paddlemix.models.diffsinger.modules.commons.common_layers import SinusoidalPosEmb +from paddlemix.models.diffsinger.utils.hparams import hparams + + +class SwiGLU(paddle.nn.Layer): + def __init__(self, dim=-1): + super().__init__() + self.dim = dim + + def forward(self, x): + out, gate = paddle_aux.split(x=x, num_or_sections=x.shape[self.dim] // 2, axis=self.dim) + return out * paddle.nn.functional.silu(x=gate) + + +class Transpose(paddle.nn.Layer): + def __init__(self, dims): + super().__init__() + assert len(dims) == 2, "dims must be a tuple of two dimensions" + self.dims = dims + + def forward(self, x): + # return x.transpose(*self.dims) + # return x.transpose(perm=list(self.dims)) # or tuple(self.dims) + return x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, *self.dims)) + + +class LYNXConvModule(paddle.nn.Layer): + @staticmethod + def calc_same_padding(kernel_size): + pad = kernel_size // 2 + return pad, pad - (kernel_size + 1) % 2 + + def __init__(self, dim, expansion_factor, kernel_size=31, activation="PReLU", dropout=0.0): + super().__init__() + inner_dim = dim * expansion_factor + activation_classes = { + "SiLU": paddle.nn.Silu, + "ReLU": paddle.nn.ReLU, + "PReLU": lambda: paddle.nn.PReLU(num_parameters=inner_dim), + } + activation = activation if activation is not None else "PReLU" + if activation not in activation_classes: + raise ValueError(f"{activation} is not a valid activation") + _activation = activation_classes[activation]() + padding = self.calc_same_padding(kernel_size) + if float(dropout) > 0.0: + _dropout = paddle.nn.Dropout(p=dropout) + else: + _dropout = paddle.nn.Identity() + self.net = paddle.nn.Sequential( + paddle.nn.LayerNorm(normalized_shape=dim), + Transpose((1, 2)), + paddle.nn.Conv1D(in_channels=dim, out_channels=inner_dim * 2, kernel_size=1), + SwiGLU(dim=1), + paddle.nn.Conv1D( + in_channels=inner_dim, + out_channels=inner_dim, + kernel_size=kernel_size, + padding=padding[0], + groups=inner_dim, + ), + _activation, + paddle.nn.Conv1D(in_channels=inner_dim, out_channels=dim, kernel_size=1), + Transpose((1, 2)), + _dropout, + ) + + def forward(self, x): + return self.net(x) + + +class LYNXNetResidualLayer(paddle.nn.Layer): + def __init__(self, dim_cond, dim, expansion_factor, kernel_size=31, activation="PReLU", dropout=0.0): + super().__init__() + self.diffusion_projection = paddle.nn.Conv1D(in_channels=dim, out_channels=dim, kernel_size=1) + self.conditioner_projection = paddle.nn.Conv1D(in_channels=dim_cond, out_channels=dim, kernel_size=1) + self.convmodule = LYNXConvModule( + dim=dim, expansion_factor=expansion_factor, kernel_size=kernel_size, activation=activation, dropout=dropout + ) + + def forward(self, x, conditioner, diffusion_step): + res_x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + x = x + self.diffusion_projection(diffusion_step) + self.conditioner_projection(conditioner) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + x = self.convmodule(x) + x = x + res_x + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + return x + + +class LYNXNet(paddle.nn.Layer): + def __init__( + self, + in_dims, + n_feats, + *, + num_layers=6, + num_channels=512, + expansion_factor=2, + kernel_size=31, + activation="PReLU", + dropout=0.0 + ): + """ + LYNXNet(Linear Gated Depthwise Separable Convolution Network) + TIPS:You can control the style of the generated results by modifying the 'activation', + - 'PReLU'(default) : Similar to WaveNet + - 'SiLU' : Voice will be more pronounced, not recommended for use under DDPM + - 'ReLU' : Contrary to 'SiLU', Voice will be weakened + """ + super().__init__() + self.in_dims = in_dims + self.n_feats = n_feats + self.input_projection = paddle.nn.Conv1D( + in_channels=in_dims * n_feats, out_channels=num_channels, kernel_size=1 + ) + self.diffusion_embedding = paddle.nn.Sequential( + SinusoidalPosEmb(num_channels), + paddle.nn.Linear(in_features=num_channels, out_features=num_channels * 4), + paddle.nn.GELU(), + paddle.nn.Linear(in_features=num_channels * 4, out_features=num_channels), + ) + self.residual_layers = paddle.nn.LayerList( + sublayers=[ + LYNXNetResidualLayer( + dim_cond=hparams["hidden_size"], + dim=num_channels, + expansion_factor=expansion_factor, + kernel_size=kernel_size, + activation=activation, + dropout=dropout, + ) + for i in range(num_layers) + ] + ) + self.norm = paddle.nn.LayerNorm(normalized_shape=num_channels) + self.output_projection = paddle.nn.Conv1D( + in_channels=num_channels, out_channels=in_dims * n_feats, kernel_size=1 + ) + init_Constant = paddle.nn.initializer.Constant(value=0.0) + init_Constant(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + :param spec: [B, F, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, H, T] + :return: + """ + if self.n_feats == 1: + x = spec[:, 0] + else: + x = spec.flatten(start_axis=1, stop_axis=2) + x = self.input_projection(x) + x = paddle.nn.functional.gelu(x=x) + diffusion_step = self.diffusion_embedding(diffusion_step).unsqueeze(axis=-1) + for layer in self.residual_layers: + x = layer(x, cond, diffusion_step) + x = self.norm(x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2))).transpose( + perm=paddle_aux.transpose_aux_func( + self.norm(x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2))).ndim, 1, 2 + ) + ) + x = self.output_projection(x) + if self.n_feats == 1: + x = x[:, None, :, :] + else: + x = x.reshape(-1, self.n_feats, self.in_dims, tuple(x.shape)[2]) + return x diff --git a/paddlemix/models/diffsinger/modules/backbones/wavenet.py b/paddlemix/models/diffsinger/modules/backbones/wavenet.py new file mode 100644 index 000000000..ad9c332b0 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/backbones/wavenet.py @@ -0,0 +1,120 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import sys +from math import sqrt + +import paddle +import paddle_aux + +from paddlemix.models.diffsinger.modules.commons.common_layers import SinusoidalPosEmb +from paddlemix.models.diffsinger.utils.hparams import hparams + + +class Conv1d(paddle.nn.Conv1D): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + init_KaimingNormal = paddle.nn.initializer.KaimingNormal(nonlinearity="leaky_relu") + init_KaimingNormal(self.weight) + + +class ResidualBlock(paddle.nn.Layer): + def __init__(self, encoder_hidden, residual_channels, dilation): + super().__init__() + self.residual_channels = residual_channels + self.dilated_conv = paddle.nn.Conv1D( + in_channels=residual_channels, + out_channels=2 * residual_channels, + kernel_size=3, + padding=dilation, + dilation=dilation, + ) + self.diffusion_projection = paddle.nn.Linear(in_features=residual_channels, out_features=residual_channels) + self.conditioner_projection = paddle.nn.Conv1D( + in_channels=encoder_hidden, out_channels=2 * residual_channels, kernel_size=1 + ) + self.output_projection = paddle.nn.Conv1D( + in_channels=residual_channels, out_channels=2 * residual_channels, kernel_size=1 + ) + + def forward(self, x, conditioner, diffusion_step): + diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(axis=-1) + conditioner = self.conditioner_projection(conditioner) + y = x + diffusion_step + y = self.dilated_conv(y) + conditioner + gate, filter = paddle_aux.split(x=y, num_or_sections=[self.residual_channels, self.residual_channels], axis=1) + y = paddle.nn.functional.sigmoid(x=gate) * paddle.nn.functional.tanh(x=filter) + y = self.output_projection(y) + residual, skip = paddle_aux.split( + x=y, num_or_sections=[self.residual_channels, self.residual_channels], axis=1 + ) + return (x + residual) / math.sqrt(2.0), skip + + +class WaveNet(paddle.nn.Layer): + def __init__(self, in_dims, n_feats, *, num_layers=20, num_channels=256, dilation_cycle_length=4): + super().__init__() + self.in_dims = in_dims + self.n_feats = n_feats + self.input_projection = Conv1d(in_dims * n_feats, num_channels, 1) + self.diffusion_embedding = SinusoidalPosEmb(num_channels) + self.mlp = paddle.nn.Sequential( + paddle.nn.Linear(in_features=num_channels, out_features=num_channels * 4), + paddle.nn.Mish(), + paddle.nn.Linear(in_features=num_channels * 4, out_features=num_channels), + ) + self.residual_layers = paddle.nn.LayerList( + sublayers=[ + ResidualBlock( + encoder_hidden=hparams["hidden_size"], + residual_channels=num_channels, + dilation=2 ** (i % dilation_cycle_length), + ) + for i in range(num_layers) + ] + ) + self.skip_projection = Conv1d(num_channels, num_channels, 1) + self.output_projection = Conv1d(num_channels, in_dims * n_feats, 1) + init_Constant = paddle.nn.initializer.Constant(value=0.0) + init_Constant(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + :param spec: [B, F, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, H, T] + :return: + """ + if self.n_feats == 1: + x = spec.squeeze(axis=1) + else: + x = spec.flatten(start_axis=1, stop_axis=2) + x = self.input_projection(x) + x = paddle.nn.functional.relu(x=x) + diffusion_step = self.diffusion_embedding(diffusion_step) + diffusion_step = self.mlp(diffusion_step) + skip = [] + for layer in self.residual_layers: + x, skip_connection = layer(x, cond, diffusion_step) + skip.append(skip_connection) + x = paddle.sum(x=paddle.stack(x=skip), axis=0) / sqrt(len(self.residual_layers)) + x = self.skip_projection(x) + x = paddle.nn.functional.relu(x=x) + x = self.output_projection(x) + if self.n_feats == 1: + x = x[:, None, :, :] + else: + x = x.reshape(-1, self.n_feats, self.in_dims, tuple(x.shape)[2]) + return x diff --git a/paddlemix/models/diffsinger/modules/commons/common_layers.py b/paddlemix/models/diffsinger/modules/commons/common_layers.py new file mode 100644 index 000000000..9eee8c1cc --- /dev/null +++ b/paddlemix/models/diffsinger/modules/commons/common_layers.py @@ -0,0 +1,188 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import math +import sys + +import paddle +import paddle_aux +from paddle.nn import GELU, LayerNorm +from paddle.nn import MultiHeadAttention as MultiheadAttention +from paddle.nn import ReLU +from paddle.nn import Silu as SiLU + +sys.path.append("/mnt/data2/pengfeiyue/code/Paddle_test/DiffSinger_paddle") +import utils + + +class NormalInitEmbedding(paddle.nn.Embedding): + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: (int | None) = None, *args, **kwargs): + super().__init__(num_embeddings, embedding_dim, *args, padding_idx=padding_idx, **kwargs) + init_Normal = paddle.nn.initializer.Normal(mean=0, std=self._embedding_dim**-0.5) + init_Normal(self.weight) + if padding_idx is not None: + init_Constant = paddle.nn.initializer.Constant(value=0) + init_Constant(self.weight[padding_idx]) + + +class XavierUniformInitLinear(paddle.nn.Linear): + def __init__(self, in_features: int, out_features: int, *args, bias: bool = True, **kwargs): + super().__init__(in_features, out_features, *args, bias_attr=bias, **kwargs) + init_XavierUniform = paddle.nn.initializer.XavierUniform() + init_XavierUniform(self.weight) + if bias: + init_Constant = paddle.nn.initializer.Constant(value=0.0) + init_Constant(self.bias) + + +class SinusoidalPositionalEmbedding(paddle.nn.Layer): + """This module produces sinusoidal positional embeddings of any length. + + Padding symbols are ignored. + """ + + def __init__(self, embedding_dim, padding_idx, init_size=1024): + super().__init__() + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.weights = SinusoidalPositionalEmbedding.get_embedding(init_size, embedding_dim, padding_idx) + self.register_buffer(name="_float_tensor", tensor=paddle.empty(shape=[1], dtype="float32")) + + @staticmethod + def get_embedding(num_embeddings, embedding_dim, padding_idx=None): + """Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = paddle.exp(x=paddle.arange(dtype="float32", end=half_dim) * -emb) + emb = paddle.arange(dtype="float32", end=num_embeddings).unsqueeze(axis=1) * emb.unsqueeze(axis=0) + emb = paddle.concat(x=[paddle.sin(x=emb), paddle.cos(x=emb)], axis=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + emb = paddle.concat(x=[emb, paddle.zeros(shape=[num_embeddings, 1])], axis=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + return emb + + def forward(self, x, incremental_state=None, timestep=None, positions=None): + """Input is expected to be of size [bsz x seqlen].""" + bsz, seq_len = tuple(x.shape)[:2] + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.shape[0]: + self.weights = SinusoidalPositionalEmbedding.get_embedding(max_pos, self.embedding_dim, self.padding_idx) + self.weights = self.weights.to(self._float_tensor) + if incremental_state is not None: + pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len + return self.weights[self.padding_idx + pos, :].expand(shape=[bsz, 1, -1]) + positions = utils.make_positions(x, self.padding_idx) if positions is None else positions + return self.weights.index_select(axis=0, index=positions.view(-1)).view(bsz, seq_len, -1).detach() + + @staticmethod + def max_positions(): + """Maximum number of supported positions.""" + return int(100000.0) + + +class TransformerFFNLayer(paddle.nn.Layer): + def __init__(self, hidden_size, filter_size, kernel_size=1, dropout=0.0, act="gelu"): + super().__init__() + self.kernel_size = kernel_size + self.dropout = dropout + self.act = act + self.ffn_1 = paddle.nn.Conv1D( + in_channels=hidden_size, out_channels=filter_size, kernel_size=kernel_size, padding=kernel_size // 2 + ) + if self.act == "relu": + self.act_fn = paddle.nn.ReLU() + elif self.act == "gelu": + self.act_fn = paddle.nn.GELU() + elif self.act == "swish": + self.act_fn = paddle.nn.Silu() + self.ffn_2 = XavierUniformInitLinear(filter_size, hidden_size) + + def forward(self, x): + x = self.ffn_1(x.transpose(perm=[1, 2, 0])).transpose(perm=[2, 0, 1]) + x = x * self.kernel_size**-0.5 + x = self.act_fn(x) + x = paddle.nn.functional.dropout(x=x, p=self.dropout, training=self.training) + x = self.ffn_2(x) + return x + + +class EncSALayer(paddle.nn.Layer): + def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act="gelu"): + super().__init__() + self.dropout = dropout + self.layer_norm1 = paddle.nn.LayerNorm(normalized_shape=c) + self.self_attn = MultiheadAttention( + c, + num_heads, + dropout=attention_dropout, + bias_attr=False, + ) + self.layer_norm2 = paddle.nn.LayerNorm(normalized_shape=c) + self.ffn = TransformerFFNLayer(c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, act=act) + + def forward(self, x, encoder_padding_mask=None, **kwargs): + layer_norm_training = kwargs.get("layer_norm_training", None) + if layer_norm_training is not None: + self.layer_norm1.training = layer_norm_training + self.layer_norm2.training = layer_norm_training + residual = x + x = self.layer_norm1(x) + x = self.self_attn( + query=x, + key=x, + value=x, + attn_mask=paddle.any(encoder_padding_mask, -1), # key_padding_mask=encoder_padding_mask + ) + x = paddle.nn.functional.dropout(x=x, p=self.dropout, training=self.training) + x = residual + x + x = ( + x + * (1 - encoder_padding_mask.astype(dtype="float32")).transpose( + perm=paddle_aux.transpose_aux_func((1 - encoder_padding_mask.astype(dtype="float32")).ndim, 0, 1) + )[..., None] + ) + residual = x + x = self.layer_norm2(x) + x = self.ffn(x) + x = paddle.nn.functional.dropout(x=x, p=self.dropout, training=self.training) + x = residual + x + x = ( + x + * (1 - encoder_padding_mask.astype(dtype="float32")).transpose( + perm=paddle_aux.transpose_aux_func((1 - encoder_padding_mask.astype(dtype="float32")).ndim, 0, 1) + )[..., None] + ) + return x + + +class SinusoidalPosEmb(paddle.nn.Layer): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.place + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = paddle.exp(x=paddle.arange(end=half_dim) * -emb) + emb = x[:, None] * emb[None, :] + emb = paddle.concat(x=(emb.sin(), emb.cos()), axis=-1) + return emb diff --git a/paddlemix/models/diffsinger/modules/commons/espnet_positional_embedding.py b/paddlemix/models/diffsinger/modules/commons/espnet_positional_embedding.py new file mode 100644 index 000000000..132cdc5dc --- /dev/null +++ b/paddlemix/models/diffsinger/modules/commons/espnet_positional_embedding.py @@ -0,0 +1,128 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import sys + +import paddle +import paddle_aux + + +class PositionalEncoding(paddle.nn.Layer): + """Positional encoding. + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + reverse (bool): Whether to reverse the input position. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = paddle.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(paddle.to_tensor(data=0.0).expand(shape=[1, max_len])) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.shape[1] >= x.shape[1]: + if self.pe.dtype != x.dtype or self.pe.place != x.place: + self.pe = self.pe.to(dtype=x.dtype, device=x.place) + return + if self.reverse: + position = paddle.arange(start=x.shape[1] - 1, end=-1, step=-1.0, dtype="float32").unsqueeze(axis=1) + else: + position = paddle.arange(start=0, end=x.shape[1], dtype="float32").unsqueeze(axis=1) + div_term = paddle.exp( + x=paddle.arange(start=0, end=self.d_model, step=2, dtype="float32") * -(math.log(10000.0) / self.d_model) + ) + pe = ( + paddle.stack(x=[paddle.sin(x=position * div_term), paddle.cos(x=position * div_term)], axis=2) + .view(-1, self.d_model) + .unsqueeze(axis=0) + ) + self.pe = pe.to(device=x.place, dtype=x.dtype) + + def forward(self, x: paddle.Tensor): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.shape[1]] + return self.dropout(x) + + +class ScaledPositionalEncoding(PositionalEncoding): + """Scaled positional encoding module. + See Sec. 3.2 https://arxiv.org/abs/1809.08895 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) + self.alpha = paddle.base.framework.EagerParamBase.from_tensor(tensor=paddle.to_tensor(data=1.0)) + + def reset_parameters(self): + """Reset parameters.""" + self.alpha.data = paddle.to_tensor(data=1.0) + + def forward(self, x): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x + self.alpha * self.pe[:, : x.shape[1]] + return self.dropout(x) + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward(self, x): + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.pe[:, : x.shape[1]] + return self.dropout(x) + self.dropout(pos_emb) diff --git a/paddlemix/models/diffsinger/modules/compat.py b/paddlemix/models/diffsinger/modules/compat.py new file mode 100644 index 000000000..31963f119 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/compat.py @@ -0,0 +1,35 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def get_backbone_type(root_config: dict, nested_config: dict = None): + if nested_config is None: + nested_config = root_config + return nested_config.get( + "backbone_type", root_config.get("backbone_type", root_config.get("diff_decoder_type", "wavenet")) + ) + + +def get_backbone_args(config: dict, backbone_type: str): + args = config.get("backbone_args") + if args is not None: + return args + elif backbone_type == "wavenet": + return { + "num_layers": config.get("residual_layers"), + "num_channels": config.get("residual_channels"), + "dilation_cycle_length": config.get("dilation_cycle_length"), + } + else: + return None diff --git a/paddlemix/models/diffsinger/modules/core/__init__.py b/paddlemix/models/diffsinger/modules/core/__init__.py new file mode 100644 index 000000000..87f21f3b7 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/core/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .ddpm import GaussianDiffusion, MultiVarianceDiffusion, PitchDiffusion +from .reflow import MultiVarianceRectifiedFlow, PitchRectifiedFlow, RectifiedFlow diff --git a/paddlemix/models/diffsinger/modules/core/ddpm.py b/paddlemix/models/diffsinger/modules/core/ddpm.py new file mode 100644 index 000000000..5b88b9723 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/core/ddpm.py @@ -0,0 +1,523 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import sys +from collections import deque +from functools import partial +from typing import List, Tuple + +import numpy as np + +# import paddle_aux +import paddle +from tqdm import tqdm + +sys.path.append("/mnt/data2/pengfeiyue/code/Paddle_test/DiffSinger_paddle") +from modules.backbones import build_backbone +from utils.hparams import hparams + + +def extract(a, t, x_shape): + b, *_ = tuple(t.shape) + out = a.take_along_axis(axis=-1, indices=t, broadcast=False) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: paddle.randn(shape=(1, *shape[1:])).tile( + repeat_times=[shape[0], *((1,) * (len(shape) - 1))] + ) + noise = lambda: paddle.randn(shape=shape) + return repeat_noise() if repeat else noise() + + +def linear_beta_schedule(timesteps, max_beta=0.01): + """ + linear schedule + """ + betas = np.linspace(0.0001, max_beta, timesteps) + return betas + + +def cosine_beta_schedule(timesteps, s=0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = np.linspace(0, steps, steps) + alphas_cumprod = np.cos((x / steps + s) / (1 + s) * np.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - alphas_cumprod[1:] / alphas_cumprod[:-1] + return np.clip(betas, a_min=0, a_max=0.999) + + +beta_schedule = {"cosine": cosine_beta_schedule, "linear": linear_beta_schedule} + + +class GaussianDiffusion(paddle.nn.Layer): + def __init__( + self, + out_dims, + num_feats=1, + timesteps=1000, + k_step=1000, + backbone_type=None, + backbone_args=None, + betas=None, + spec_min=None, + spec_max=None, + ): + super().__init__() + self.denoise_fn: paddle.nn.Layer = build_backbone(out_dims, num_feats, backbone_type, backbone_args) + self.out_dims = out_dims + self.num_feats = num_feats + if betas is not None: + betas = betas.detach().cpu().numpy() if isinstance(betas, paddle.Tensor) else betas + else: + betas = beta_schedule[hparams["schedule_type"]](timesteps) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1]) + self.use_shallow_diffusion = hparams.get("use_shallow_diffusion", False) + if self.use_shallow_diffusion: + assert k_step <= timesteps, "K_step should not be larger than timesteps." + self.timesteps = timesteps + self.k_step = k_step if self.use_shallow_diffusion else timesteps + self.noise_list = deque(maxlen=4) + to_torch = partial(paddle.to_tensor, dtype="float32") + self.register_buffer(name="betas", tensor=to_torch(betas)) + self.register_buffer(name="alphas_cumprod", tensor=to_torch(alphas_cumprod)) + self.register_buffer(name="alphas_cumprod_prev", tensor=to_torch(alphas_cumprod_prev)) + self.register_buffer(name="sqrt_alphas_cumprod", tensor=to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer(name="sqrt_one_minus_alphas_cumprod", tensor=to_torch(np.sqrt(1.0 - alphas_cumprod))) + self.register_buffer(name="log_one_minus_alphas_cumprod", tensor=to_torch(np.log(1.0 - alphas_cumprod))) + self.register_buffer(name="sqrt_recip_alphas_cumprod", tensor=to_torch(np.sqrt(1.0 / alphas_cumprod))) + self.register_buffer(name="sqrt_recipm1_alphas_cumprod", tensor=to_torch(np.sqrt(1.0 / alphas_cumprod - 1))) + posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod) + self.register_buffer(name="posterior_variance", tensor=to_torch(posterior_variance)) + self.register_buffer( + name="posterior_log_variance_clipped", tensor=to_torch(np.log(np.maximum(posterior_variance, 1e-20))) + ) + self.register_buffer( + name="posterior_mean_coef1", tensor=to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)) + ) + self.register_buffer( + name="posterior_mean_coef2", + tensor=to_torch((1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)), + ) + spec_min = paddle.to_tensor(data=spec_min, dtype="float32")[None, None, :out_dims].transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=spec_min, dtype="float32")[None, None, :out_dims].ndim, -3, -2 + ) + ) + spec_max = paddle.to_tensor(data=spec_max, dtype="float32")[None, None, :out_dims].transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=spec_max, dtype="float32")[None, None, :out_dims].ndim, -3, -2 + ) + ) + self.register_buffer(name="spec_min", tensor=spec_min) + self.register_buffer(name="spec_max", tensor=spec_max) + self.time_scale_factor = self.timesteps + self.t_start = 1 - self.k_step / self.timesteps + factors = paddle.to_tensor( + data=[i for i in range(1, self.timesteps + 1) if self.timesteps % i == 0], dtype="int64" + ) + self.register_buffer(name="timestep_factors", tensor=factors, persistable=False) + + def q_mean_variance(self, x_start, t): + mean = extract(self.sqrt_alphas_cumprod, t, tuple(x_start.shape)) * x_start + variance = extract(1.0 - self.alphas_cumprod, t, tuple(x_start.shape)) + log_variance = extract(self.log_one_minus_alphas_cumprod, t, tuple(x_start.shape)) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, tuple(x_t.shape)) * x_t + - extract(self.sqrt_recipm1_alphas_cumprod, t, tuple(x_t.shape)) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract(self.posterior_mean_coef1, t, tuple(x_t.shape)) * x_start + + extract(self.posterior_mean_coef2, t, tuple(x_t.shape)) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, tuple(x_t.shape)) + posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, tuple(x_t.shape)) + return (posterior_mean, posterior_variance, posterior_log_variance_clipped) + + def p_mean_variance(self, x, t, cond): + noise_pred = self.denoise_fn(x, t, cond=cond) + x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @paddle.no_grad() + def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): + b, *_, device = *tuple(x.shape), x.place + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond) + noise = noise_like(tuple(x.shape), device, repeat_noise) + nonzero_mask = (1 - (t == 0).astype(dtype="float32")).reshape(b, *((1,) * (len(tuple(x.shape)) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @paddle.no_grad() + def p_sample_ddim(self, x, t, interval, cond): + a_t = extract(self.alphas_cumprod, t, tuple(x.shape)) + a_prev = extract(self.alphas_cumprod, paddle_aux.max(t - interval, paddle.zeros_like(x=t)), tuple(x.shape)) + noise_pred = self.denoise_fn(x, t, cond=cond) + x_prev = a_prev.sqrt() * ( + x / a_t.sqrt() + (((1 - a_prev) / a_prev).sqrt() - ((1 - a_t) / a_t).sqrt()) * noise_pred + ) + return x_prev + + @paddle.no_grad() + def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): + """ + Use the PLMS method from + [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). + """ + + def get_x_pred(x, noise_t, t): + a_t = extract(self.alphas_cumprod, t, tuple(x.shape)) + a_prev = extract(self.alphas_cumprod, paddle_aux.max(t - interval, paddle.zeros_like(x=t)), tuple(x.shape)) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + x_delta = (a_prev - a_t) * ( + 1 / (a_t_sq * (a_t_sq + a_prev_sq)) * x + - 1 / (a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t + ) + x_pred = x + x_delta + return x_pred + + noise_list = self.noise_list + noise_pred = self.denoise_fn(x, t, cond=cond) + if len(noise_list) == 0: + x_pred = get_x_pred(x, noise_pred, t) + noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond) + noise_pred_prime = (noise_pred + noise_pred_prev) / 2 + elif len(noise_list) == 1: + noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 + elif len(noise_list) == 2: + noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 + else: + noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 + x_prev = get_x_pred(x, noise_pred_prime, t) + noise_list.append(noise_pred) + return x_prev + + def q_sample(self, x_start, t, noise): + return ( + extract(self.sqrt_alphas_cumprod, t, tuple(x_start.shape)) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, tuple(x_start.shape)) * noise + ) + + def p_losses(self, x_start, t, cond, noise=None): + if noise is None: + noise = paddle.randn(shape=x_start.shape, dtype=x_start.dtype) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + x_recon = self.denoise_fn(x_noisy, t, cond) + return x_recon, noise + + def inference(self, cond, b=1, x_start=None, device=None): + depth = hparams.get("K_step_infer", self.k_step) + speedup = hparams["diff_speedup"] + if speedup > 0: + assert depth % speedup == 0, f"Acceleration ratio must be a factor of diffusion depth {depth}." + noise = paddle.randn(shape=[b, self.num_feats, self.out_dims, tuple(cond.shape)[2]]) + if self.use_shallow_diffusion: + t_max = min(depth, self.k_step) + else: + t_max = self.k_step + if t_max >= self.timesteps: + x = noise + elif t_max > 0: + assert x_start is not None, "Missing shallow diffusion source." + x = self.q_sample(x_start, paddle.full(shape=(b,), fill_value=t_max - 1, dtype="int64"), noise) + else: + assert x_start is not None, "Missing shallow diffusion source." + x = x_start + if speedup > 1 and t_max > 0: + algorithm = hparams["diff_accelerator"] + if algorithm == "dpm-solver": + from inference.dpm_solver_pytorch import ( + DPM_Solver, + NoiseScheduleVP, + model_wrapper, + ) + + noise_schedule = NoiseScheduleVP(schedule="discrete", betas=self.betas[:t_max]) + + def my_wrapper(fn): + def wrapped(x, t, **kwargs): + ret = fn(x, t, **kwargs) + self.bar.update(1) + return ret + + return wrapped + + model_fn = model_wrapper( + my_wrapper(self.denoise_fn), noise_schedule, model_type="noise", model_kwargs={"cond": cond} + ) + dpm_solver = DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++") + steps = t_max // hparams["diff_speedup"] + self.bar = tqdm(desc="sample time step", total=steps, disable=not hparams["infer"], leave=False) + x = dpm_solver.sample(x, steps=steps, order=2, skip_type="time_uniform", method="multistep") + self.bar.close() + elif algorithm == "unipc": + from inference.uni_pc import NoiseScheduleVP, UniPC, model_wrapper + + noise_schedule = NoiseScheduleVP(schedule="discrete", betas=self.betas[:t_max]) + + def my_wrapper(fn): + def wrapped(x, t, **kwargs): + ret = fn(x, t, **kwargs) + self.bar.update(1) + return ret + + return wrapped + + model_fn = model_wrapper( + my_wrapper(self.denoise_fn), noise_schedule, model_type="noise", model_kwargs={"cond": cond} + ) + uni_pc = UniPC(model_fn, noise_schedule, variant="bh2") + steps = t_max // hparams["diff_speedup"] + self.bar = tqdm(desc="sample time step", total=steps, disable=not hparams["infer"], leave=False) + x = uni_pc.sample(x, steps=steps, order=2, skip_type="time_uniform", method="multistep") + self.bar.close() + elif algorithm == "pndm": + self.noise_list = deque(maxlen=4) + iteration_interval = speedup + for i in tqdm( + reversed(range(0, t_max, iteration_interval)), + desc="sample time step", + total=t_max // iteration_interval, + disable=not hparams["infer"], + leave=False, + ): + x = self.p_sample_plms( + x, paddle.full(shape=(b,), fill_value=i, dtype="int64"), iteration_interval, cond=cond + ) + elif algorithm == "ddim": + iteration_interval = speedup + for i in tqdm( + reversed(range(0, t_max, iteration_interval)), + desc="sample time step", + total=t_max // iteration_interval, + disable=not hparams["infer"], + leave=False, + ): + x = self.p_sample_ddim( + x, paddle.full(shape=(b,), fill_value=i, dtype="int64"), iteration_interval, cond=cond + ) + else: + raise ValueError(f"Unsupported acceleration algorithm for DDPM: {algorithm}.") + else: + for i in tqdm( + reversed(range(0, t_max)), + desc="sample time step", + total=t_max, + disable=not hparams["infer"], + leave=False, + ): + x = self.p_sample(x, paddle.full(shape=(b,), fill_value=i, dtype="int64"), cond) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 2, 3)).squeeze(axis=1) + return x + + def forward(self, condition, gt_spec=None, src_spec=None, infer=True): + """ + conditioning diffusion, use fastspeech2 encoder output as the condition + """ + cond = condition.transpose(perm=paddle_aux.transpose_aux_func(condition.ndim, 1, 2)) + b, device = tuple(condition.shape)[0], condition.place + if not infer: + spec = self.norm_spec(gt_spec).transpose( + perm=paddle_aux.transpose_aux_func(self.norm_spec(gt_spec).ndim, -2, -1) + ) + if self.num_feats == 1: + spec = spec[:, None, :, :] + t = paddle.randint(low=0, high=self.k_step, shape=(b,)).astype(dtype="int64") + x_recon, noise = self.p_losses(spec, t, cond=cond) + return x_recon, noise + else: + if src_spec is not None: + spec = self.norm_spec(src_spec).transpose( + perm=paddle_aux.transpose_aux_func(self.norm_spec(src_spec).ndim, -2, -1) + ) + if self.num_feats == 1: + spec = spec[:, None, :, :] + else: + spec = None + x = self.inference(cond, b=b, x_start=spec, device=device) + return self.denorm_spec(x) + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + +class RepetitiveDiffusion(GaussianDiffusion): + def __init__( + self, + vmin: (float | int | list), + vmax: (float | int | list), + repeat_bins: int, + timesteps=1000, + k_step=1000, + backbone_type=None, + backbone_args=None, + betas=None, + ): + assert isinstance(vmin, (float, int)) and isinstance(vmin, (float, int)) or len(vmin) == len(vmax) + num_feats = 1 if isinstance(vmin, (float, int)) else len(vmin) + spec_min = [vmin] if num_feats == 1 else [[v] for v in vmin] + spec_max = [vmax] if num_feats == 1 else [[v] for v in vmax] + self.repeat_bins = repeat_bins + super().__init__( + out_dims=repeat_bins, + num_feats=num_feats, + timesteps=timesteps, + k_step=k_step, + backbone_type=backbone_type, + backbone_args=backbone_args, + betas=betas, + spec_min=spec_min, + spec_max=spec_max, + ) + + def norm_spec(self, x): + """ + + :param x: [B, T] or [B, F, T] + :return [B, T, R] or [B, F, T, R] + """ + if self.num_feats == 1: + repeats = [1, 1, self.repeat_bins] + else: + repeats = [1, 1, 1, self.repeat_bins] + return super().norm_spec(x.unsqueeze(axis=-1).tile(repeat_times=repeats)) + + def denorm_spec(self, x): + """ + + :param x: [B, T, R] or [B, F, T, R] + :return [B, T] or [B, F, T] + """ + return super().denorm_spec(x).mean(axis=-1) + + +class PitchDiffusion(RepetitiveDiffusion): + def __init__( + self, + vmin: float, + vmax: float, + cmin: float, + cmax: float, + repeat_bins, + timesteps=1000, + k_step=1000, + backbone_type=None, + backbone_args=None, + betas=None, + ): + self.vmin = vmin + self.vmax = vmax + self.cmin = cmin + self.cmax = cmax + super().__init__( + vmin=vmin, + vmax=vmax, + repeat_bins=repeat_bins, + timesteps=timesteps, + k_step=k_step, + backbone_type=backbone_type, + backbone_args=backbone_args, + betas=betas, + ) + + def norm_spec(self, x): + return super().norm_spec(x.clip(min=self.cmin, max=self.cmax)) + + def denorm_spec(self, x): + return super().denorm_spec(x).clip(min=self.cmin, max=self.cmax) + + +class MultiVarianceDiffusion(RepetitiveDiffusion): + def __init__( + self, + ranges: List[Tuple[float, float]], + clamps: List[Tuple[float | None, float | None] | None], + repeat_bins, + timesteps=1000, + k_step=1000, + backbone_type=None, + backbone_args=None, + betas=None, + ): + assert len(ranges) == len(clamps) + self.clamps = clamps + vmin = [r[0] for r in ranges] + vmax = [r[1] for r in ranges] + if len(vmin) == 1: + vmin = vmin[0] + if len(vmax) == 1: + vmax = vmax[0] + super().__init__( + vmin=vmin, + vmax=vmax, + repeat_bins=repeat_bins, + timesteps=timesteps, + k_step=k_step, + backbone_type=backbone_type, + backbone_args=backbone_args, + betas=betas, + ) + + def clamp_spec(self, xs: (list | tuple)): + clamped = [] + for x, c in zip(xs, self.clamps): + if c is None: + clamped.append(x) + continue + clamped.append(x.clip(min=c[0], max=c[1])) + return clamped + + def norm_spec(self, xs: (list | tuple)): + """ + + :param xs: sequence of [B, T] + :return: [B, F, T] => super().norm_spec(xs) => [B, F, T, R] + """ + assert len(xs) == self.num_feats + clamped = self.clamp_spec(xs) + xs = paddle.stack(x=clamped, axis=1) + if self.num_feats == 1: + xs = xs.squeeze(axis=1) + return super().norm_spec(xs) + + def denorm_spec(self, xs): + """ + + :param xs: [B, T, R] or [B, F, T, R] => super().denorm_spec(xs) => [B, T] or [B, F, T] + :return: sequence of [B, T] + """ + xs = super().denorm_spec(xs) + if self.num_feats == 1: + xs = [xs] + else: + xs = xs.unbind(axis=1) + assert len(xs) == self.num_feats + return self.clamp_spec(xs) diff --git a/paddlemix/models/diffsinger/modules/core/reflow.py b/paddlemix/models/diffsinger/modules/core/reflow.py new file mode 100644 index 000000000..3d8a6a158 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/core/reflow.py @@ -0,0 +1,313 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import sys +from typing import List, Tuple + +import paddle +import paddle_aux +from tqdm import tqdm + +from paddlemix.models.diffsinger.modules.backbones import build_backbone +from paddlemix.models.diffsinger.utils.hparams import hparams + + +class RectifiedFlow(paddle.nn.Layer): + def __init__( + self, + out_dims, + num_feats=1, + t_start=0.0, + time_scale_factor=1000, + backbone_type=None, + backbone_args=None, + spec_min=None, + spec_max=None, + ): + super().__init__() + self.velocity_fn: paddle.nn.Layer = build_backbone(out_dims, num_feats, backbone_type, backbone_args) + self.out_dims = out_dims + self.num_feats = num_feats + self.use_shallow_diffusion = hparams.get("use_shallow_diffusion", False) + if self.use_shallow_diffusion: + assert 0.0 <= t_start <= 1.0, "T_start should be in [0, 1]." + else: + t_start = 0.0 + self.t_start = t_start + self.time_scale_factor = time_scale_factor + spec_min = paddle.to_tensor(data=spec_min, dtype="float32")[None, None, :out_dims].transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=spec_min, dtype="float32")[None, None, :out_dims].ndim, -3, -2 + ) + ) + spec_max = paddle.to_tensor(data=spec_max, dtype="float32")[None, None, :out_dims].transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=spec_max, dtype="float32")[None, None, :out_dims].ndim, -3, -2 + ) + ) + self.register_buffer(name="spec_min", tensor=spec_min, persistable=False) + self.register_buffer(name="spec_max", tensor=spec_max, persistable=False) + + def p_losses(self, x_end, t, cond): + x_start = paddle.randn(shape=x_end.shape, dtype=x_end.dtype) + x_t = x_start + t[:, None, None, None] * (x_end - x_start) + v_pred = self.velocity_fn(x_t, t * self.time_scale_factor, cond) + return v_pred, x_end - x_start + + def forward(self, condition, gt_spec=None, src_spec=None, infer=True): + cond = condition.transpose(perm=paddle_aux.transpose_aux_func(condition.ndim, 1, 2)) + b, device = tuple(condition.shape)[0], condition.place + if not infer: + spec = self.norm_spec(gt_spec).transpose( + perm=paddle_aux.transpose_aux_func(self.norm_spec(gt_spec).ndim, -2, -1) + ) + if self.num_feats == 1: + spec = spec[:, None, :, :] + t = self.t_start + (1.0 - self.t_start) * paddle.rand(shape=(b,)) + v_pred, v_gt = self.p_losses(spec, t, cond=cond) + return v_pred, v_gt, t + else: + if src_spec is not None: + spec = self.norm_spec(src_spec).transpose( + perm=paddle_aux.transpose_aux_func(self.norm_spec(src_spec).ndim, -2, -1) + ) + if self.num_feats == 1: + spec = spec[:, None, :, :] + else: + spec = None + x = self.inference(cond, b=b, x_end=spec, device=device) + return self.denorm_spec(x) + + @paddle.no_grad() + def sample_euler(self, x, t, dt, cond): + x += self.velocity_fn(x, self.time_scale_factor * t, cond) * dt + t += dt + return x, t + + @paddle.no_grad() + def sample_rk2(self, x, t, dt, cond): + k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond) + k_2 = self.velocity_fn(x + 0.5 * k_1 * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + x += k_2 * dt + t += dt + return x, t + + @paddle.no_grad() + def sample_rk4(self, x, t, dt, cond): + k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond) + k_2 = self.velocity_fn(x + 0.5 * k_1 * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + k_3 = self.velocity_fn(x + 0.5 * k_2 * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + k_4 = self.velocity_fn(x + k_3 * dt, self.time_scale_factor * (t + dt), cond) + x += (k_1 + 2 * k_2 + 2 * k_3 + k_4) * dt / 6 + t += dt + return x, t + + @paddle.no_grad() + def sample_rk5(self, x, t, dt, cond): + k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond) + k_2 = self.velocity_fn(x + 0.25 * k_1 * dt, self.time_scale_factor * (t + 0.25 * dt), cond) + k_3 = self.velocity_fn(x + 0.125 * (k_2 + k_1) * dt, self.time_scale_factor * (t + 0.25 * dt), cond) + k_4 = self.velocity_fn(x + 0.5 * (-k_2 + 2 * k_3) * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + k_5 = self.velocity_fn(x + 0.0625 * (3 * k_1 + 9 * k_4) * dt, self.time_scale_factor * (t + 0.75 * dt), cond) + k_6 = self.velocity_fn( + x + (-3 * k_1 + 2 * k_2 + 12 * k_3 - 12 * k_4 + 8 * k_5) * dt / 7, self.time_scale_factor * (t + dt), cond + ) + x += (7 * k_1 + 32 * k_3 + 12 * k_4 + 32 * k_5 + 7 * k_6) * dt / 90 + t += dt + return x, t + + @paddle.no_grad() + def inference(self, cond, b=1, x_end=None, device=None): + noise = paddle.randn(shape=[b, self.num_feats, self.out_dims, tuple(cond.shape)[2]]) + t_start = hparams.get("T_start_infer", self.t_start) + if self.use_shallow_diffusion and t_start > 0: + assert x_end is not None, "Missing shallow diffusion source." + if t_start >= 1.0: + t_start = 1.0 + x = x_end + else: + x = t_start * x_end + (1 - t_start) * noise + else: + t_start = 0.0 + x = noise + algorithm = hparams["sampling_algorithm"] + infer_step = hparams["sampling_steps"] + if t_start < 1: + dt = (1.0 - t_start) / max(1, infer_step) + algorithm_fn = { + "euler": self.sample_euler, + "rk2": self.sample_rk2, + "rk4": self.sample_rk4, + "rk5": self.sample_rk5, + }.get(algorithm) + if algorithm_fn is None: + raise ValueError(f"Unsupported algorithm for Rectified Flow: {algorithm}.") + dts = paddle.to_tensor(data=[dt]).to(x) + for i in tqdm( + range(infer_step), desc="sample time step", total=infer_step, disable=not hparams["infer"], leave=False + ): + x, _ = algorithm_fn(x, t_start + i * dts, dt, cond) + x = x.astype(dtype="float32") + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 2, 3)).squeeze(axis=1) + return x + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + +class RepetitiveRectifiedFlow(RectifiedFlow): + def __init__( + self, + vmin: (float | int | list), + vmax: (float | int | list), + repeat_bins: int, + time_scale_factor=1000, + backbone_type=None, + backbone_args=None, + ): + assert isinstance(vmin, (float, int)) and isinstance(vmin, (float, int)) or len(vmin) == len(vmax) + num_feats = 1 if isinstance(vmin, (float, int)) else len(vmin) + spec_min = [vmin] if num_feats == 1 else [[v] for v in vmin] + spec_max = [vmax] if num_feats == 1 else [[v] for v in vmax] + self.repeat_bins = repeat_bins + super().__init__( + out_dims=repeat_bins, + num_feats=num_feats, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, + backbone_args=backbone_args, + spec_min=spec_min, + spec_max=spec_max, + ) + + def norm_spec(self, x): + """ + + :param x: [B, T] or [B, F, T] + :return [B, T, R] or [B, F, T, R] + """ + if self.num_feats == 1: + repeats = [1, 1, self.repeat_bins] + else: + repeats = [1, 1, 1, self.repeat_bins] + return super().norm_spec(x.unsqueeze(axis=-1).tile(repeat_times=repeats)) + + def denorm_spec(self, x): + """ + + :param x: [B, T, R] or [B, F, T, R] + :return [B, T] or [B, F, T] + """ + return super().denorm_spec(x).mean(axis=-1) + + +class PitchRectifiedFlow(RepetitiveRectifiedFlow): + def __init__( + self, + vmin: float, + vmax: float, + cmin: float, + cmax: float, + repeat_bins, + time_scale_factor=1000, + backbone_type=None, + backbone_args=None, + ): + self.vmin = vmin + self.vmax = vmax + self.cmin = cmin + self.cmax = cmax + super().__init__( + vmin=vmin, + vmax=vmax, + repeat_bins=repeat_bins, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, + backbone_args=backbone_args, + ) + + def norm_spec(self, x): + return super().norm_spec(x.clip(min=self.cmin, max=self.cmax)) + + def denorm_spec(self, x): + return super().denorm_spec(x).clip(min=self.cmin, max=self.cmax) + + +class MultiVarianceRectifiedFlow(RepetitiveRectifiedFlow): + def __init__( + self, + ranges: List[Tuple[float, float]], + clamps: List[Tuple[float | None, float | None] | None], + repeat_bins, + time_scale_factor=1000, + backbone_type=None, + backbone_args=None, + ): + assert len(ranges) == len(clamps) + self.clamps = clamps + vmin = [r[0] for r in ranges] + vmax = [r[1] for r in ranges] + if len(vmin) == 1: + vmin = vmin[0] + if len(vmax) == 1: + vmax = vmax[0] + super().__init__( + vmin=vmin, + vmax=vmax, + repeat_bins=repeat_bins, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, + backbone_args=backbone_args, + ) + + def clamp_spec(self, xs: (list | tuple)): + clamped = [] + for x, c in zip(xs, self.clamps): + if c is None: + clamped.append(x) + continue + clamped.append(x.clip(min=c[0], max=c[1])) + return clamped + + def norm_spec(self, xs: (list | tuple)): + """ + + :param xs: sequence of [B, T] + :return: [B, F, T] => super().norm_spec(xs) => [B, F, T, R] + """ + assert len(xs) == self.num_feats + clamped = self.clamp_spec(xs) + xs = paddle.stack(x=clamped, axis=1) + if self.num_feats == 1: + xs = xs.squeeze(axis=1) + return super().norm_spec(xs) + + def denorm_spec(self, xs): + """ + + :param xs: [B, T, R] or [B, F, T, R] => super().denorm_spec(xs) => [B, T] or [B, F, T] + :return: sequence of [B, T] + """ + xs = super().denorm_spec(xs) + if self.num_feats == 1: + xs = [xs] + else: + xs = xs.unbind(axis=1) + assert len(xs) == self.num_feats + return self.clamp_spec(xs) diff --git a/paddlemix/models/diffsinger/modules/fastspeech/acoustic_encoder.py b/paddlemix/models/diffsinger/modules/fastspeech/acoustic_encoder.py new file mode 100644 index 000000000..2be4dbcf7 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/fastspeech/acoustic_encoder.py @@ -0,0 +1,110 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle + +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + NormalInitEmbedding as Embedding, +) +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + XavierUniformInitLinear as Linear, +) +from paddlemix.models.diffsinger.modules.fastspeech.tts_modules import ( + FastSpeech2Encoder, + mel2ph_to_dur, +) +from paddlemix.models.diffsinger.utils.hparams import hparams +from paddlemix.models.diffsinger.utils.text_encoder import PAD_INDEX + + +class FastSpeech2Acoustic(paddle.nn.Layer): + def __init__(self, vocab_size): + super().__init__() + self.txt_embed = Embedding(vocab_size, hparams["hidden_size"], PAD_INDEX) + self.dur_embed = Linear(1, hparams["hidden_size"]) + self.encoder = FastSpeech2Encoder( + hidden_size=hparams["hidden_size"], + num_layers=hparams["enc_layers"], + ffn_kernel_size=hparams["enc_ffn_kernel_size"], + ffn_act=hparams["ffn_act"], + dropout=hparams["dropout"], + num_heads=hparams["num_heads"], + use_pos_embed=hparams["use_pos_embed"], + rel_pos=hparams["rel_pos"], + ) + self.pitch_embed = Linear(1, hparams["hidden_size"]) + self.variance_embed_list = [] + self.use_energy_embed = hparams.get("use_energy_embed", False) + self.use_breathiness_embed = hparams.get("use_breathiness_embed", False) + self.use_voicing_embed = hparams.get("use_voicing_embed", False) + self.use_tension_embed = hparams.get("use_tension_embed", False) + if self.use_energy_embed: + self.variance_embed_list.append("energy") + if self.use_breathiness_embed: + self.variance_embed_list.append("breathiness") + if self.use_voicing_embed: + self.variance_embed_list.append("voicing") + if self.use_tension_embed: + self.variance_embed_list.append("tension") + self.use_variance_embeds = len(self.variance_embed_list) > 0 + if self.use_variance_embeds: + self.variance_embeds = paddle.nn.LayerDict( + sublayers={v_name: Linear(1, hparams["hidden_size"]) for v_name in self.variance_embed_list} + ) + self.use_key_shift_embed = hparams.get("use_key_shift_embed", False) + if self.use_key_shift_embed: + self.key_shift_embed = Linear(1, hparams["hidden_size"]) + self.use_speed_embed = hparams.get("use_speed_embed", False) + if self.use_speed_embed: + self.speed_embed = Linear(1, hparams["hidden_size"]) + self.use_spk_id = hparams["use_spk_id"] + if self.use_spk_id: + self.spk_embed = Embedding(hparams["num_spk"], hparams["hidden_size"]) + + def forward_variance_embedding(self, condition, key_shift=None, speed=None, **variances): + if self.use_variance_embeds: + variance_embeds = paddle.stack( + x=[self.variance_embeds[v_name](variances[v_name][:, :, None]) for v_name in self.variance_embed_list], + axis=-1, + ).sum(axis=-1) + condition += variance_embeds + if self.use_key_shift_embed: + key_shift_embed = self.key_shift_embed(key_shift[:, :, None]) + condition += key_shift_embed + if self.use_speed_embed: + speed_embed = self.speed_embed(speed[:, :, None]) + condition += speed_embed + return condition + + def forward(self, txt_tokens, mel2ph, f0, key_shift=None, speed=None, spk_embed_id=None, **kwargs): + txt_embed = self.txt_embed(txt_tokens) + # dur = mel2ph_to_dur(mel2ph, tuple(txt_tokens.shape)[1]).float() + dur = paddle.cast(mel2ph_to_dur(mel2ph, tuple(txt_tokens.shape)[1]), dtype="float32") + dur_embed = self.dur_embed(dur[:, :, None]) + encoder_out = self.encoder(txt_embed, dur_embed, txt_tokens == 0) + encoder_out = paddle.nn.functional.pad(x=encoder_out, pad=[0, 0, 1, 0], pad_from_left_axis=False) + mel2ph_ = mel2ph[..., None].tile(repeat_times=[1, 1, tuple(encoder_out.shape)[-1]]) + condition = paddle.take_along_axis(arr=encoder_out, axis=1, indices=mel2ph_, broadcast=False) + if self.use_spk_id: + spk_mix_embed = kwargs.get("spk_mix_embed") + if spk_mix_embed is not None: + spk_embed = spk_mix_embed + else: + spk_embed = self.spk_embed(spk_embed_id)[:, None, :] + condition += spk_embed + f0_mel = (1 + f0 / 700).log() + pitch_embed = self.pitch_embed(f0_mel[:, :, None]) + condition += pitch_embed + condition = self.forward_variance_embedding(condition, key_shift=key_shift, speed=speed, **kwargs) + return condition diff --git a/paddlemix/models/diffsinger/modules/fastspeech/param_adaptor.py b/paddlemix/models/diffsinger/modules/fastspeech/param_adaptor.py new file mode 100644 index 000000000..5f793337d --- /dev/null +++ b/paddlemix/models/diffsinger/modules/fastspeech/param_adaptor.py @@ -0,0 +1,88 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import sys + +import paddle + +import paddlemix.models.diffsinger.modules.compat as compat +from paddlemix.models.diffsinger.modules.core.ddpm import MultiVarianceDiffusion +from paddlemix.models.diffsinger.utils import filter_kwargs +from paddlemix.models.diffsinger.utils.hparams import hparams + +VARIANCE_CHECKLIST = ["energy", "breathiness", "voicing", "tension"] + + +class ParameterAdaptorModule(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.variance_prediction_list = [] + self.predict_energy = hparams.get("predict_energy", False) + self.predict_breathiness = hparams.get("predict_breathiness", False) + self.predict_voicing = hparams.get("predict_voicing", False) + self.predict_tension = hparams.get("predict_tension", False) + if self.predict_energy: + self.variance_prediction_list.append("energy") + if self.predict_breathiness: + self.variance_prediction_list.append("breathiness") + if self.predict_voicing: + self.variance_prediction_list.append("voicing") + if self.predict_tension: + self.variance_prediction_list.append("tension") + self.predict_variances = len(self.variance_prediction_list) > 0 + + def build_adaptor(self, cls=MultiVarianceDiffusion): + ranges = [] + clamps = [] + if self.predict_energy: + ranges.append((hparams["energy_db_min"], hparams["energy_db_max"])) + clamps.append((hparams["energy_db_min"], 0.0)) + if self.predict_breathiness: + ranges.append((hparams["breathiness_db_min"], hparams["breathiness_db_max"])) + clamps.append((hparams["breathiness_db_min"], 0.0)) + if self.predict_voicing: + ranges.append((hparams["voicing_db_min"], hparams["voicing_db_max"])) + clamps.append((hparams["voicing_db_min"], 0.0)) + if self.predict_tension: + ranges.append((hparams["tension_logit_min"], hparams["tension_logit_max"])) + clamps.append((hparams["tension_logit_min"], hparams["tension_logit_max"])) + variances_hparams = hparams["variances_prediction_args"] + total_repeat_bins = variances_hparams["total_repeat_bins"] + assert ( + total_repeat_bins % len(self.variance_prediction_list) == 0 + ), f"Total number of repeat bins must be divisible by number of variance parameters ({len(self.variance_prediction_list)})." + repeat_bins = total_repeat_bins // len(self.variance_prediction_list) + backbone_type = compat.get_backbone_type(hparams, nested_config=variances_hparams) + backbone_args = compat.get_backbone_args(variances_hparams, backbone_type=backbone_type) + kwargs = filter_kwargs( + { + "ranges": ranges, + "clamps": clamps, + "repeat_bins": repeat_bins, + "timesteps": hparams.get("timesteps"), + "time_scale_factor": hparams.get("time_scale_factor"), + "backbone_type": backbone_type, + "backbone_args": backbone_args, + }, + cls, + ) + return cls(**kwargs) + + def collect_variance_inputs(self, **kwargs) -> list: + return [kwargs.get(name) for name in self.variance_prediction_list] + + def collect_variance_outputs(self, variances: (list | tuple)) -> dict: + return {name: pred for name, pred in zip(self.variance_prediction_list, variances)} diff --git a/paddlemix/models/diffsinger/modules/fastspeech/tts_modules.py b/paddlemix/models/diffsinger/modules/fastspeech/tts_modules.py new file mode 100644 index 000000000..ca6ce6b17 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/fastspeech/tts_modules.py @@ -0,0 +1,473 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import sys + +import paddle +import paddle_aux + +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + EncSALayer, + SinusoidalPositionalEmbedding, +) +from paddlemix.models.diffsinger.modules.commons.espnet_positional_embedding import ( + RelPositionalEncoding, +) + +DEFAULT_MAX_SOURCE_POSITIONS = 2000 +DEFAULT_MAX_TARGET_POSITIONS = 2000 + + +class TransformerEncoderLayer(paddle.nn.Layer): + def __init__(self, hidden_size, dropout, kernel_size=None, act="gelu", num_heads=2): + super().__init__() + self.op = EncSALayer( + hidden_size, + num_heads, + dropout=dropout, + attention_dropout=0.0, + relu_dropout=dropout, + kernel_size=kernel_size, + act=act, + ) + + def forward(self, x, **kwargs): + return self.op(x, **kwargs) + + +class LayerNorm(paddle.nn.LayerNorm): + """Layer normalization module. + :param int nout: output dim size + :param int dim: dimension to be normalized + """ + + def __init__(self, nout, dim=-1): + """Construct an LayerNorm object.""" + super(LayerNorm, self).__init__(nout, eps=1e-12) + self.dim = dim + + def forward(self, x): + """Apply layer normalization. + :param torch.Tensor x: input tensor + :return: layer normalized tensor + :rtype torch.Tensor + """ + if self.dim == -1: + return super(LayerNorm, self).forward(x) + return ( + super(LayerNorm, self) + .forward(x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, -1))) + .transpose( + perm=paddle_aux.transpose_aux_func( + super(LayerNorm, self) + .forward(x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, -1))) + .ndim, + 1, + -1, + ) + ) + ) + + +class DurationPredictor(paddle.nn.Layer): + """Duration predictor module. + This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_. + The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder. + .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: + https://arxiv.org/pdf/1905.09263.pdf + Note: + The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`, + the outputs are calculated in log domain but in `inference`, those are calculated in linear domain. + """ + + def __init__( + self, in_dims, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0, dur_loss_type="mse" + ): + """Initialize duration predictor module. + Args: + in_dims (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + offset (float, optional): Offset value to avoid nan in log domain. + """ + super(DurationPredictor, self).__init__() + self.offset = offset + self.conv = paddle.nn.LayerList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = in_dims if idx == 0 else n_chans + self.conv.append( + paddle.nn.Sequential( + paddle.nn.Identity(), + paddle.nn.Conv1D( + in_channels=in_chans, + out_channels=n_chans, + kernel_size=kernel_size, + stride=1, + padding=kernel_size // 2, + ), + paddle.nn.ReLU(), + LayerNorm(n_chans, dim=1), + paddle.nn.Dropout(p=dropout_rate), + ) + ) + self.loss_type = dur_loss_type + if self.loss_type in ["mse", "huber"]: + self.out_dims = 1 + else: + raise NotImplementedError() + self.linear = paddle.nn.Linear(in_features=n_chans, out_features=self.out_dims) + + def out2dur(self, xs): + if self.loss_type in ["mse", "huber"]: + dur = xs.squeeze(axis=-1).exp() - self.offset + else: + raise NotImplementedError() + return dur + + def forward(self, xs, x_masks=None, infer=True): + """Calculate forward propagation. + Args: + xs (Tensor): Batch of input sequences (B, Tmax, idim). + x_masks (BoolTensor, optional): Batch of masks indicating padded part (B, Tmax). + infer (bool): Whether inference + Returns: + (train) FloatTensor, (infer) LongTensor: Batch of predicted durations in linear domain (B, Tmax). + """ + xs = xs.transpose(perm=paddle_aux.transpose_aux_func(xs.ndim, 1, -1)) + masks = 1 - x_masks.astype(dtype="float32") + masks_ = masks[:, None, :] + for f in self.conv: + xs = f(xs) + if x_masks is not None: + xs = xs * masks_ + xs = self.linear(xs.transpose(perm=paddle_aux.transpose_aux_func(xs.ndim, 1, -1))) + xs = xs * masks[:, :, None] + dur_pred = self.out2dur(xs) + if infer: + dur_pred = dur_pred.clip(min=0.0) + return dur_pred + + +class VariancePredictor(paddle.nn.Layer): + def __init__(self, vmin, vmax, in_dims, n_layers=5, n_chans=512, kernel_size=5, dropout_rate=0.1): + """Initialize variance predictor module. + Args: + in_dims (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + """ + super(VariancePredictor, self).__init__() + self.vmin = vmin + self.vmax = vmax + self.conv = paddle.nn.LayerList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = in_dims if idx == 0 else n_chans + self.conv.append( + paddle.nn.Sequential( + paddle.nn.Conv1D( + in_channels=in_chans, + out_channels=n_chans, + kernel_size=kernel_size, + stride=1, + padding=kernel_size // 2, + ), + paddle.nn.ReLU(), + LayerNorm(n_chans, dim=1), + paddle.nn.Dropout(p=dropout_rate), + ) + ) + self.linear = paddle.nn.Linear(in_features=n_chans, out_features=1) + self.embed_positions = SinusoidalPositionalEmbedding(in_dims, 0, init_size=4096) + self.pos_embed_alpha = paddle.base.framework.EagerParamBase.from_tensor( + tensor=paddle.to_tensor(data=[1], dtype="float32") + ) + + def out2value(self, xs): + return (xs + 1) / 2 * (self.vmax - self.vmin) + self.vmin + + def forward(self, xs, infer=True): + """ + :param xs: [B, T, H] + :param infer: whether inference + :return: [B, T] + """ + positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0]) + xs = xs + positions + xs = xs.transpose(perm=paddle_aux.transpose_aux_func(xs.ndim, 1, -1)) + for f in self.conv: + xs = f(xs) + xs = self.linear(xs.transpose(perm=paddle_aux.transpose_aux_func(xs.ndim, 1, -1))).squeeze(axis=-1) + if infer: + xs = self.out2value(xs) + return xs + + +class PitchPredictor(paddle.nn.Layer): + def __init__( + self, vmin, vmax, num_bins, deviation, in_dims, n_layers=5, n_chans=384, kernel_size=5, dropout_rate=0.1 + ): + """Initialize pitch predictor module. + Args: + in_dims (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + """ + super(PitchPredictor, self).__init__() + self.vmin = vmin + self.vmax = vmax + self.interval = (vmax - vmin) / (num_bins - 1) + self.sigma = deviation / self.interval + self.register_buffer(name="x", tensor=paddle.arange(end=num_bins).astype(dtype="float32").reshape(1, 1, -1)) + self.base_pitch_embed = paddle.nn.Linear(in_features=1, out_features=in_dims) + self.conv = paddle.nn.LayerList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = in_dims if idx == 0 else n_chans + self.conv.append( + paddle.nn.Sequential( + paddle.nn.Conv1D( + in_channels=in_chans, + out_channels=n_chans, + kernel_size=kernel_size, + stride=1, + padding=kernel_size // 2, + ), + paddle.nn.ReLU(), + LayerNorm(n_chans, dim=1), + paddle.nn.Dropout(p=dropout_rate), + ) + ) + self.linear = paddle.nn.Linear(in_features=n_chans, out_features=num_bins) + self.embed_positions = SinusoidalPositionalEmbedding(in_dims, 0, init_size=4096) + self.pos_embed_alpha = paddle.base.framework.EagerParamBase.from_tensor( + tensor=paddle.to_tensor(data=[1], dtype="float32") + ) + + def bins_to_values(self, bins): + return bins * self.interval + self.vmin + + def out2pitch(self, probs): + logits = probs.sigmoid() + bins = paddle.sum(x=self.x * logits, axis=2) / paddle.sum(x=logits, axis=2) + pitch = self.bins_to_values(bins) + return pitch + + def forward(self, xs, base): + """ + :param xs: [B, T, H] + :param base: [B, T] + :return: [B, T, N] + """ + xs = xs + self.base_pitch_embed(base[..., None]) + positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0]) + xs = xs + positions + xs = xs.transpose(perm=paddle_aux.transpose_aux_func(xs.ndim, 1, -1)) + for f in self.conv: + xs = f(xs) + xs = self.linear(xs.transpose(perm=paddle_aux.transpose_aux_func(xs.ndim, 1, -1))) + return self.out2pitch(xs) + base, xs + + +class RhythmRegulator(paddle.nn.Layer): + def __init__(self, eps=1e-05): + super().__init__() + self.eps = eps + + def forward(self, ph_dur, ph2word, word_dur): + """ + Example (no batch dim version): + 1. ph_dur = [4,2,3,2] + 2. word_dur = [3,4,2], ph2word = [1,2,2,3] + 3. word_dur_in = [4,5,2] + 4. alpha_w = [0.75,0.8,1], alpha_ph = [0.75,0.8,0.8,1] + 5. ph_dur_out = [3,1.6,2.4,2] + :param ph_dur: [B, T_ph] + :param ph2word: [B, T_ph] + :param word_dur: [B, T_w] + """ + ph_dur = ph_dur.astype(dtype="float32") * (ph2word > 0) + word_dur = word_dur.astype(dtype="float32") + word_dur_in = paddle.zeros( + shape=[tuple(ph_dur.shape)[0], ph2word.max() + 1], dtype=ph_dur.dtype + ).put_along_axis(axis=1, indices=ph2word, values=ph_dur, reduce="add")[:, 1:] + alpha_w = word_dur / word_dur_in.clip(min=self.eps) + alpha_ph = paddle.take_along_axis( + arr=paddle.nn.functional.pad(x=alpha_w, pad=[1, 0], pad_from_left_axis=False), + axis=1, + indices=ph2word, + broadcast=False, + ) + ph_dur_out = ph_dur * alpha_ph + return ph_dur_out.round().astype(dtype="int64") + + +class LengthRegulator(paddle.nn.Layer): + def forward(self, dur, dur_padding=None, alpha=None): + """ + Example (no batch dim version): + 1. dur = [2,2,3] + 2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4] + 3. token_mask = [[1,1,0,0,0,0,0], + [0,0,1,1,0,0,0], + [0,0,0,0,1,1,1]] + 4. token_idx * token_mask = [[1,1,0,0,0,0,0], + [0,0,2,2,0,0,0], + [0,0,0,0,3,3,3]] + 5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3] + + :param dur: Batch of durations of each frame (B, T_txt) + :param dur_padding: Batch of padding of each frame (B, T_txt) + :param alpha: duration rescale coefficient + :return: + mel2ph (B, T_speech) + """ + assert alpha is None or alpha > 0 + if alpha is not None: + dur = paddle.round(dur.astype(dtype="float32") * alpha).astype(dtype="int64") + if dur_padding is not None: + dur = dur * (1 - dur_padding.astype(dtype="int64")) + token_idx = paddle.arange(start=1, end=tuple(dur.shape)[1] + 1)[None, :, None].to(dur.place) + dur_cumsum = paddle.cumsum(x=dur, axis=1) + # dur_cumsum_prev = paddle.nn.functional.pad(x=dur_cumsum, pad=[1, -1 + # ], mode='constant', value=0, pad_from_left_axis=False) + dur_cumsum_prev = paddle.concat([paddle.zeros_like(dur_cumsum[:, :1]), dur_cumsum[:, :-1]], axis=1) + + pos_idx = paddle.arange(end=dur.sum(axis=-1).max())[None, None].to(dur.place) + token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None]) + mel2ph = (token_idx * token_mask.astype(dtype="int64")).sum(axis=1) + return mel2ph + + +class StretchRegulator(paddle.nn.Layer): + def forward(self, mel2ph, dur=None): + """ + Example (no batch dim version): + 1. dur = [2,4,3] + 2. mel2ph = [1,1,2,2,2,2,3,3,3] + 3. mel2dur = [2,2,4,4,4,4,3,3,3] + 4. bound_mask = [0,1,0,0,0,1,0,0,1] + 5. 1 - bound_mask * mel2dur = [1,-1,1,1,1,-3,1,1,-2] => pad => [0,1,-1,1,1,1,-3,1,1] + 6. stretch_denorm = [0,1,0,1,2,3,0,1,2] + + :param dur: Batch of durations of each frame (B, T_txt) + :param mel2ph: Batch of mel2ph (B, T_speech) + :return: + stretch (B, T_speech) + """ + if dur is None: + dur = mel2ph_to_dur(mel2ph, mel2ph.max()) + dur = paddle.nn.functional.pad(x=dur, pad=[1, 0], value=1, pad_from_left_axis=False) + mel2dur = paddle.take_along_axis(arr=dur, axis=1, indices=mel2ph, broadcast=False) + bound_mask = paddle.greater_than(x=mel2ph[:, 1:], y=paddle.to_tensor(mel2ph[:, :-1])) + bound_mask = paddle.nn.functional.pad( + x=bound_mask, pad=[0, 1], mode="constant", value=True, pad_from_left_axis=False + ) + stretch_delta = 1 - bound_mask * mel2dur + stretch_delta = paddle.nn.functional.pad( + x=stretch_delta, pad=[1, -1], mode="constant", value=0, pad_from_left_axis=False + ) + stretch_denorm = paddle.cumsum(x=stretch_delta, axis=1) + stretch = stretch_denorm / mel2dur + return stretch * (mel2ph > 0) + + +def mel2ph_to_dur(mel2ph, T_txt, max_dur=None): + B, _ = tuple(mel2ph.shape) + dur = paddle.zeros(shape=[B, T_txt + 1], dtype=mel2ph.dtype).put_along_axis( + axis=1, indices=mel2ph, values=paddle.ones_like(x=mel2ph), reduce="add" + ) + dur = dur[:, 1:] + if max_dur is not None: + dur = dur.clip(max=max_dur) + return dur + + +class FastSpeech2Encoder(paddle.nn.Layer): + def __init__( + self, + hidden_size, + num_layers, + ffn_kernel_size=9, + ffn_act="gelu", + dropout=None, + num_heads=2, + use_pos_embed=True, + rel_pos=True, + ): + super().__init__() + self.num_layers = num_layers + embed_dim = self.hidden_size = hidden_size + self.dropout = dropout + self.use_pos_embed = use_pos_embed + self.layers = paddle.nn.LayerList( + sublayers=[ + TransformerEncoderLayer( + self.hidden_size, self.dropout, kernel_size=ffn_kernel_size, act=ffn_act, num_heads=num_heads + ) + for _ in range(self.num_layers) + ] + ) + self.layer_norm = paddle.nn.LayerNorm(normalized_shape=embed_dim) + self.embed_scale = math.sqrt(hidden_size) + self.padding_idx = 0 + self.rel_pos = rel_pos + if self.rel_pos: + self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0) + else: + self.embed_positions = SinusoidalPositionalEmbedding( + hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS + ) + + def forward_embedding(self, main_embed, extra_embed=None, padding_mask=None): + x = self.embed_scale * main_embed + if extra_embed is not None: + x = x + extra_embed + if self.use_pos_embed: + if self.rel_pos: + x = self.embed_positions(x) + else: + positions = self.embed_positions(~padding_mask) + x = x + positions + x = paddle.nn.functional.dropout(x=x, p=self.dropout, training=self.training) + return x + + def forward(self, main_embed, extra_embed, padding_mask, attn_mask=None, return_hiddens=False): + x = self.forward_embedding(main_embed, extra_embed, padding_mask=padding_mask) + nonpadding_mask_TB = ( + 1 + - padding_mask.transpose(perm=paddle_aux.transpose_aux_func(padding_mask.ndim, 0, 1)).astype( + dtype="float32" + )[:, :, None] + ) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 0, 1)) * nonpadding_mask_TB + hiddens = [] + for layer in self.layers: + x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB + hiddens.append(x) + x = self.layer_norm(x) * nonpadding_mask_TB + if return_hiddens: + x = paddle.stack(x=hiddens, axis=0) + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 1, 2)) + else: + x = x.transpose(perm=paddle_aux.transpose_aux_func(x.ndim, 0, 1)) + return x diff --git a/paddlemix/models/diffsinger/modules/fastspeech/variance_encoder.py b/paddlemix/models/diffsinger/modules/fastspeech/variance_encoder.py new file mode 100644 index 000000000..0062c58e8 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/fastspeech/variance_encoder.py @@ -0,0 +1,151 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle +import paddle_aux + +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + NormalInitEmbedding as Embedding, +) +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + XavierUniformInitLinear as Linear, +) +from paddlemix.models.diffsinger.modules.fastspeech.tts_modules import ( + DurationPredictor, + FastSpeech2Encoder, +) +from paddlemix.models.diffsinger.utils.hparams import hparams +from paddlemix.models.diffsinger.utils.text_encoder import PAD_INDEX + + +class FastSpeech2Variance(paddle.nn.Layer): + def __init__(self, vocab_size): + super().__init__() + self.predict_dur = hparams["predict_dur"] + self.linguistic_mode = "word" if hparams["predict_dur"] else "phoneme" + self.txt_embed = Embedding(vocab_size, hparams["hidden_size"], PAD_INDEX) + if self.predict_dur: + self.onset_embed = Embedding(2, hparams["hidden_size"]) + self.word_dur_embed = Linear(1, hparams["hidden_size"]) + else: + self.ph_dur_embed = Linear(1, hparams["hidden_size"]) + self.encoder = FastSpeech2Encoder( + hidden_size=hparams["hidden_size"], + num_layers=hparams["enc_layers"], + ffn_kernel_size=hparams["enc_ffn_kernel_size"], + ffn_act=hparams["ffn_act"], + dropout=hparams["dropout"], + num_heads=hparams["num_heads"], + use_pos_embed=hparams["use_pos_embed"], + rel_pos=hparams["rel_pos"], + ) + dur_hparams = hparams["dur_prediction_args"] + if self.predict_dur: + self.midi_embed = Embedding(128, hparams["hidden_size"]) + self.dur_predictor = DurationPredictor( + in_dims=hparams["hidden_size"], + n_chans=dur_hparams["hidden_size"], + n_layers=dur_hparams["num_layers"], + dropout_rate=dur_hparams["dropout"], + kernel_size=dur_hparams["kernel_size"], + offset=dur_hparams["log_offset"], + dur_loss_type=dur_hparams["loss_type"], + ) + + def forward(self, txt_tokens, midi, ph2word, ph_dur=None, word_dur=None, spk_embed=None, infer=True): + """ + :param txt_tokens: (train, infer) [B, T_ph] + :param midi: (train, infer) [B, T_ph] + :param ph2word: (train, infer) [B, T_ph] + :param ph_dur: (train, [infer]) [B, T_ph] + :param word_dur: (infer) [B, T_w] + :param spk_embed: (train) [B, T_ph, H] + :param infer: whether inference + :return: encoder_out, ph_dur_pred + """ + txt_embed = self.txt_embed(txt_tokens) + if self.linguistic_mode == "word": + b = tuple(txt_tokens.shape)[0] + onset = paddle.diff(x=ph2word, axis=1, prepend=paddle.zeros(shape=[b, 1], dtype=ph2word.dtype)) > 0 + onset_embed = self.onset_embed(onset.astype(dtype="int64")) + if word_dur is None or not infer: + word_dur = paddle.zeros(shape=[b, ph2word.max() + 1], dtype=ph_dur.dtype).put_along_axis( + axis=1, indices=ph2word, values=ph_dur, reduce="add" + )[:, 1:] + word_dur = paddle.take_along_axis( + arr=paddle.nn.functional.pad(x=word_dur, pad=[1, 0], value=0, pad_from_left_axis=False), + axis=1, + indices=ph2word, + broadcast=False, + ) + word_dur_embed = self.word_dur_embed(word_dur.astype(dtype="float32")[:, :, None]) + encoder_out = self.encoder(txt_embed, onset_embed + word_dur_embed, txt_tokens == 0) + else: + ph_dur_embed = self.ph_dur_embed(ph_dur.astype(dtype="float32")[:, :, None]) + encoder_out = self.encoder(txt_embed, ph_dur_embed, txt_tokens == 0) + if self.predict_dur: + midi_embed = self.midi_embed(midi) + dur_cond = encoder_out + midi_embed + if spk_embed is not None: + dur_cond += spk_embed + ph_dur_pred = self.dur_predictor(dur_cond, x_masks=txt_tokens == PAD_INDEX, infer=infer) + return encoder_out, ph_dur_pred + else: + return encoder_out, None + + +class MelodyEncoder(paddle.nn.Layer): + def __init__(self, enc_hparams: dict): + super().__init__() + + def get_hparam(key): + return enc_hparams.get(key, hparams.get(key)) + + hidden_size = get_hparam("hidden_size") + self.note_midi_embed = Linear(1, hidden_size) + self.note_dur_embed = Linear(1, hidden_size) + self.use_glide_embed = hparams["use_glide_embed"] + self.glide_embed_scale = hparams["glide_embed_scale"] + if self.use_glide_embed: + self.note_glide_embed = Embedding(len(hparams["glide_types"]) + 1, hidden_size, padding_idx=0) + self.encoder = FastSpeech2Encoder( + hidden_size=hidden_size, + num_layers=get_hparam("enc_layers"), + ffn_kernel_size=get_hparam("enc_ffn_kernel_size"), + ffn_act=get_hparam("ffn_act"), + dropout=get_hparam("dropout"), + num_heads=get_hparam("num_heads"), + use_pos_embed=get_hparam("use_pos_embed"), + rel_pos=get_hparam("rel_pos"), + ) + self.out_proj = Linear(hidden_size, hparams["hidden_size"]) + + def forward(self, note_midi, note_rest, note_dur, glide=None): + """ + :param note_midi: float32 [B, T_n], -1: padding + :param note_rest: bool [B, T_n] + :param note_dur: int64 [B, T_n] + :param glide: int64 [B, T_n] + :return: [B, T_n, H] + """ + midi_embed = self.note_midi_embed(note_midi[:, :, None]) * ~note_rest[:, :, None] + dur_embed = self.note_dur_embed(note_dur.astype(dtype="float32")[:, :, None]) + ornament_embed = 0 + if self.use_glide_embed: + ornament_embed += self.note_glide_embed(glide) * self.glide_embed_scale + encoder_out = self.encoder(midi_embed, dur_embed + ornament_embed, padding_mask=note_midi < 0) + encoder_out = self.out_proj(encoder_out) + return encoder_out diff --git a/paddlemix/models/diffsinger/modules/hnsep/__init__.py b/paddlemix/models/diffsinger/modules/hnsep/__init__.py new file mode 100644 index 000000000..fd05a9208 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/hnsep/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/paddlemix/models/diffsinger/modules/hnsep/vr/__init__.py b/paddlemix/models/diffsinger/modules/hnsep/vr/__init__.py new file mode 100644 index 000000000..bd7501439 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/hnsep/vr/__init__.py @@ -0,0 +1,42 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pathlib + +import paddle +import yaml + +from .nets import CascadedNet + + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_sep_model(model_path, device="cpu"): + model_path = pathlib.Path(model_path) + config_file = model_path.with_name("config.yaml") + with open(config_file, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + model = CascadedNet(args.n_fft, args.hop_length, args.n_out, args.n_out_lstm, True, is_mono=args.is_mono) + model.to(device) + model.set_state_dict(state_dict=paddle.load(path=str(model_path))) + model.eval() + return model diff --git a/paddlemix/models/diffsinger/modules/hnsep/vr/layers.py b/paddlemix/models/diffsinger/modules/hnsep/vr/layers.py new file mode 100644 index 000000000..f6751b725 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/hnsep/vr/layers.py @@ -0,0 +1,140 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle +import paddle_aux + + +def crop_center(h1, h2): + h1_shape = tuple(h1.shape) + h2_shape = tuple(h2.shape) + if h1_shape[3] == h2_shape[3]: + return h1 + elif h1_shape[3] < h2_shape[3]: + raise ValueError("h1_shape[3] must be greater than h2_shape[3]") + s_time = (h1_shape[3] - h2_shape[3]) // 2 + e_time = s_time + h2_shape[3] + h1 = h1[:, :, :, s_time:e_time] + return h1 + + +class Conv2DBNActiv(paddle.nn.Layer): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=paddle.nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = paddle.nn.Sequential( + paddle.nn.Conv2D( + in_channels=nin, + out_channels=nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias_attr=False, + ), + paddle.nn.BatchNorm2D(num_features=nout), + activ(), + ) + + def forward(self, x): + return self.conv(x) + + +class Encoder(paddle.nn.Layer): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=paddle.nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) + + def forward(self, x): + h = self.conv1(x) + h = self.conv2(h) + return h + + +class Decoder(paddle.nn.Layer): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=paddle.nn.ReLU, dropout=False): + super(Decoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + self.dropout = paddle.nn.Dropout2D(p=0.1) if dropout else None + + def forward(self, x, skip=None): + x = paddle.nn.functional.interpolate(x=x, scale_factor=2, mode="bilinear", align_corners=True) + if skip is not None: + skip = crop_center(skip, x) + x = paddle.concat(x=[x, skip], axis=1) + h = self.conv1(x) + if self.dropout is not None: + h = self.dropout(h) + return h + + +class Mean(paddle.nn.Layer): + def __init__(self, dim, keepdims=False): + super(Mean, self).__init__() + self.dim = dim + self.keepdims = keepdims + + def forward(self, x): + return x.mean(self.dim, keepdims=self.keepdims) + + +class ASPPModule(paddle.nn.Layer): + def __init__(self, nin, nout, dilations=(4, 8, 12), activ=paddle.nn.ReLU, dropout=False): + super(ASPPModule, self).__init__() + self.conv1 = paddle.nn.Sequential(Mean(dim=-2, keepdims=True), Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)) + self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) + self.conv3 = Conv2DBNActiv(nin, nout, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = Conv2DBNActiv(nin, nout, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = Conv2DBNActiv(nin, nout, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ) + self.dropout = paddle.nn.Dropout2D(p=0.1) if dropout else None + + def forward(self, x): + _, _, h, w = tuple(x.shape) + feat1 = self.conv1(x).tile(repeat_times=[1, 1, h, 1]) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + out = paddle.concat(x=(feat1, feat2, feat3, feat4, feat5), axis=1) + out = self.bottleneck(out) + if self.dropout is not None: + out = self.dropout(out) + return out + + +class LSTMModule(paddle.nn.Layer): + def __init__(self, nin_conv, nin_lstm, nout_lstm): + super(LSTMModule, self).__init__() + self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) + self.lstm = paddle.nn.LSTM( + input_size=nin_lstm, hidden_size=nout_lstm // 2, time_major=not False, direction="bidirect" + ) + self.dense = paddle.nn.Sequential( + paddle.nn.Linear(in_features=nout_lstm, out_features=nin_lstm), + paddle.nn.BatchNorm1D(num_features=nin_lstm), + paddle.nn.ReLU(), + ) + + def forward(self, x): + N, _, nbins, nframes = tuple(x.shape) + h = self.conv(x)[:, 0] + h = h.transpose(perm=[2, 0, 1]) + h, _ = self.lstm(h) + h = self.dense(h.reshape(-1, tuple(h.shape)[-1])) + h = h.reshape(nframes, N, 1, nbins) + h = h.transpose(perm=[1, 2, 3, 0]) + return h diff --git a/paddlemix/models/diffsinger/modules/hnsep/vr/nets.py b/paddlemix/models/diffsinger/modules/hnsep/vr/nets.py new file mode 100644 index 000000000..001426fb6 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/hnsep/vr/nets.py @@ -0,0 +1,185 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle + +from . import layers + + +class BaseNet(paddle.nn.Layer): + def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))): + super(BaseNet, self).__init__() + self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1) + self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1) + self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1) + self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1) + self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1) + self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True) + self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1) + self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1) + self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1) + self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm) + self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1) + + def forward(self, x): + e1 = self.enc1(x) + e2 = self.enc2(e1) + e3 = self.enc3(e2) + e4 = self.enc4(e3) + e5 = self.enc5(e4) + h = self.aspp(e5) + h = self.dec4(h, e4) + h = self.dec3(h, e3) + h = self.dec2(h, e2) + h = paddle.concat(x=[h, self.lstm_dec2(h)], axis=1) + h = self.dec1(h, e1) + return h + + +class CascadedNet(paddle.nn.Layer): + def __init__(self, n_fft, hop_length, nout=32, nout_lstm=128, is_complex=False, is_mono=False): + super(CascadedNet, self).__init__() + self.n_fft = n_fft + self.hop_length = hop_length + self.is_complex = is_complex + self.is_mono = is_mono + self.register_buffer( + name="window", + tensor=paddle.audio.functional.get_window("hann", n_fft).astype("float32"), + persistable=False, + ) + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + self.nin_lstm = self.max_bin // 2 + self.offset = 64 + nin = 4 if is_complex else 2 + if is_mono: + nin = nin // 2 + self.stg1_low_band_net = paddle.nn.Sequential( + BaseNet(nin, nout // 2, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0) + ) + self.stg1_high_band_net = BaseNet(nin, nout // 4, self.nin_lstm // 2, nout_lstm // 2) + self.stg2_low_band_net = paddle.nn.Sequential( + BaseNet(nout // 4 + nin, nout, self.nin_lstm // 2, nout_lstm), + layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0), + ) + self.stg2_high_band_net = BaseNet(nout // 4 + nin, nout // 2, self.nin_lstm // 2, nout_lstm // 2) + self.stg3_full_band_net = BaseNet(3 * nout // 4 + nin, nout, self.nin_lstm, nout_lstm) + self.out = paddle.nn.Conv2D(in_channels=nout, out_channels=nin, kernel_size=1, bias_attr=False) + self.aux_out = paddle.nn.Conv2D(in_channels=3 * nout // 4, out_channels=nin, kernel_size=1, bias_attr=False) + + def forward(self, x): + if self.is_complex: + x = paddle.concat(x=[x.real(), x.imag()], axis=1) + x = x[:, :, : self.max_bin] + bandw = tuple(x.shape)[2] // 2 + l1_in = x[:, :, :bandw] + h1_in = x[:, :, bandw:] + l1 = self.stg1_low_band_net(l1_in) + h1 = self.stg1_high_band_net(h1_in) + aux1 = paddle.concat(x=[l1, h1], axis=2) + l2_in = paddle.concat(x=[l1_in, l1], axis=1) + h2_in = paddle.concat(x=[h1_in, h1], axis=1) + l2 = self.stg2_low_band_net(l2_in) + h2 = self.stg2_high_band_net(h2_in) + aux2 = paddle.concat(x=[l2, h2], axis=2) + f3_in = paddle.concat(x=[x, aux1, aux2], axis=1) + f3 = self.stg3_full_band_net(f3_in) + if self.is_complex: + mask = self.out(f3) + if self.is_mono: + mask = paddle.complex(real=mask[:, :1], imag=mask[:, 1:]) + else: + mask = paddle.complex(real=mask[:, :2], imag=mask[:, 2:]) + mask = self.bounded_mask(mask) + else: + mask = paddle.nn.functional.sigmoid(x=self.out(f3)) + mask = paddle.nn.functional.pad( + x=mask, pad=(0, 0, 0, self.output_bin - tuple(mask.shape)[2]), mode="replicate", pad_from_left_axis=False + ) + return mask + + def bounded_mask(self, mask, eps=1e-08): + mask_mag = paddle.abs(x=mask) + mask = paddle.nn.functional.tanh(x=mask_mag) * mask / (mask_mag + eps) + return mask + + def predict_mask(self, x): + mask = self.forward(x) + if self.offset > 0: + mask = mask[:, :, :, self.offset : -self.offset] + assert tuple(mask.shape)[3] > 0 + return mask + + def predict(self, x): + mask = self.forward(x) + pred = x * mask + if self.offset > 0: + pred = pred[:, :, :, self.offset : -self.offset] + assert tuple(pred.shape)[3] > 0 + return pred + + def audio2spec(self, x, use_pad=False): + B, C, T = tuple(x.shape) + x = x.reshape(B * C, T) + if use_pad: + n_frames = T // self.hop_length + 1 + T_pad = (32 * ((n_frames - 1) // 32 + 1) - 1) * self.hop_length - T + nl_pad = T_pad // 2 // self.hop_length + Tl_pad = nl_pad * self.hop_length + x = paddle.nn.functional.pad(x=x, pad=(Tl_pad, T_pad - Tl_pad), pad_from_left_axis=False) + spec = paddle.signal.stft( + x, + n_fft=self.n_fft, + hop_length=self.hop_length, + return_complex=True, + window=self.window, + pad_mode="constant", + ) + spec = spec.reshape(B, C, tuple(spec.shape)[-2], tuple(spec.shape)[-1]) + return spec + + def spec2audio(self, x): + B, C, N, T = tuple(x.shape) + x = x.reshape(-1, N, T) + x = paddle.signal.istft(x=x, n_fft=self.n_fft, hop_length=self.hop_length, window=self.window) + x = x.reshape(B, C, -1) + return x + + def predict_from_audio(self, x): + B, C, T = tuple(x.shape) + x = x.reshape(B * C, T) + n_frames = T // self.hop_length + 1 + T_pad = (32 * (n_frames // 32 + 1) - 1) * self.hop_length - T + nl_pad = T_pad // 2 // self.hop_length + Tl_pad = nl_pad * self.hop_length + x = paddle.nn.functional.pad(x=x, pad=(Tl_pad, T_pad - Tl_pad), pad_from_left_axis=False) + spec = paddle.signal.stft( + x, + n_fft=self.n_fft, + hop_length=self.hop_length, + return_complex=True, + window=self.window, + pad_mode="constant", + ) + spec = spec.reshape(B, C, tuple(spec.shape)[-2], tuple(spec.shape)[-1]) + mask = self.forward(spec) + spec_pred = spec * mask + spec_pred = spec_pred.reshape(B * C, tuple(spec.shape)[-2], tuple(spec.shape)[-1]) + x_pred = paddle.signal.istft(x=spec_pred, n_fft=self.n_fft, hop_length=self.hop_length, window=self.window) + x_pred = x_pred[:, Tl_pad : Tl_pad + T] + x_pred = x_pred.reshape(B, C, T) + return x_pred diff --git a/paddlemix/models/diffsinger/modules/metrics/curve.py b/paddlemix/models/diffsinger/modules/metrics/curve.py new file mode 100644 index 000000000..73a529193 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/metrics/curve.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle +import torchmetrics + + +class RawCurveAccuracy(torchmetrics.Metric): + def __init__(self, *, tolerance, **kwargs): + super().__init__(**kwargs) + self.tolerance = tolerance + self.add_state("close", default=paddle.to_tensor(data=0, dtype="int32"), dist_reduce_fx="sum") + self.add_state("total", default=paddle.to_tensor(data=0, dtype="int32"), dist_reduce_fx="sum") + + def update(self, pred: paddle.Tensor, target: paddle.Tensor, mask=None) -> None: + """ + + :param pred: predicted curve + :param target: reference curve + :param mask: valid or non-padding mask + """ + if mask is None: + assert tuple(pred.shape) == tuple( + target.shape + ), f"shapes of pred and target mismatch: {tuple(pred.shape)}, {tuple(target.shape)}" + else: + assert ( + tuple(pred.shape) == tuple(target.shape) == tuple(mask.shape) + ), f"shapes of pred, target and mask mismatch: {tuple(pred.shape)}, {tuple(target.shape)}, {tuple(mask.shape)}" + close = paddle.abs(x=pred - target) <= self.tolerance + if mask is not None: + close &= mask + self.close += close.sum() + self.total += pred.size if mask is None else mask.sum() + + def compute(self) -> paddle.Tensor: + return self.close / self.total diff --git a/paddlemix/models/diffsinger/modules/metrics/duration.py b/paddlemix/models/diffsinger/modules/metrics/duration.py new file mode 100644 index 000000000..f97284819 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/metrics/duration.py @@ -0,0 +1,110 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle +import paddle_aux +import torchmetrics +from modules.fastspeech.tts_modules import RhythmRegulator + + +def linguistic_checks(pred, target, ph2word, mask=None): + if mask is None: + assert ( + tuple(pred.shape) == tuple(target.shape) == tuple(ph2word.shape) + ), f"shapes of pred, target and ph2word mismatch: {tuple(pred.shape)}, {tuple(target.shape)}, {tuple(ph2word.shape)}" + else: + assert ( + tuple(pred.shape) == tuple(target.shape) == tuple(ph2word.shape) == tuple(mask.shape) + ), f"shapes of pred, target and mask mismatch: {tuple(pred.shape)}, {tuple(target.shape)}, {tuple(ph2word.shape)}, {tuple(mask.shape)}" + assert pred.ndim == 2, f"all inputs should be 2D, but got {tuple(pred.shape)}" + assert paddle.any(x=ph2word > 0), "empty word sequence" + assert paddle.all(x=ph2word >= 0), "unexpected negative word index" + assert ph2word.max() <= tuple(pred.shape)[1], f"word index out of range: {ph2word.max()} > {tuple(pred.shape)[1]}" + assert paddle.all(x=pred >= 0.0), f"unexpected negative ph_dur prediction" + assert paddle.all(x=target >= 0.0), f"unexpected negative ph_dur target" + + +class RhythmCorrectness(torchmetrics.Metric): + def __init__(self, *, tolerance, **kwargs): + super().__init__(**kwargs) + assert 0.0 < tolerance < 1.0, "tolerance should be within (0, 1)" + self.tolerance = tolerance + self.add_state("correct", default=paddle.to_tensor(data=0, dtype="int32"), dist_reduce_fx="sum") + self.add_state("total", default=paddle.to_tensor(data=0, dtype="int32"), dist_reduce_fx="sum") + + def update(self, pdur_pred: paddle.Tensor, pdur_target: paddle.Tensor, ph2word: paddle.Tensor, mask=None) -> None: + """ + + :param pdur_pred: predicted ph_dur + :param pdur_target: reference ph_dur + :param ph2word: word division sequence + :param mask: valid or non-padding mask + """ + linguistic_checks(pdur_pred, pdur_target, ph2word, mask=mask) + shape = tuple(pdur_pred.shape)[0], ph2word.max() + 1 + wdur_pred = paddle.zeros(shape=shape, dtype=pdur_pred.dtype).put_along_axis( + axis=1, indices=ph2word, values=pdur_pred, reduce="add" + )[:, 1:] + wdur_target = paddle.zeros(shape=shape, dtype=pdur_target.dtype).put_along_axis( + axis=1, indices=ph2word, values=pdur_target, reduce="add" + )[:, 1:] + if mask is None: + wdur_mask = paddle.ones_like(x=wdur_pred, dtype="bool") + else: + wdur_mask = ( + paddle.zeros(shape=shape, dtype=mask.dtype) + .put_along_axis(axis=1, indices=ph2word, values=mask, reduce="add")[:, 1:] + .astype(dtype="bool") + ) + correct = paddle.abs(x=wdur_pred - wdur_target) <= wdur_target * self.tolerance + correct &= wdur_mask + self.correct += correct.sum() + self.total += wdur_mask.sum() + + def compute(self) -> paddle.Tensor: + return self.correct / self.total + + +class PhonemeDurationAccuracy(torchmetrics.Metric): + def __init__(self, *, tolerance, **kwargs): + super().__init__(**kwargs) + self.tolerance = tolerance + self.rr = RhythmRegulator() + self.add_state("accurate", default=paddle.to_tensor(data=0, dtype="int32"), dist_reduce_fx="sum") + self.add_state("total", default=paddle.to_tensor(data=0, dtype="int32"), dist_reduce_fx="sum") + + def update(self, pdur_pred: paddle.Tensor, pdur_target: paddle.Tensor, ph2word: paddle.Tensor, mask=None) -> None: + """ + + :param pdur_pred: predicted ph_dur + :param pdur_target: reference ph_dur + :param ph2word: word division sequence + :param mask: valid or non-padding mask + """ + linguistic_checks(pdur_pred, pdur_target, ph2word, mask=mask) + shape = tuple(pdur_pred.shape)[0], ph2word.max() + 1 + wdur_target = paddle.zeros(shape=shape, dtype=pdur_target.dtype).put_along_axis( + axis=1, indices=ph2word, values=pdur_target, reduce="add" + )[:, 1:] + pdur_align = self.rr(pdur_pred, ph2word=ph2word, word_dur=wdur_target) + accurate = paddle.abs(x=pdur_align - pdur_target) <= pdur_target * self.tolerance + if mask is not None: + accurate &= mask + self.accurate += accurate.sum() + self.total += pdur_pred.size if mask is None else mask.sum() + + def compute(self) -> paddle.Tensor: + return self.accurate / self.total diff --git a/paddlemix/models/diffsinger/modules/nsf_hifigan/env.py b/paddlemix/models/diffsinger/modules/nsf_hifigan/env.py new file mode 100644 index 000000000..9414862e6 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/nsf_hifigan/env.py @@ -0,0 +1,46 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class AttrDict(dict): + """A dictionary with attribute-style access. It maps attribute access to + the real dictionary.""" + + def __init__(self, *args, **kwargs): + dict.__init__(self, *args, **kwargs) + + def __getstate__(self): + return self.__dict__.items() + + def __setstate__(self, items): + for key, val in items: + self.__dict__[key] = val + + def __repr__(self): + return "%s(%s)" % (self.__class__.__name__, dict.__repr__(self)) + + def __setitem__(self, key, value): + return super(AttrDict, self).__setitem__(key, value) + + def __getitem__(self, name): + return super(AttrDict, self).__getitem__(name) + + def __delitem__(self, name): + return super(AttrDict, self).__delitem__(name) + + __getattr__ = __getitem__ + __setattr__ = __setitem__ + + def copy(self): + return AttrDict(self) diff --git a/paddlemix/models/diffsinger/modules/nsf_hifigan/models.py b/paddlemix/models/diffsinger/modules/nsf_hifigan/models.py new file mode 100644 index 000000000..c37fcf546 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/nsf_hifigan/models.py @@ -0,0 +1,380 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import pathlib +import sys + +import numpy as np +import paddle +import paddle.nn.functional as F +import paddle_aux +from paddle.nn.utils import remove_weight_norm, weight_norm + +from .env import AttrDict +from .utils import get_padding, init_weights + +LRELU_SLOPE = 0.1 + + +def load_model(model_path: pathlib.Path): + config_file = model_path.with_name("config.json") + with open(config_file) as f: + data = f.read() + json_config = json.loads(data) + h = AttrDict(json_config) + generator = Generator(h) + cp_dict = paddle.load(path=str(model_path)) + generator.set_state_dict(state_dict=cp_dict["generator"]) + generator.eval() + generator.remove_weight_norm() + del cp_dict + return generator, h + + +class ResBlock1(paddle.nn.Layer): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = paddle.nn.LayerList( + sublayers=[ + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + self.convs2 = paddle.nn.LayerList( + sublayers=[ + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = paddle.nn.functional.leaky_relu(x=x, negative_slope=LRELU_SLOPE) + xt = c1(xt) + xt = paddle.nn.functional.leaky_relu(x=xt, negative_slope=LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + paddle.nn.utils.remove_weight_norm(layer=l) + for l in self.convs2: + paddle.nn.utils.remove_weight_norm(layer=l) + + +class ResBlock2(paddle.nn.Layer): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = paddle.nn.LayerList( + sublayers=[ + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=channels, + out_channels=channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = paddle.nn.functional.leaky_relu(x=x, negative_slope=LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + paddle.nn.utils.remove_weight_norm(layer=l) + + +class SineGen(paddle.nn.Layer): + """Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-waveform (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_threshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voiced_threshold=0): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + + def _f02uv(self, f0): + uv = paddle.ones_like(x=f0) + uv = uv * (f0 > self.voiced_threshold) + return uv + + def _f02sine(self, f0, upp): + """f0: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # rad = f0 / self.sampling_rate * paddle.arange(start=1, end=upp + 1) + rad = f0 / self.sampling_rate * paddle.arange(start=1, end=upp + 1, dtype="float32") + rad2 = ( + paddle.mod( + x=rad[..., -1:].astype(dtype="float32") + 0.5, + y=paddle.to_tensor(1.0, dtype=(rad[..., -1:].astype(dtype="float32") + 0.5).dtype), + ) + - 0.5 + ) + rad_acc = rad2.cumsum(axis=1).mod(y=paddle.to_tensor(1.0)).to(f0) + # rad += paddle.nn.functional.pad(x=rad_acc, pad=(0, 0, 1, -1), + # pad_from_left_axis=False) + # 等效实现 + rad_shifted = paddle.concat([paddle.zeros_like(rad_acc[:, :1]), rad_acc[:, :-1]], axis=1) + rad += rad_shifted + rad = rad.reshape(tuple(f0.shape)[0], -1, 1) + # rad = paddle.multiply(x=rad, y=paddle.to_tensor(paddle.arange(start + # =1, end=self.dim + 1).reshape(1, 1, -1))) + rad = paddle.multiply( + x=rad, + y=paddle.to_tensor( + paddle.arange(start=1, end=self.dim + 1), dtype="float32" # Explicitly set dtype to float32 + ).reshape(1, 1, -1), + ) + + rand_ini = paddle.rand(shape=[1, 1, self.dim]) + rand_ini[..., 0] = 0 + rad += rand_ini + sines = paddle.sin(x=2 * np.pi * rad) + return sines + + @paddle.no_grad() + def forward(self, f0, upp): + """sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + f0 = f0.unsqueeze(axis=-1) + sine_waves = self._f02sine(f0, upp) * self.sine_amp + uv = (f0 > self.voiced_threshold).astype(dtype="float32") + uv = F.interpolate(uv.transpose([0, 2, 1]), scale_factor=upp, mode="linear", data_format="NCW").transpose( + [0, 2, 1] + ) + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * paddle.randn(shape=sine_waves.shape, dtype=sine_waves.dtype) + sine_waves = sine_waves * uv + noise + return sine_waves + + +class SourceModuleHnNSF(paddle.nn.Layer): + """SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshold=0): + super(SourceModuleHnNSF, self).__init__() + self.sine_amp = sine_amp + self.noise_std = add_noise_std + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshold) + self.l_linear = paddle.nn.Linear(in_features=harmonic_num + 1, out_features=1) + self.l_tanh = paddle.nn.Tanh() + + def forward(self, x, upp): + sine_wavs = self.l_sin_gen(x, upp) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + return sine_merge + + +class Generator(paddle.nn.Layer): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.m_source = SourceModuleHnNSF(sampling_rate=h.sampling_rate, harmonic_num=8) + self.noise_convs = paddle.nn.LayerList() + self.conv_pre = paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D( + in_channels=h.num_mels, out_channels=h.upsample_initial_channel, kernel_size=7, stride=1, padding=3 + ) + ) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + self.ups = paddle.nn.LayerList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + c_cur = h.upsample_initial_channel // 2 ** (i + 1) + self.ups.append( + paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1DTranspose( + in_channels=h.upsample_initial_channel // 2**i, + out_channels=h.upsample_initial_channel // 2 ** (i + 1), + kernel_size=k, + stride=u, + padding=(k - u) // 2, + ) + ) + ) + if i + 1 < len(h.upsample_rates): + stride_f0 = int(np.prod(h.upsample_rates[i + 1 :])) + self.noise_convs.append( + paddle.nn.Conv1D( + in_channels=1, + out_channels=c_cur, + kernel_size=stride_f0 * 2, + stride=stride_f0, + padding=stride_f0 // 2, + ) + ) + else: + self.noise_convs.append(paddle.nn.Conv1D(in_channels=1, out_channels=c_cur, kernel_size=1)) + self.resblocks = paddle.nn.LayerList() + ch = h.upsample_initial_channel + for i in range(len(self.ups)): + ch //= 2 + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + self.conv_post = paddle.nn.utils.weight_norm( + layer=paddle.nn.Conv1D(in_channels=ch, out_channels=1, kernel_size=7, stride=1, padding=3) + ) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.upp = int(np.prod(h.upsample_rates)) + + def forward(self, x, f0): + har_source = self.m_source(f0, self.upp).transpose( + perm=paddle_aux.transpose_aux_func(self.m_source(f0, self.upp).ndim, 1, 2) + ) + # har_source = self.m_source(f0, self.upp).transpose(1, 2) + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = paddle.nn.functional.leaky_relu(x=x, negative_slope=LRELU_SLOPE) + x = self.ups[i](x) + x_source = self.noise_convs[i](har_source) + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = paddle.nn.functional.leaky_relu(x=x) + x = self.conv_post(x) + x = paddle.nn.functional.tanh(x=x) + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) diff --git a/paddlemix/models/diffsinger/modules/nsf_hifigan/nvSTFT.py b/paddlemix/models/diffsinger/modules/nsf_hifigan/nvSTFT.py new file mode 100644 index 000000000..cc718134d --- /dev/null +++ b/paddlemix/models/diffsinger/modules/nsf_hifigan/nvSTFT.py @@ -0,0 +1,104 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import paddle + +os.environ["LRU_CACHE_CAPACITY"] = "3" +import numpy as np +from librosa.filters import mel as librosa_mel_fn + + +def dynamic_range_compression(x, C=1, clip_val=1e-05): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-05): + return paddle.log(x=paddle.clip(x=x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return paddle.exp(x=x) / C + + +class STFT: + def __init__( + self, + sr=22050, + n_mels=80, + n_fft=1024, + win_size=1024, + hop_length=256, + fmin=20, + fmax=11025, + clip_val=1e-05, + device=None, + ): + self.target_sr = sr + self.n_mels = n_mels + self.n_fft = n_fft + self.win_size = win_size + self.hop_length = hop_length + self.fmin = fmin + self.fmax = fmax + self.clip_val = clip_val + if device is None: + device = str("cuda" if paddle.device.cuda.device_count() >= 1 else "cpu").replace("cuda", "gpu") + self.device = device + mel_basis = librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) + self.mel_basis = paddle.to_tensor(data=mel_basis).astype(dtype="float32").to(device) + + def get_mel(self, y, keyshift=0, speed=1, center=False): + factor = 2 ** (keyshift / 12) + n_fft_new = int(np.round(self.n_fft * factor)) + win_size_new = int(np.round(self.win_size * factor)) + hop_length_new = int(np.round(self.hop_length * speed)) + if paddle.min(x=y) < -1.0: + print("min value is ", paddle.min(x=y)) + if paddle.max(x=y) > 1.0: + print("max value is ", paddle.max(x=y)) + window = paddle.audio.functional.get_window("hann", win_size_new).astype("float32").to(self.device) + y = paddle.nn.functional.pad( + x=y.unsqueeze(axis=1), + pad=((win_size_new - hop_length_new) // 2, (win_size_new - hop_length_new + 1) // 2), + mode="reflect", + pad_from_left_axis=False, + ) + y = y.squeeze(axis=1) + spec = paddle.signal.stft( + y, + n_fft_new, + hop_length=hop_length_new, + win_length=win_size_new, + window=window, + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + ).abs() + + if keyshift != 0: + size = self.n_fft // 2 + 1 + resize = spec.shape[1] + if resize < size: + spec = paddle.nn.functional.pad(x=spec, pad=(0, 0, 0, size - resize), pad_from_left_axis=False) + spec = spec[:, :size, :] * self.win_size / win_size_new + spec = paddle.matmul(x=self.mel_basis, y=spec) + spec = dynamic_range_compression_torch(spec, clip_val=self.clip_val) + return spec diff --git a/paddlemix/models/diffsinger/modules/nsf_hifigan/utils.py b/paddlemix/models/diffsinger/modules/nsf_hifigan/utils.py new file mode 100644 index 000000000..34b299c86 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/nsf_hifigan/utils.py @@ -0,0 +1,27 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import matplotlib + +matplotlib.use("Agg") + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) diff --git a/paddlemix/models/diffsinger/modules/pe/__init__.py b/paddlemix/models/diffsinger/modules/pe/__init__.py new file mode 100644 index 000000000..0a11a65ae --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/__init__.py @@ -0,0 +1,32 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from utils import hparams + +from .pm import ParselmouthPE +from .pw import HarvestPE +from .rmvpe import RMVPE + + +def initialize_pe(): + pe = hparams["pe"] + pe_ckpt = hparams["pe_ckpt"] + if pe == "parselmouth": + return ParselmouthPE() + elif pe == "rmvpe": + return RMVPE(pe_ckpt) + elif pe == "harvest": + return HarvestPE() + else: + raise ValueError(f" [x] Unknown f0 extractor: {pe}") diff --git a/paddlemix/models/diffsinger/modules/pe/pm.py b/paddlemix/models/diffsinger/modules/pe/pm.py new file mode 100644 index 000000000..6f9c835ba --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/pm.py @@ -0,0 +1,30 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddlemix.models.diffsinger.basics.base_pe import BasePE +from paddlemix.models.diffsinger.utils.binarizer_utils import get_pitch_parselmouth + + +class ParselmouthPE(BasePE): + def get_pitch(self, waveform, samplerate, length, *, hop_size, f0_min=65, f0_max=1100, speed=1, interp_uv=False): + return get_pitch_parselmouth( + waveform, + samplerate=samplerate, + length=length, + hop_size=hop_size, + f0_min=f0_min, + f0_max=f0_max, + speed=speed, + interp_uv=interp_uv, + ) diff --git a/paddlemix/models/diffsinger/modules/pe/pw.py b/paddlemix/models/diffsinger/modules/pe/pw.py new file mode 100644 index 000000000..34b661841 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/pw.py @@ -0,0 +1,36 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pyworld as pw + +from paddlemix.models.diffsinger.basics.base_pe import BasePE +from paddlemix.models.diffsinger.utils.pitch_utils import interp_f0 + + +class HarvestPE(BasePE): + def get_pitch(self, waveform, samplerate, length, *, hop_size, f0_min=65, f0_max=1100, speed=1, interp_uv=False): + hop_size = int(np.round(hop_size * speed)) + time_step = 1000 * hop_size / samplerate + f0, _ = pw.harvest( + waveform.astype(np.float64), samplerate, f0_floor=f0_min, f0_ceil=f0_max, frame_period=time_step + ) + f0 = f0.astype(np.float32) + if f0.size < length: + f0 = np.pad(f0, (0, length - f0.size)) + f0 = f0[:length] + uv = f0 == 0 + if interp_uv: + f0, uv = interp_f0(f0, uv) + return f0, uv diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/__init__.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/__init__.py new file mode 100644 index 000000000..6590b9daf --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .constants import * +from .inference import RMVPE +from .model import E2E0 +from .spec import MelSpectrogram +from .utils import to_local_average_f0, to_viterbi_f0 diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/constants.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/constants.py new file mode 100644 index 000000000..bc213dbe7 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/constants.py @@ -0,0 +1,21 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +SAMPLE_RATE = 16000 +N_CLASS = 360 +N_MELS = 128 +MEL_FMIN = 30 +MEL_FMAX = 8000 +WINDOW_LENGTH = 1024 +CONST = 1997.379408437619 diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/deepunet.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/deepunet.py new file mode 100644 index 000000000..c25d0ff4b --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/deepunet.py @@ -0,0 +1,194 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle + +from .constants import N_MELS + + +class ConvBlockRes(paddle.nn.Layer): + def __init__(self, in_channels, out_channels, momentum=0.01): + super(ConvBlockRes, self).__init__() + self.conv = paddle.nn.Sequential( + paddle.nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=(3, 3), + stride=(1, 1), + padding=(1, 1), + bias_attr=False, + ), + paddle.nn.BatchNorm2D(num_features=out_channels, momentum=1 - momentum), + paddle.nn.ReLU(), + paddle.nn.Conv2D( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=(3, 3), + stride=(1, 1), + padding=(1, 1), + bias_attr=False, + ), + paddle.nn.BatchNorm2D(num_features=out_channels, momentum=1 - momentum), + paddle.nn.ReLU(), + ) + if in_channels != out_channels: + self.shortcut = paddle.nn.Conv2D(in_channels=in_channels, out_channels=out_channels, kernel_size=(1, 1)) + self.is_shortcut = True + else: + self.is_shortcut = False + + def forward(self, x): + if self.is_shortcut: + return self.conv(x) + self.shortcut(x) + else: + return self.conv(x) + x + + +class ResEncoderBlock(paddle.nn.Layer): + def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01): + super(ResEncoderBlock, self).__init__() + self.n_blocks = n_blocks + self.conv = paddle.nn.LayerList() + self.conv.append(ConvBlockRes(in_channels, out_channels, momentum)) + for i in range(n_blocks - 1): + self.conv.append(ConvBlockRes(out_channels, out_channels, momentum)) + self.kernel_size = kernel_size + if self.kernel_size is not None: + self.pool = paddle.nn.AvgPool2D(kernel_size=kernel_size, exclusive=False) + + def forward(self, x): + for i in range(self.n_blocks): + x = self.conv[i](x) + if self.kernel_size is not None: + return x, self.pool(x) + else: + return x + + +class ResDecoderBlock(paddle.nn.Layer): + def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01): + super(ResDecoderBlock, self).__init__() + out_padding = (0, 1) if stride == (1, 2) else (1, 1) + self.n_blocks = n_blocks + self.conv1 = paddle.nn.Sequential( + paddle.nn.Conv2DTranspose( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=(3, 3), + stride=stride, + padding=(1, 1), + output_padding=out_padding, + bias_attr=False, + ), + paddle.nn.BatchNorm2D(num_features=out_channels, momentum=1 - momentum), + paddle.nn.ReLU(), + ) + self.conv2 = paddle.nn.LayerList() + self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum)) + for i in range(n_blocks - 1): + self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) + + def forward(self, x, concat_tensor): + x = self.conv1(x) + x = paddle.concat(x=(x, concat_tensor), axis=1) + for i in range(self.n_blocks): + x = self.conv2[i](x) + return x + + +class Encoder(paddle.nn.Layer): + def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01): + super(Encoder, self).__init__() + self.n_encoders = n_encoders + self.bn = paddle.nn.BatchNorm2D(num_features=in_channels, momentum=1 - momentum) + self.layers = paddle.nn.LayerList() + self.latent_channels = [] + for i in range(self.n_encoders): + self.layers.append(ResEncoderBlock(in_channels, out_channels, kernel_size, n_blocks, momentum=momentum)) + self.latent_channels.append([out_channels, in_size]) + in_channels = out_channels + out_channels *= 2 + in_size //= 2 + self.out_size = in_size + self.out_channel = out_channels + + def forward(self, x): + concat_tensors = [] + x = self.bn(x) + for i in range(self.n_encoders): + _, x = self.layers[i](x) + concat_tensors.append(_) + return x, concat_tensors + + +class Intermediate(paddle.nn.Layer): + def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01): + super(Intermediate, self).__init__() + self.n_inters = n_inters + self.layers = paddle.nn.LayerList() + self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)) + for i in range(self.n_inters - 1): + self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)) + + def forward(self, x): + for i in range(self.n_inters): + x = self.layers[i](x) + return x + + +class Decoder(paddle.nn.Layer): + def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01): + super(Decoder, self).__init__() + self.layers = paddle.nn.LayerList() + self.n_decoders = n_decoders + for i in range(self.n_decoders): + out_channels = in_channels // 2 + self.layers.append(ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)) + in_channels = out_channels + + def forward(self, x, concat_tensors): + for i in range(self.n_decoders): + x = self.layers[i](x, concat_tensors[-1 - i]) + return x + + +class TimbreFilter(paddle.nn.Layer): + def __init__(self, latent_rep_channels): + super(TimbreFilter, self).__init__() + self.layers = paddle.nn.LayerList() + for latent_rep in latent_rep_channels: + self.layers.append(ConvBlockRes(latent_rep[0], latent_rep[0])) + + def forward(self, x_tensors): + out_tensors = [] + for i, layer in enumerate(self.layers): + out_tensors.append(layer(x_tensors[i])) + return out_tensors + + +class DeepUnet0(paddle.nn.Layer): + def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16): + super(DeepUnet0, self).__init__() + self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels) + self.intermediate = Intermediate( + self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks + ) + self.tf = TimbreFilter(self.encoder.latent_channels) + self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks) + + def forward(self, x): + x, concat_tensors = self.encoder(x) + x = self.intermediate(x) + x = self.decoder(x, concat_tensors) + return x diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/inference.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/inference.py new file mode 100644 index 000000000..a9769326e --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/inference.py @@ -0,0 +1,80 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import paddle +from basics.base_pe import BasePE +from torchaudio.transforms import Resample +from utils.infer_utils import resample_align_curve +from utils.pitch_utils import interp_f0 + +from .constants import * +from .model import E2E0 +from .spec import MelSpectrogram +from .utils import to_local_average_f0, to_viterbi_f0 + + +class RMVPE(BasePE): + def __init__(self, model_path, hop_length=160): + self.resample_kernel = {} + self.device = "cuda" if paddle.device.cuda.device_count() >= 1 else "cpu" + self.model = E2E0(4, 1, (2, 2)).eval().to(self.device) + ckpt = paddle.load(path=str(model_path)) + self.model.set_state_dict(state_dict=ckpt["model"]) + self.mel_extractor = MelSpectrogram( + N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX + ).to(self.device) + + @paddle.no_grad() + def mel2hidden(self, mel): + n_frames = tuple(mel.shape)[-1] + mel = paddle.nn.functional.pad( + x=mel, pad=(0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="constant", pad_from_left_axis=False + ) + hidden = self.model(mel) + return hidden[:, :n_frames] + + def decode(self, hidden, thred=0.03, use_viterbi=False): + if use_viterbi: + f0 = to_viterbi_f0(hidden, thred=thred) + else: + f0 = to_local_average_f0(hidden, thred=thred) + return f0 + + def infer_from_audio(self, audio, sample_rate=16000, thred=0.03, use_viterbi=False): + audio = paddle.to_tensor(data=audio).astype(dtype="float32").unsqueeze(axis=0).to(self.device) + if sample_rate == 16000: + audio_res = audio + else: + key_str = str(sample_rate) + if key_str not in self.resample_kernel: + self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128) + self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.device) + audio_res = self.resample_kernel[key_str](audio) + mel = self.mel_extractor(audio_res, center=True) + hidden = self.mel2hidden(mel) + f0 = self.decode(hidden, thred=thred, use_viterbi=use_viterbi) + return f0 + + def get_pitch(self, waveform, samplerate, length, *, hop_size, f0_min=65, f0_max=1100, speed=1, interp_uv=False): + f0 = self.infer_from_audio(waveform, sample_rate=samplerate) + uv = f0 == 0 + f0, uv = interp_f0(f0, uv) + hop_size = int(np.round(hop_size * speed)) + time_step = hop_size / samplerate + f0_res = resample_align_curve(f0, 0.01, time_step, length) + uv_res = resample_align_curve(uv.astype(np.float32), 0.01, time_step, length) > 0.5 + if not interp_uv: + f0_res[uv_res] = 0 + return f0_res, uv_res diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/model.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/model.py new file mode 100644 index 000000000..1e5b0107f --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/model.py @@ -0,0 +1,54 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import paddle +import paddle_aux + +from .constants import * +from .deepunet import DeepUnet0 +from .seq import BiGRU + + +class E2E0(paddle.nn.Layer): + def __init__( + self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16 + ): + super(E2E0, self).__init__() + self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels) + self.cnn = paddle.nn.Conv2D(in_channels=en_out_channels, out_channels=3, kernel_size=(3, 3), padding=(1, 1)) + if n_gru: + self.fc = paddle.nn.Sequential( + BiGRU(3 * N_MELS, 256, n_gru), + paddle.nn.Linear(in_features=512, out_features=N_CLASS), + paddle.nn.Dropout(p=0.25), + paddle.nn.Sigmoid(), + ) + else: + self.fc = paddle.nn.Sequential( + paddle.nn.Linear(in_features=3 * N_MELS, out_features=N_CLASS), + paddle.nn.Dropout(p=0.25), + paddle.nn.Sigmoid(), + ) + + def forward(self, mel): + mel = mel.transpose(perm=paddle_aux.transpose_aux_func(mel.ndim, -1, -2)).unsqueeze(axis=1) + x = ( + self.cnn(self.unet(mel)) + .transpose(perm=paddle_aux.transpose_aux_func(self.cnn(self.unet(mel)).ndim, 1, 2)) + .flatten(start_axis=-2) + ) + x = self.fc(x) + return x diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/seq.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/seq.py new file mode 100644 index 000000000..7a9ac7f55 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/seq.py @@ -0,0 +1,30 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import paddle + + +class BiGRU(paddle.nn.Layer): + def __init__(self, input_features, hidden_features, num_layers): + super(BiGRU, self).__init__() + self.gru = paddle.nn.GRU( + input_size=input_features, + hidden_size=hidden_features, + num_layers=num_layers, + time_major=not True, + direction="bidirect", + ) + + def forward(self, x): + return self.gru(x)[0] diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/spec.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/spec.py new file mode 100644 index 000000000..727167379 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/spec.py @@ -0,0 +1,65 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import paddle +from librosa.filters import mel + + +class MelSpectrogram(paddle.nn.Layer): + def __init__( + self, n_mel_channels, sampling_rate, win_length, hop_length, n_fft=None, mel_fmin=0, mel_fmax=None, clamp=1e-05 + ): + super().__init__() + n_fft = win_length if n_fft is None else n_fft + self.hann_window = {} + mel_basis = mel(sr=sampling_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=mel_fmin, fmax=mel_fmax, htk=True) + mel_basis = paddle.to_tensor(data=mel_basis).astype(dtype="float32") + self.register_buffer(name="mel_basis", tensor=mel_basis) + self.n_fft = win_length if n_fft is None else n_fft + self.hop_length = hop_length + self.win_length = win_length + self.sampling_rate = sampling_rate + self.n_mel_channels = n_mel_channels + self.clamp = clamp + + def forward(self, audio, keyshift=0, speed=1, center=True): + factor = 2 ** (keyshift / 12) + n_fft_new = int(np.round(self.n_fft * factor)) + win_length_new = int(np.round(self.win_length * factor)) + hop_length_new = int(np.round(self.hop_length * speed)) + keyshift_key = str(keyshift) + "_" + str(audio.place) + if keyshift_key not in self.hann_window: + self.hann_window[keyshift_key] = paddle.audio.functional.get_window("hann", win_length_new).to(audio.place) + fft = paddle.signal.stft( + audio, + n_fft=n_fft_new, + hop_length=hop_length_new, + win_length=win_length_new, + window=self.hann_window[keyshift_key], + center=center, + return_complex=True, + ) + magnitude = fft.abs() + if keyshift != 0: + size = self.n_fft // 2 + 1 + resize = magnitude.shape[1] + if resize < size: + magnitude = paddle.nn.functional.pad( + x=magnitude, pad=(0, 0, 0, size - resize), pad_from_left_axis=False + ) + magnitude = magnitude[:, :size, :] * self.win_length / win_length_new + mel_output = paddle.matmul(x=self.mel_basis, y=magnitude) + log_mel_spec = paddle.log(x=paddle.clip(x=mel_output, min=self.clamp)) + return log_mel_spec diff --git a/paddlemix/models/diffsinger/modules/pe/rmvpe/utils.py b/paddlemix/models/diffsinger/modules/pe/rmvpe/utils.py new file mode 100644 index 000000000..3e57f205a --- /dev/null +++ b/paddlemix/models/diffsinger/modules/pe/rmvpe/utils.py @@ -0,0 +1,54 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import librosa +import numpy as np +import paddle +import paddle_aux + +from .constants import * + + +def to_local_average_f0(hidden, center=None, thred=0.03): + idx = paddle.arange(end=N_CLASS)[None, None, :] + idx_cents = idx * 20 + CONST + if center is None: + center = paddle.argmax(x=hidden, axis=2, keepdim=True) + start = paddle.clip(x=center - 4, min=0) + end = paddle.clip(x=center + 5, max=N_CLASS) + idx_mask = (idx >= start) & (idx < end) + weights = hidden * idx_mask + product_sum = paddle.sum(x=weights * idx_cents, axis=2) + weight_sum = paddle.sum(x=weights, axis=2) + cents = product_sum / (weight_sum + (weight_sum == 0)) + f0 = 10 * 2 ** (cents / 1200) + uv = hidden.max(dim=2)[0] < thred + f0 = f0 * ~uv + return f0.squeeze(axis=0).cpu().numpy() + + +def to_viterbi_f0(hidden, thred=0.03): + if not hasattr(to_viterbi_f0, "transition"): + xx, yy = np.meshgrid(range(N_CLASS), range(N_CLASS)) + transition = np.maximum(30 - abs(xx - yy), 0) + transition = transition / transition.sum(axis=1, keepdims=True) + to_viterbi_f0.transition = transition + prob = hidden.squeeze(axis=0).cpu().numpy() + prob = prob.T + prob = prob / prob.sum(axis=0) + path = librosa.sequence.viterbi(prob, to_viterbi_f0.transition).astype(np.int64) + center = paddle.to_tensor(data=path).unsqueeze(axis=0).unsqueeze(axis=-1).to(hidden.place) + return to_local_average_f0(hidden, center=center, thred=thred) diff --git a/paddlemix/models/diffsinger/modules/toplevel.py b/paddlemix/models/diffsinger/modules/toplevel.py new file mode 100644 index 000000000..8d241daab --- /dev/null +++ b/paddlemix/models/diffsinger/modules/toplevel.py @@ -0,0 +1,323 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict + +import paddle + +import paddlemix.models.diffsinger.modules.compat as compat +from paddlemix.models.diffsinger.basics.base_module import CategorizedModule +from paddlemix.models.diffsinger.modules.aux_decoder import AuxDecoderAdaptor +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + NormalInitEmbedding as Embedding, +) +from paddlemix.models.diffsinger.modules.commons.common_layers import ( + XavierUniformInitLinear as Linear, +) +from paddlemix.models.diffsinger.modules.core import ( + GaussianDiffusion, + MultiVarianceDiffusion, + MultiVarianceRectifiedFlow, + PitchDiffusion, + PitchRectifiedFlow, + RectifiedFlow, +) +from paddlemix.models.diffsinger.modules.fastspeech.acoustic_encoder import ( + FastSpeech2Acoustic, +) +from paddlemix.models.diffsinger.modules.fastspeech.param_adaptor import ( + ParameterAdaptorModule, +) +from paddlemix.models.diffsinger.modules.fastspeech.tts_modules import ( + LengthRegulator, + RhythmRegulator, +) +from paddlemix.models.diffsinger.modules.fastspeech.variance_encoder import ( + FastSpeech2Variance, + MelodyEncoder, +) +from paddlemix.models.diffsinger.utils.hparams import hparams + + +class ShallowDiffusionOutput: + def __init__(self, *, aux_out=None, diff_out=None): + self.aux_out = aux_out + self.diff_out = diff_out + + +class DiffSingerAcoustic(CategorizedModule, ParameterAdaptorModule): + @property + def category(self): + return "acoustic" + + def __init__(self, vocab_size, out_dims): + CategorizedModule.__init__(self) + ParameterAdaptorModule.__init__(self) + self.fs2 = FastSpeech2Acoustic(vocab_size=vocab_size) + self.use_shallow_diffusion = hparams.get("use_shallow_diffusion", False) + self.shallow_args = hparams.get("shallow_diffusion_args", {}) + if self.use_shallow_diffusion: + self.train_aux_decoder = self.shallow_args["train_aux_decoder"] + self.train_diffusion = self.shallow_args["train_diffusion"] + self.aux_decoder_grad = self.shallow_args["aux_decoder_grad"] + self.aux_decoder = AuxDecoderAdaptor( + in_dims=hparams["hidden_size"], + out_dims=out_dims, + num_feats=1, + spec_min=hparams["spec_min"], + spec_max=hparams["spec_max"], + aux_decoder_arch=self.shallow_args["aux_decoder_arch"], + aux_decoder_args=self.shallow_args["aux_decoder_args"], + ) + self.diffusion_type = hparams.get("diffusion_type", "ddpm") + self.backbone_type = compat.get_backbone_type(hparams) + self.backbone_args = compat.get_backbone_args(hparams, self.backbone_type) + if self.diffusion_type == "ddpm": + self.diffusion = GaussianDiffusion( + out_dims=out_dims, + num_feats=1, + timesteps=hparams["timesteps"], + k_step=hparams["K_step"], + backbone_type=self.backbone_type, + backbone_args=self.backbone_args, + spec_min=hparams["spec_min"], + spec_max=hparams["spec_max"], + ) + elif self.diffusion_type == "reflow": + self.diffusion = RectifiedFlow( + out_dims=out_dims, + num_feats=1, + t_start=hparams["T_start"], + time_scale_factor=hparams["time_scale_factor"], + backbone_type=self.backbone_type, + backbone_args=self.backbone_args, + spec_min=hparams["spec_min"], + spec_max=hparams["spec_max"], + ) + else: + raise NotImplementedError(self.diffusion_type) + + def forward( + self, txt_tokens, mel2ph, f0, key_shift=None, speed=None, spk_embed_id=None, gt_mel=None, infer=True, **kwargs + ) -> ShallowDiffusionOutput: + condition = self.fs2( + txt_tokens, mel2ph, f0, key_shift=key_shift, speed=speed, spk_embed_id=spk_embed_id, **kwargs + ) + if infer: + if self.use_shallow_diffusion: + aux_mel_pred = self.aux_decoder(condition, infer=True) + aux_mel_pred *= (mel2ph > 0).astype(dtype="float32")[:, :, None] + if gt_mel is not None and self.shallow_args["val_gt_start"]: + src_mel = gt_mel + else: + src_mel = aux_mel_pred + else: + aux_mel_pred = src_mel = None + mel_pred = self.diffusion(condition, src_spec=src_mel, infer=True) + mel_pred *= (mel2ph > 0).astype(dtype="float32")[:, :, None] + return ShallowDiffusionOutput(aux_out=aux_mel_pred, diff_out=mel_pred) + elif self.use_shallow_diffusion: + if self.train_aux_decoder: + aux_cond = condition * self.aux_decoder_grad + condition.detach() * (1 - self.aux_decoder_grad) + aux_out = self.aux_decoder(aux_cond, infer=False) + else: + aux_out = None + if self.train_diffusion: + diff_out = self.diffusion(condition, gt_spec=gt_mel, infer=False) + else: + diff_out = None + return ShallowDiffusionOutput(aux_out=aux_out, diff_out=diff_out) + else: + aux_out = None + diff_out = self.diffusion(condition, gt_spec=gt_mel, infer=False) + return ShallowDiffusionOutput(aux_out=aux_out, diff_out=diff_out) + + +class DiffSingerVariance(CategorizedModule, ParameterAdaptorModule): + @property + def category(self): + return "variance" + + def __init__(self, vocab_size): + CategorizedModule.__init__(self) + ParameterAdaptorModule.__init__(self) + self.predict_dur = hparams["predict_dur"] + self.predict_pitch = hparams["predict_pitch"] + self.use_spk_id = hparams["use_spk_id"] + if self.use_spk_id: + self.spk_embed = Embedding(hparams["num_spk"], hparams["hidden_size"]) + self.fs2 = FastSpeech2Variance(vocab_size=vocab_size) + self.rr = RhythmRegulator() + self.lr = LengthRegulator() + self.diffusion_type = hparams.get("diffusion_type", "ddpm") + if self.predict_pitch: + self.use_melody_encoder = hparams.get("use_melody_encoder", False) + if self.use_melody_encoder: + self.melody_encoder = MelodyEncoder(enc_hparams=hparams["melody_encoder_args"]) + self.delta_pitch_embed = Linear(1, hparams["hidden_size"]) + else: + self.base_pitch_embed = Linear(1, hparams["hidden_size"]) + self.pitch_retake_embed = Embedding(2, hparams["hidden_size"]) + pitch_hparams = hparams["pitch_prediction_args"] + self.pitch_backbone_type = compat.get_backbone_type(hparams, nested_config=pitch_hparams) + self.pitch_backbone_args = compat.get_backbone_args(pitch_hparams, backbone_type=self.pitch_backbone_type) + if self.diffusion_type == "ddpm": + self.pitch_predictor = PitchDiffusion( + vmin=pitch_hparams["pitd_norm_min"], + vmax=pitch_hparams["pitd_norm_max"], + cmin=pitch_hparams["pitd_clip_min"], + cmax=pitch_hparams["pitd_clip_max"], + repeat_bins=pitch_hparams["repeat_bins"], + timesteps=hparams["timesteps"], + k_step=hparams["K_step"], + backbone_type=self.pitch_backbone_type, + backbone_args=self.pitch_backbone_args, + ) + elif self.diffusion_type == "reflow": + self.pitch_predictor = PitchRectifiedFlow( + vmin=pitch_hparams["pitd_norm_min"], + vmax=pitch_hparams["pitd_norm_max"], + cmin=pitch_hparams["pitd_clip_min"], + cmax=pitch_hparams["pitd_clip_max"], + repeat_bins=pitch_hparams["repeat_bins"], + time_scale_factor=hparams["time_scale_factor"], + backbone_type=self.pitch_backbone_type, + backbone_args=self.pitch_backbone_args, + ) + else: + raise ValueError(f"Invalid diffusion type: {self.diffusion_type}") + if self.predict_variances: + self.pitch_embed = Linear(1, hparams["hidden_size"]) + self.variance_embeds = paddle.nn.LayerDict( + sublayers={v_name: Linear(1, hparams["hidden_size"]) for v_name in self.variance_prediction_list} + ) + if self.diffusion_type == "ddpm": + self.variance_predictor = self.build_adaptor(cls=MultiVarianceDiffusion) + elif self.diffusion_type == "reflow": + self.variance_predictor = self.build_adaptor(cls=MultiVarianceRectifiedFlow) + else: + raise NotImplementedError(self.diffusion_type) + + def forward( + self, + txt_tokens, + midi, + ph2word, + ph_dur=None, + word_dur=None, + mel2ph=None, + note_midi=None, + note_rest=None, + note_dur=None, + note_glide=None, + mel2note=None, + base_pitch=None, + pitch=None, + pitch_expr=None, + pitch_retake=None, + variance_retake: Dict[str, paddle.Tensor] = None, + spk_id=None, + infer=True, + **kwargs + ): + if self.use_spk_id: + ph_spk_mix_embed = kwargs.get("ph_spk_mix_embed") + spk_mix_embed = kwargs.get("spk_mix_embed") + if ph_spk_mix_embed is not None and spk_mix_embed is not None: + ph_spk_embed = ph_spk_mix_embed + spk_embed = spk_mix_embed + else: + ph_spk_embed = spk_embed = self.spk_embed(spk_id)[:, None, :] + else: + ph_spk_embed = spk_embed = None + encoder_out, dur_pred_out = self.fs2( + txt_tokens, + midi=midi, + ph2word=ph2word, + ph_dur=ph_dur, + word_dur=word_dur, + spk_embed=ph_spk_embed, + infer=infer, + ) + if not self.predict_pitch and not self.predict_variances: + return dur_pred_out, None, {} if infer else None + if mel2ph is None and word_dur is not None: + dur_pred_align = self.rr(dur_pred_out, ph2word, word_dur) + mel2ph = self.lr(dur_pred_align) + mel2ph = paddle.nn.functional.pad( + x=mel2ph, pad=[0, tuple(base_pitch.shape)[1] - tuple(mel2ph.shape)[1]], pad_from_left_axis=False + ) + encoder_out = paddle.nn.functional.pad(x=encoder_out, pad=[0, 0, 1, 0], pad_from_left_axis=False) + mel2ph_ = mel2ph[..., None].tile(repeat_times=[1, 1, hparams[hidden_size]]) + condition = paddle.take_along_axis(arr=encoder_out, axis=1, indices=mel2ph_, broadcast=False) + if self.use_spk_id: + condition += spk_embed + if self.predict_pitch: + if self.use_melody_encoder: + melody_encoder_out = self.melody_encoder(note_midi, note_rest, note_dur, glide=note_glide) + melody_encoder_out = paddle.nn.functional.pad( + x=melody_encoder_out, pad=[0, 0, 1, 0], pad_from_left_axis=False + ) + mel2note_ = mel2note[..., None].tile(repeat_times=[1, 1, hparams[hidden_size]]) + melody_condition = paddle.take_along_axis( + arr=melody_encoder_out, axis=1, indices=mel2note_, broadcast=False + ) + pitch_cond = condition + melody_condition + else: + pitch_cond = condition.clone() + retake_unset = pitch_retake is None + if retake_unset: + pitch_retake = paddle.ones_like(x=mel2ph, dtype="bool") + if pitch_expr is None: + pitch_retake_embed = self.pitch_retake_embed(pitch_retake.astype(dtype="int64")) + else: + retake_true_embed = self.pitch_retake_embed(paddle.ones(shape=[1, 1], dtype="int64")) + retake_false_embed = self.pitch_retake_embed(paddle.zeros(shape=[1, 1], dtype="int64")) + pitch_expr = (pitch_expr * pitch_retake)[:, :, None] + pitch_retake_embed = pitch_expr * retake_true_embed + (1.0 - pitch_expr) * retake_false_embed + pitch_cond += pitch_retake_embed + if self.use_melody_encoder: + if retake_unset: + delta_pitch_in = paddle.zeros_like(x=base_pitch) + else: + delta_pitch_in = (pitch - base_pitch) * ~pitch_retake + pitch_cond += self.delta_pitch_embed(delta_pitch_in[:, :, None]) + else: + if not retake_unset: + base_pitch = base_pitch * pitch_retake + pitch * ~pitch_retake + pitch_cond += self.base_pitch_embed(base_pitch[:, :, None]) + if infer: + pitch_pred_out = self.pitch_predictor(pitch_cond, infer=True) + else: + pitch_pred_out = self.pitch_predictor(pitch_cond, pitch - base_pitch, infer=False) + else: + pitch_pred_out = None + if not self.predict_variances: + return dur_pred_out, pitch_pred_out, {} if infer else None + if pitch is None: + pitch = base_pitch + pitch_pred_out + var_cond = condition + self.pitch_embed(pitch[:, :, None]) + variance_inputs = self.collect_variance_inputs(**kwargs) + if variance_retake is not None: + variance_embeds = [ + (self.variance_embeds[v_name](v_input[:, :, None]) * ~variance_retake[v_name][:, :, None]) + for v_name, v_input in zip(self.variance_prediction_list, variance_inputs) + ] + var_cond += paddle.stack(x=variance_embeds, axis=-1).sum(axis=-1) + variance_outputs = self.variance_predictor(var_cond, variance_inputs, infer=infer) + if infer: + variances_pred_out = self.collect_variance_outputs(variance_outputs) + else: + variances_pred_out = variance_outputs + return dur_pred_out, pitch_pred_out, variances_pred_out diff --git a/paddlemix/models/diffsinger/modules/vocoders/__init__.py b/paddlemix/models/diffsinger/modules/vocoders/__init__.py new file mode 100644 index 000000000..39b14a505 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/vocoders/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from paddlemix.models.diffsinger.modules.vocoders import ddsp, nsf_hifigan diff --git a/paddlemix/models/diffsinger/modules/vocoders/ddsp.py b/paddlemix/models/diffsinger/modules/vocoders/ddsp.py new file mode 100644 index 000000000..d2226ca81 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/vocoders/ddsp.py @@ -0,0 +1,176 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pathlib +import sys + +import numpy as np +import paddle +import paddle_aux +import yaml +from librosa.filters import mel as librosa_mel_fn + +from paddlemix.models.diffsinger.basics.base_vocoder import BaseVocoder +from paddlemix.models.diffsinger.modules.vocoders.registry import register_vocoder +from paddlemix.models.diffsinger.utils.hparams import hparams + + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_model(model_path: pathlib.Path, device="cpu"): + config_file = model_path.with_name("config.yaml") + with open(config_file, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + print(" [Loading] " + str(model_path)) + model = paddle.jit.load(model_path) + model.eval() + return model, args + + +@register_vocoder +class DDSP(BaseVocoder): + def __init__(self, device="cpu"): + self.device = device + model_path = pathlib.Path(hparams["vocoder_ckpt"]) + assert model_path.exists(), "DDSP model file is not found!" + self.model, self.args = load_model(model_path, device=self.device) + + def to_device(self, device): + pass + + def get_device(self): + return self.device + + def spec2wav_torch(self, mel, f0): + if self.args.data.sampling_rate != hparams["audio_sample_rate"]: + print( + "Mismatch parameters: hparams['audio_sample_rate']=", + hparams["audio_sample_rate"], + "!=", + self.args.data.sampling_rate, + "(vocoder)", + ) + if self.args.data.n_mels != hparams["audio_num_mel_bins"]: + print( + "Mismatch parameters: hparams['audio_num_mel_bins']=", + hparams["audio_num_mel_bins"], + "!=", + self.args.data.n_mels, + "(vocoder)", + ) + if self.args.data.n_fft != hparams["fft_size"]: + print( + "Mismatch parameters: hparams['fft_size']=", + hparams["fft_size"], + "!=", + self.args.data.n_fft, + "(vocoder)", + ) + if self.args.data.win_length != hparams["win_size"]: + print( + "Mismatch parameters: hparams['win_size']=", + hparams["win_size"], + "!=", + self.args.data.win_length, + "(vocoder)", + ) + if self.args.data.block_size != hparams["hop_size"]: + print( + "Mismatch parameters: hparams['hop_size']=", + hparams["hop_size"], + "!=", + self.args.data.block_size, + "(vocoder)", + ) + if self.args.data.mel_fmin != hparams["fmin"]: + print("Mismatch parameters: hparams['fmin']=", hparams["fmin"], "!=", self.args.data.mel_fmin, "(vocoder)") + if self.args.data.mel_fmax != hparams["fmax"]: + print("Mismatch parameters: hparams['fmax']=", hparams["fmax"], "!=", self.args.data.mel_fmax, "(vocoder)") + with paddle.no_grad(): + mel = mel.to(self.device) + mel_base = hparams.get("mel_base", 10) + if mel_base != "e": + assert mel_base in [10, "10"], "mel_base must be 'e', '10' or 10." + else: + mel = 0.434294 * mel + f0 = f0.unsqueeze(axis=-1).to(self.device) + signal, _, (s_h, s_n) = self.model(mel, f0) + signal = signal.view(-1) + return signal + + def spec2wav(self, mel, f0): + if self.args.data.sampling_rate != hparams["audio_sample_rate"]: + print( + "Mismatch parameters: hparams['audio_sample_rate']=", + hparams["audio_sample_rate"], + "!=", + self.args.data.sampling_rate, + "(vocoder)", + ) + if self.args.data.n_mels != hparams["audio_num_mel_bins"]: + print( + "Mismatch parameters: hparams['audio_num_mel_bins']=", + hparams["audio_num_mel_bins"], + "!=", + self.args.data.n_mels, + "(vocoder)", + ) + if self.args.data.n_fft != hparams["fft_size"]: + print( + "Mismatch parameters: hparams['fft_size']=", + hparams["fft_size"], + "!=", + self.args.data.n_fft, + "(vocoder)", + ) + if self.args.data.win_length != hparams["win_size"]: + print( + "Mismatch parameters: hparams['win_size']=", + hparams["win_size"], + "!=", + self.args.data.win_length, + "(vocoder)", + ) + if self.args.data.block_size != hparams["hop_size"]: + print( + "Mismatch parameters: hparams['hop_size']=", + hparams["hop_size"], + "!=", + self.args.data.block_size, + "(vocoder)", + ) + if self.args.data.mel_fmin != hparams["fmin"]: + print("Mismatch parameters: hparams['fmin']=", hparams["fmin"], "!=", self.args.data.mel_fmin, "(vocoder)") + if self.args.data.mel_fmax != hparams["fmax"]: + print("Mismatch parameters: hparams['fmax']=", hparams["fmax"], "!=", self.args.data.mel_fmax, "(vocoder)") + with paddle.no_grad(): + mel = paddle.to_tensor(data=mel, dtype="float32").unsqueeze(axis=0).to(self.device) + mel_base = hparams.get("mel_base", 10) + if mel_base != "e": + assert mel_base in [10, "10"], "mel_base must be 'e', '10' or 10." + else: + mel = 0.434294 * mel + f0 = paddle.to_tensor(data=f0, dtype="float32").unsqueeze(axis=0).unsqueeze(axis=-1).to(self.device) + signal, _, (s_h, s_n) = self.model(mel, f0) + signal = signal.view(-1) + wav_out = signal.cpu().numpy() + return wav_out diff --git a/paddlemix/models/diffsinger/modules/vocoders/nsf_hifigan.py b/paddlemix/models/diffsinger/modules/vocoders/nsf_hifigan.py new file mode 100644 index 000000000..7a4c1c357 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/vocoders/nsf_hifigan.py @@ -0,0 +1,138 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pathlib +import sys + +import paddle +import paddle_aux + +from paddlemix.models.diffsinger.basics.base_vocoder import BaseVocoder +from paddlemix.models.diffsinger.modules.nsf_hifigan.models import load_model +from paddlemix.models.diffsinger.modules.vocoders.registry import register_vocoder +from paddlemix.models.diffsinger.utils.hparams import hparams + + +@register_vocoder +class NsfHifiGAN(BaseVocoder): + def __init__(self): + model_path = pathlib.Path(hparams["vocoder_ckpt"]) + if not model_path.exists(): + raise FileNotFoundError( + f"NSF-HiFiGAN vocoder model is not found at '{model_path}'. Please follow instructions in docs/BestPractices.md#vocoders to get one." + ) + print(f"| Load HifiGAN: {model_path}") + self.model, self.h = load_model(model_path) + + @property + def device(self): + # return next(self.model.parameters()).place + return next(iter(self.model.parameters())).place + + def to_device(self, device): + self.model.to(device) + + def get_device(self): + return self.device + + def spec2wav_torch(self, mel, **kwargs): + if self.h.sampling_rate != hparams["audio_sample_rate"]: + print( + "Mismatch parameters: hparams['audio_sample_rate']=", + hparams["audio_sample_rate"], + "!=", + self.h.sampling_rate, + "(vocoder)", + ) + if self.h.num_mels != hparams["audio_num_mel_bins"]: + print( + "Mismatch parameters: hparams['audio_num_mel_bins']=", + hparams["audio_num_mel_bins"], + "!=", + self.h.num_mels, + "(vocoder)", + ) + if self.h.n_fft != hparams["fft_size"]: + print("Mismatch parameters: hparams['fft_size']=", hparams["fft_size"], "!=", self.h.n_fft, "(vocoder)") + if self.h.win_size != hparams["win_size"]: + print("Mismatch parameters: hparams['win_size']=", hparams["win_size"], "!=", self.h.win_size, "(vocoder)") + if self.h.hop_size != hparams["hop_size"]: + print("Mismatch parameters: hparams['hop_size']=", hparams["hop_size"], "!=", self.h.hop_size, "(vocoder)") + if self.h.fmin != hparams["fmin"]: + print("Mismatch parameters: hparams['fmin']=", hparams["fmin"], "!=", self.h.fmin, "(vocoder)") + if self.h.fmax != hparams["fmax"]: + print("Mismatch parameters: hparams['fmax']=", hparams["fmax"], "!=", self.h.fmax, "(vocoder)") + with paddle.no_grad(): + c = mel.transpose(perm=paddle_aux.transpose_aux_func(mel.ndim, 2, 1)) + mel_base = hparams.get("mel_base", 10) + if mel_base != "e": + assert mel_base in [10, "10"], "mel_base must be 'e', '10' or 10." + c = 2.30259 * c + f0 = kwargs.get("f0") + if f0 is not None: + y = self.model(c, f0).view(-1) + else: + y = self.model(c).view(-1) + return y + + def spec2wav(self, mel, **kwargs): + if self.h.sampling_rate != hparams["audio_sample_rate"]: + print( + "Mismatch parameters: hparams['audio_sample_rate']=", + hparams["audio_sample_rate"], + "!=", + self.h.sampling_rate, + "(vocoder)", + ) + if self.h.num_mels != hparams["audio_num_mel_bins"]: + print( + "Mismatch parameters: hparams['audio_num_mel_bins']=", + hparams["audio_num_mel_bins"], + "!=", + self.h.num_mels, + "(vocoder)", + ) + if self.h.n_fft != hparams["fft_size"]: + print("Mismatch parameters: hparams['fft_size']=", hparams["fft_size"], "!=", self.h.n_fft, "(vocoder)") + if self.h.win_size != hparams["win_size"]: + print("Mismatch parameters: hparams['win_size']=", hparams["win_size"], "!=", self.h.win_size, "(vocoder)") + if self.h.hop_size != hparams["hop_size"]: + print("Mismatch parameters: hparams['hop_size']=", hparams["hop_size"], "!=", self.h.hop_size, "(vocoder)") + if self.h.fmin != hparams["fmin"]: + print("Mismatch parameters: hparams['fmin']=", hparams["fmin"], "!=", self.h.fmin, "(vocoder)") + if self.h.fmax != hparams["fmax"]: + print("Mismatch parameters: hparams['fmax']=", hparams["fmax"], "!=", self.h.fmax, "(vocoder)") + with paddle.no_grad(): + c = ( + paddle.to_tensor(data=mel, dtype="float32") + .unsqueeze(axis=0) + .transpose( + perm=paddle_aux.transpose_aux_func( + paddle.to_tensor(data=mel, dtype="float32").unsqueeze(axis=0).ndim, 2, 1 + ) + ) + .to(self.device) + ) + mel_base = hparams.get("mel_base", 10) + if mel_base != "e": + assert mel_base in [10, "10"], "mel_base must be 'e', '10' or 10." + c = 2.30259 * c + f0 = kwargs.get("f0") + if f0 is not None: + f0 = paddle.to_tensor(data=f0[None, :], dtype="float32").to(self.device) + y = self.model(c, f0).view(-1) + else: + y = self.model(c).view(-1) + wav_out = y.cpu().numpy() + return wav_out diff --git a/paddlemix/models/diffsinger/modules/vocoders/registry.py b/paddlemix/models/diffsinger/modules/vocoders/registry.py new file mode 100644 index 000000000..f19db1cf1 --- /dev/null +++ b/paddlemix/models/diffsinger/modules/vocoders/registry.py @@ -0,0 +1,34 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import importlib + +VOCODERS = {} + + +def register_vocoder(cls): + VOCODERS[cls.__name__.lower()] = cls + VOCODERS[cls.__name__] = cls + return cls + + +def get_vocoder_cls(hparams): + if hparams["vocoder"] in VOCODERS: + return VOCODERS[hparams["vocoder"]] + else: + vocoder_cls = hparams["vocoder"] + pkg = ".".join(vocoder_cls.split(".")[:-1]) + cls_name = vocoder_cls.split(".")[-1] + vocoder_cls = getattr(importlib.import_module(pkg), cls_name) + return vocoder_cls diff --git a/paddlemix/models/diffsinger/utils/__init__.py b/paddlemix/models/diffsinger/utils/__init__.py new file mode 100644 index 000000000..cb25e9324 --- /dev/null +++ b/paddlemix/models/diffsinger/utils/__init__.py @@ -0,0 +1,345 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import pathlib +import re +import time +import types +from collections import OrderedDict + +import numpy as np +import paddle + +from paddlemix.models.diffsinger.basics.base_module import CategorizedModule +from paddlemix.models.diffsinger.utils import paddle_aux +from paddlemix.models.diffsinger.utils.hparams import hparams + +# import paddle_aux + + +def tensors_to_scalars(metrics): + new_metrics = {} + for k, v in metrics.items(): + if isinstance(v, paddle.Tensor): + v = v.item() + if type(v) is dict: + v = tensors_to_scalars(v) + new_metrics[k] = v + return new_metrics + + +def collate_nd(values, pad_value=0, max_len=None): + """ + Pad a list of Nd tensors on their first dimension and stack them into a (N+1)d tensor. + """ + size = max(v.shape[0] for v in values) if max_len is None else max_len, *tuple(values[0].shape)[1:] + res = paddle.full(shape=(len(values), *size), fill_value=pad_value, dtype=values[0].dtype) + for i, v in enumerate(values): + res[i, : len(v), ...] = v + return res + + +def random_continuous_masks(*shape: int, dim: int, device: (str | (paddle.CPUPlace, paddle.CUDAPlace, str)) = "cpu"): # type: ignore + start, end = ( + paddle.sort( + x=paddle.randint( + low=0, high=shape[dim] + 1, shape=(*shape[:dim], 2, *((1,) * (len(shape) - dim - 1))) + ).expand(shape=[*((-1,) * (dim + 1)), *shape[dim + 1 :]]), + axis=dim, + ), + paddle.argsort( + x=paddle.randint( + low=0, high=shape[dim] + 1, shape=(*shape[:dim], 2, *((1,) * (len(shape) - dim - 1))) + ).expand(shape=[*((-1,) * (dim + 1)), *shape[dim + 1 :]]), + axis=dim, + ), + )[0].split(1, dim=dim) + idx = paddle.arange(start=0, end=shape[dim], dtype="int64").reshape( + *((1,) * dim), shape[dim], *((1,) * (len(shape) - dim - 1)) + ) + masks = (idx >= start) & (idx < end) + return masks + + +def _is_batch_full(batch, num_frames, max_batch_frames, max_batch_size): + if len(batch) == 0: + return 0 + if len(batch) == max_batch_size: + return 1 + if num_frames > max_batch_frames: + return 1 + return 0 + + +def batch_by_size(indices, num_frames_fn, max_batch_frames=80000, max_batch_size=48, required_batch_size_multiple=1): + """ + Yield mini-batches of indices bucketed by size. Batches may contain + sequences of different lengths. + + Args: + indices (List[int]): ordered list of dataset indices + num_frames_fn (callable): function that returns the number of frames at + a given index + max_batch_frames (int, optional): max number of frames in each batch + (default: 80000). + max_batch_size (int, optional): max number of sentences in each + batch (default: 48). + required_batch_size_multiple: require the batch size to be multiple + of a given number + """ + bsz_mult = required_batch_size_multiple + if isinstance(indices, types.GeneratorType): + indices = np.fromiter(indices, dtype=np.int64, count=-1) + sample_len = 0 + sample_lens = [] + batch = [] + batches = [] + for i in range(len(indices)): + idx = indices[i] + num_frames = num_frames_fn(idx) + sample_lens.append(num_frames) + sample_len = max(sample_len, num_frames) + assert ( + sample_len <= max_batch_frames + ), "sentence at index {} of size {} exceeds max_batch_samples limit of {}!".format( + idx, sample_len, max_batch_frames + ) + num_frames = (len(batch) + 1) * sample_len + if _is_batch_full(batch, num_frames, max_batch_frames, max_batch_size): + mod_len = max(bsz_mult * (len(batch) // bsz_mult), len(batch) % bsz_mult) + batches.append(batch[:mod_len]) + batch = batch[mod_len:] + sample_lens = sample_lens[mod_len:] + sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 + batch.append(idx) + if len(batch) > 0: + batches.append(batch) + return batches + + +def make_positions(tensor, padding_idx): + """Replace non-padding symbols with their position numbers. + + Position numbers begin at padding_idx+1. Padding symbols are ignored. + """ + mask = tensor.not_equal(y=paddle.to_tensor(padding_idx)).astype(dtype="int32") + return (paddle.cumsum(x=mask, axis=1).astype(dtype=mask.dtype) * mask).astype(dtype="int64") + padding_idx + + +def softmax(x, dim): + return paddle.nn.functional.softmax(x=x, axis=dim, dtype="float32") + + +def unpack_dict_to_list(samples): + samples_ = [] + bsz = samples.get("outputs").shape[0] + for i in range(bsz): + res = {} + for k, v in samples.items(): + try: + res[k] = v[i] + except: + pass + samples_.append(res) + return samples_ + + +def filter_kwargs(dict_to_filter, kwarg_obj): + import inspect + + sig = inspect.signature(kwarg_obj) + if any(param.kind == param.VAR_KEYWORD for param in sig.parameters.values()): + return dict_to_filter.copy() + filter_keys = [ + param.name + for param in sig.parameters.values() + if param.kind == param.POSITIONAL_OR_KEYWORD or param.kind == param.KEYWORD_ONLY + ] + filtered_dict = { + filter_key: dict_to_filter[filter_key] for filter_key in filter_keys if filter_key in dict_to_filter + } + return filtered_dict + + +def load_ckpt( + cur_model, + ckpt_base_dir, + ckpt_steps=None, + prefix_in_ckpt="model", + ignored_prefixes=None, + key_in_ckpt="state_dict", + strict=True, + device="cpu", +): + if ignored_prefixes is None: + ignored_prefixes = ["model.fs2.encoder.embed_tokens"] + if not isinstance(ckpt_base_dir, pathlib.Path): + ckpt_base_dir = pathlib.Path(ckpt_base_dir) + if ckpt_base_dir.is_file(): + checkpoint_path = [ckpt_base_dir] + elif ckpt_steps is not None: + checkpoint_path = [ckpt_base_dir / f"model_ckpt_steps_{int(ckpt_steps)}.ckpt"] + else: + base_dir = ckpt_base_dir + checkpoint_path = sorted( + [ + ckpt_file + for ckpt_file in base_dir.iterdir() + if ckpt_file.is_file() and re.fullmatch("model_ckpt_steps_\\d+\\.ckpt", ckpt_file.name) + ], + key=lambda x: int(re.search("\\d+", x.name).group(0)), + ) + assert len(checkpoint_path) > 0, f"| ckpt not found in {ckpt_base_dir}." + checkpoint_path = checkpoint_path[-1] + ckpt_loaded = paddle.load(path=str(checkpoint_path)) + if isinstance(cur_model, CategorizedModule): + cur_model.check_category(ckpt_loaded.get("category")) + if key_in_ckpt is None: + state_dict = ckpt_loaded + else: + state_dict = ckpt_loaded[key_in_ckpt] + if prefix_in_ckpt is not None: + state_dict = OrderedDict( + { + k[len(prefix_in_ckpt) + 1 :]: v + for k, v in state_dict.items() + if k.startswith(f"{prefix_in_ckpt}.") + if all(not k.startswith(p) for p in ignored_prefixes) + } + ) + if not strict: + cur_model_state_dict = cur_model.state_dict() + unmatched_keys = [] + for key, param in state_dict.items(): + if key in cur_model_state_dict: + new_param = cur_model_state_dict[key] + if tuple(new_param.shape) != tuple(param.shape): + unmatched_keys.append(key) + print("| Unmatched keys: ", key, tuple(new_param.shape), tuple(param.shape)) + for key in unmatched_keys: + del state_dict[key] + cur_model.set_state_dict(state_dict=state_dict) + shown_model_name = "state dict" + if prefix_in_ckpt is not None: + shown_model_name = f"'{prefix_in_ckpt}'" + elif key_in_ckpt is not None: + shown_model_name = f"'{key_in_ckpt}'" + print(f"| load {shown_model_name} from '{checkpoint_path}'.") + + +def remove_padding(x, padding_idx=0): + if x is None: + return None + assert len(tuple(x.shape)) in [1, 2] + if len(tuple(x.shape)) == 2: + return x[np.abs(x).sum(-1) != padding_idx] + elif len(tuple(x.shape)) == 1: + return x[x != padding_idx] + + +class Timer: + timer_map = {} + + def __init__(self, name, print_time=False): + if name not in Timer.timer_map: + Timer.timer_map[name] = 0 + self.name = name + self.print_time = print_time + + def __enter__(self): + self.t = time.time() + + def __exit__(self, exc_type, exc_val, exc_tb): + Timer.timer_map[self.name] += time.time() - self.t + if self.print_time: + print(self.name, Timer.timer_map[self.name]) + + +def print_arch(model, model_name="model"): + print(f"| {model_name} Arch: ", model) + + +def num_params(model, print_out=True, model_name="model"): + parameters = filter(lambda p: not p.stop_gradient, model.parameters()) + parameters = sum([np.prod(tuple(p.shape)) for p in parameters]) / 1000000 + if print_out: + print(f"| {model_name} Trainable Parameters: %.3fM" % parameters) + return parameters + + +def build_object_from_class_name(cls_str, parent_cls, *args, **kwargs): + import importlib + + pkg = ".".join(cls_str.split(".")[:-1]) + cls_name = cls_str.split(".")[-1] + cls_type = getattr(importlib.import_module(pkg), cls_name) + if parent_cls is not None: + assert issubclass(cls_type, parent_cls), f"| {cls_type} is not subclass of {parent_cls}." + return cls_type(*args, **filter_kwargs(kwargs, cls_type)) + + +def build_lr_scheduler_from_config(optimizer, scheduler_args): + # try: + # except ImportError: + from paddle.optimizer.lr import LRScheduler as LRScheduler + + def helper(params): + if isinstance(params, list): + return [helper(s) for s in params] + elif isinstance(params, dict): + resolved = {k: helper(v) for k, v in params.items()} + if "cls" in resolved: + if ( + resolved["cls"] == "torch.optim.lr_scheduler.ChainedScheduler" + and scheduler_args["scheduler_cls"] == "torch.optim.lr_scheduler.SequentialLR" + ): + raise ValueError(f"ChainedScheduler cannot be part of a SequentialLR.") + resolved["optimizer"] = optimizer + obj = build_object_from_class_name(resolved["cls"], LRScheduler, **resolved) + return obj + return resolved + else: + return params + + resolved = helper(scheduler_args) + resolved["optimizer"] = optimizer + return build_object_from_class_name(scheduler_args["scheduler_cls"], LRScheduler, **resolved) + + +def simulate_lr_scheduler(optimizer_args, scheduler_args, step_count, num_param_groups=1): + optimizer = build_object_from_class_name( + optimizer_args["optimizer_cls"], + paddle.optimizer.Optimizer, + [ + { + "params": paddle.base.framework.EagerParamBase.from_tensor(tensor=paddle.to_tensor([])), + "initial_lr": optimizer_args["lr"], + } + for _ in range(num_param_groups) + ], + **optimizer_args, + ) + scheduler = build_lr_scheduler_from_config(optimizer, scheduler_args) + scheduler.optimizer._step_count = 1 + for _ in range(step_count): + scheduler.step() + return scheduler.state_dict() + + +def remove_suffix(string: str, suffix: str): + if string.endswith(suffix): + string = string[: -len(suffix)] + return string diff --git a/paddlemix/models/diffsinger/utils/binarizer_utils.py b/paddlemix/models/diffsinger/utils/binarizer_utils.py new file mode 100644 index 000000000..aeae05e03 --- /dev/null +++ b/paddlemix/models/diffsinger/utils/binarizer_utils.py @@ -0,0 +1,241 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +from typing import Union + +import librosa +import numpy as np +import paddle +import parselmouth +from modules.nsf_hifigan.nvSTFT import STFT +from utils.decomposed_waveform import DecomposedWaveform +from utils.pitch_utils import interp_f0 + + +def get_mel_torch( + waveform, + samplerate, + *, + num_mel_bins=128, + hop_size=512, + win_size=2048, + fft_size=2048, + fmin=40, + fmax=16000, + keyshift=0, + speed=1, + device=None +): + if device is None: + device = "cuda" if paddle.device.cuda.device_count() >= 1 else "cpu" + stft = STFT(samplerate, num_mel_bins, fft_size, win_size, hop_size, fmin, fmax, device=device) + with paddle.no_grad(): + wav_torch = paddle.to_tensor(data=waveform).to(device) + mel_torch = stft.get_mel(wav_torch.unsqueeze(axis=0), keyshift=keyshift, speed=speed).squeeze(axis=0).T + return mel_torch.cpu().numpy() + + +@paddle.no_grad() +def get_mel2ph_torch(lr, durs, length, timestep, device="cpu"): + ph_acc = paddle.round(paddle.cumsum(x=durs.to(device), axis=0) / timestep + 0.5).astype(dtype="int64") + ph_dur = paddle.diff(x=ph_acc, axis=0, prepend=paddle.to_tensor(data=[0], dtype="int64").to(device)) + mel2ph = lr(ph_dur[None])[0] + num_frames = tuple(mel2ph.shape)[0] + if num_frames < length: + mel2ph = paddle.concat(x=(mel2ph, paddle.full(shape=(length - num_frames,), fill_value=mel2ph[-1])), axis=0) + elif num_frames > length: + mel2ph = mel2ph[:length] + return mel2ph + + +def get_pitch_parselmouth(waveform, samplerate, length, *, hop_size, f0_min=65, f0_max=1100, speed=1, interp_uv=False): + """ + + :param waveform: [T] + :param samplerate: sampling rate + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param f0_min: Minimum f0 in Hz + :param f0_max: Maximum f0 in Hz + :param speed: Change the speed + :param interp_uv: Interpolate unvoiced parts + :return: f0, uv + """ + hop_size = int(np.round(hop_size * speed)) + time_step = hop_size / samplerate + l_pad = int(np.ceil(1.5 / f0_min * samplerate)) + r_pad = hop_size * ((len(waveform) - 1) // hop_size + 1) - len(waveform) + l_pad + 1 + waveform = np.pad(waveform, (l_pad, r_pad)) + s = parselmouth.Sound(waveform, sampling_frequency=samplerate).to_pitch_ac( + time_step=time_step, voicing_threshold=0.6, pitch_floor=f0_min, pitch_ceiling=f0_max + ) + assert np.abs(s.t1 - 1.5 / f0_min) < 0.001 + f0 = s.selected_array["frequency"].astype(np.float32) + if len(f0) < length: + f0 = np.pad(f0, (0, length - len(f0))) + f0 = f0[:length] + uv = f0 == 0 + if interp_uv: + f0, uv = interp_f0(f0, uv) + return f0, uv + + +def get_energy_librosa(waveform, length, *, hop_size, win_size, domain="db"): + """ + Definition of energy: RMS of the waveform, in dB representation + :param waveform: [T] + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param win_size: Window size, in number of samples + :param domain: db or amplitude + :return: energy + """ + energy = librosa.feature.rms(y=waveform, frame_length=win_size, hop_length=hop_size)[0] + if len(energy) < length: + energy = np.pad(energy, (0, length - len(energy))) + energy = energy[:length] + if domain == "db": + energy = librosa.amplitude_to_db(energy) + elif domain == "amplitude": + pass + else: + raise ValueError(f"Invalid domain: {domain}") + return energy + + +def get_breathiness( + waveform: Union[np.ndarray, DecomposedWaveform], + samplerate, + f0, + length, + *, + hop_size=None, + fft_size=None, + win_size=None +): + """ + Definition of breathiness: RMS of the aperiodic part, in dB representation + :param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given + :param samplerate: sampling rate + :param f0: reference f0 + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param fft_size: Number of fft bins + :param win_size: Window size, in number of samples + :return: breathiness + """ + if not isinstance(waveform, DecomposedWaveform): + waveform = DecomposedWaveform( + waveform=waveform, samplerate=samplerate, f0=f0, hop_size=hop_size, fft_size=fft_size, win_size=win_size + ) + waveform_ap = waveform.aperiodic() + breathiness = get_energy_librosa( + waveform_ap, length=length, hop_size=waveform.hop_size, win_size=waveform.win_size + ) + return breathiness + + +def get_voicing( + waveform: Union[np.ndarray, DecomposedWaveform], + samplerate, + f0, + length, + *, + hop_size=None, + fft_size=None, + win_size=None +): + """ + Definition of voicing: RMS of the harmonic part, in dB representation + :param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given + :param samplerate: sampling rate + :param f0: reference f0 + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param fft_size: Number of fft bins + :param win_size: Window size, in number of samples + :return: voicing + """ + if not isinstance(waveform, DecomposedWaveform): + waveform = DecomposedWaveform( + waveform=waveform, samplerate=samplerate, f0=f0, hop_size=hop_size, fft_size=fft_size, win_size=win_size + ) + waveform_sp = waveform.harmonic() + voicing = get_energy_librosa(waveform_sp, length=length, hop_size=waveform.hop_size, win_size=waveform.win_size) + return voicing + + +def get_tension_base_harmonic( + waveform: Union[np.ndarray, DecomposedWaveform], + samplerate, + f0, + length, + *, + hop_size=None, + fft_size=None, + win_size=None, + domain="logit" +): + """ + Definition of tension: radio of the real harmonic part (harmonic part except the base harmonic) + to the full harmonic part. + :param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given + :param samplerate: sampling rate + :param f0: reference f0 + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param fft_size: Number of fft bins + :param win_size: Window size, in number of samples + :param domain: The domain of the final ratio representation. + Can be 'ratio' (the raw ratio), 'db' (log decibel) or 'logit' (the reverse function of sigmoid) + :return: tension + """ + if not isinstance(waveform, DecomposedWaveform): + waveform = DecomposedWaveform( + waveform=waveform, samplerate=samplerate, f0=f0, hop_size=hop_size, fft_size=fft_size, win_size=win_size + ) + waveform_h = waveform.harmonic() + waveform_base_h = waveform.harmonic(0) + energy_base_h = get_energy_librosa( + waveform_base_h, length, hop_size=waveform.hop_size, win_size=waveform.win_size, domain="amplitude" + ) + energy_h = get_energy_librosa( + waveform_h, length, hop_size=waveform.hop_size, win_size=waveform.win_size, domain="amplitude" + ) + tension = np.sqrt(np.clip(energy_h**2 - energy_base_h**2, a_min=0, a_max=None)) / (energy_h + 1e-05) + if domain == "ratio": + tension = np.clip(tension, a_min=0, a_max=1) + elif domain == "db": + tension = np.clip(tension, a_min=1e-05, a_max=1) + tension = librosa.amplitude_to_db(tension) + elif domain == "logit": + tension = np.clip(tension, a_min=0.0001, a_max=1 - 0.0001) + tension = np.log(tension / (1 - tension)) + return tension + + +class SinusoidalSmoothingConv1d(paddle.nn.Conv1D): + def __init__(self, kernel_size): + super().__init__( + in_channels=1, + out_channels=1, + kernel_size=kernel_size, + bias=False, + padding="same", + padding_mode="replicate", + ) + smooth_kernel = paddle.sin(x=paddle.to_tensor(data=np.linspace(0, 1, kernel_size).astype(np.float32) * np.pi)) + smooth_kernel /= smooth_kernel.sum() + self.weight.data = smooth_kernel[None, None] diff --git a/paddlemix/models/diffsinger/utils/decomposed_waveform.py b/paddlemix/models/diffsinger/utils/decomposed_waveform.py new file mode 100644 index 000000000..cbbc75b84 --- /dev/null +++ b/paddlemix/models/diffsinger/utils/decomposed_waveform.py @@ -0,0 +1,333 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict + +import numpy as np +import pyworld as pw +import torch +from torch.nn import functional as F + +from paddlemix.models.diffsinger.modules.hnsep.vr import load_sep_model +from paddlemix.models.diffsinger.utils.hparams import hparams +from paddlemix.models.diffsinger.utils.pitch_utils import interp_f0 + + +class DecomposedWaveform: + def __new__( + cls, waveform, samplerate, f0, *, hop_size=None, fft_size=None, win_size=None, algorithm="world", device=None + ): + if algorithm == "world": + obj = object.__new__(DecomposedWaveformPyWorld) + # noinspection PyProtectedMember + obj._init( + waveform=waveform, + samplerate=samplerate, + f0=f0, + hop_size=hop_size, + fft_size=fft_size, + win_size=win_size, + device=device, + ) + elif algorithm == "vr": + obj = object.__new__(DecomposedWaveformVocalRemover) + hnsep_ckpt = hparams["hnsep_ckpt"] + # noinspection PyProtectedMember + obj._init( + waveform=waveform, + samplerate=samplerate, + f0=f0, + hop_size=hop_size, + fft_size=fft_size, + win_size=win_size, + model_path=hnsep_ckpt, + device=device, + ) + else: + raise ValueError(f" [x] Unknown harmonic-noise separator: {algorithm}") + return obj + + @property + def samplerate(self): + raise NotImplementedError() + + @property + def hop_size(self): + raise NotImplementedError() + + @property + def fft_size(self): + raise NotImplementedError() + + @property + def win_size(self): + raise NotImplementedError() + + def harmonic(self, k: int = None) -> np.ndarray: + raise NotImplementedError() + + def aperiodic(self) -> np.ndarray: + raise NotImplementedError() + + +class DecomposedWaveformPyWorld(DecomposedWaveform): + def _init( + self, + waveform, + samplerate, + f0, # basic parameters + *, + hop_size=None, + fft_size=None, + win_size=None, + base_harmonic_radius=3.5, # analysis parameters + device=None # computation parameters + ): + # the source components + self._waveform = waveform + self._samplerate = samplerate + self._f0 = f0 + # extraction parameters + self._hop_size = hop_size + self._fft_size = fft_size if fft_size is not None else win_size + self._win_size = win_size if win_size is not None else win_size + self._time_step = hop_size / samplerate + self._half_width = base_harmonic_radius + self._device = ("cuda" if torch.cuda.is_available() else "cpu") if device is None else device + # intermediate variables + self._f0_world = None + self._sp = None + self._ap = None + # final components + self._harmonic_part: np.ndarray = None + self._aperiodic_part: np.ndarray = None + self._harmonics: Dict[int, np.ndarray] = {} + + @property + def samplerate(self): + return self._samplerate + + @property + def hop_size(self): + return self._hop_size + + @property + def fft_size(self): + return self._fft_size + + @property + def win_size(self): + return self._win_size + + def _world_extraction(self): + # Add a tiny noise to the signal to avoid NaN results of D4C in rare edge cases + # References: + # - https://github.com/JeremyCCHsu/Python-Wrapper-for-World-Vocoder/issues/50 + # - https://github.com/mmorise/World/issues/116 + x = self._waveform.astype(np.double) + np.random.randn(*self._waveform.shape) * 1e-5 + samplerate = self._samplerate + f0 = self._f0.astype(np.double) + + hop_size = self._hop_size + fft_size = self._fft_size + + wav_frames = (x.shape[0] + hop_size - 1) // hop_size + f0_frames = f0.shape[0] + if f0_frames < wav_frames: + f0 = np.pad(f0, (0, wav_frames - f0_frames), mode="constant", constant_values=(f0[0], f0[-1])) + elif f0_frames > wav_frames: + f0 = f0[:wav_frames] + + time_step = hop_size / samplerate + t = np.arange(0, wav_frames) * time_step + self._f0_world = f0 + self._sp = pw.cheaptrick(x, f0, t, samplerate, fft_size=fft_size) # extract smoothed spectrogram + self._ap = pw.d4c(x, f0, t, samplerate, fft_size=fft_size) # extract aperiodicity + + def _kth_harmonic(self, k: int) -> np.ndarray: + """ + Extract the Kth harmonic (starting from 0) from the waveform. Author: @yxlllc + :param k: a non-negative integer + :return: kth_harmonic float32[T] + """ + if k in self._harmonics: + return self._harmonics[k] + + hop_size = self._hop_size + win_size = self._win_size + samplerate = self._samplerate + half_width = self._half_width + device = self._device + + waveform = torch.from_numpy(self.harmonic()).unsqueeze(0).to(device) # [B, n_samples] + n_samples = waveform.shape[1] + f0 = self._f0 * (k + 1) + pad_size = int(n_samples // hop_size) - len(f0) + 1 + if pad_size > 0: + f0 = np.pad(f0, (0, pad_size), mode="constant", constant_values=(f0[0], f0[-1])) + + f0, _ = interp_f0(f0, uv=f0 == 0) + f0 = torch.from_numpy(f0).to(device)[None, :, None] # [B, n_frames, 1] + n_f0_frames = f0.shape[1] + + phase = torch.arange(win_size, dtype=waveform.dtype, device=device) / win_size * 2 * np.pi + nuttall_window = ( + 0.355768 - 0.487396 * torch.cos(phase) + 0.144232 * torch.cos(2 * phase) - 0.012604 * torch.cos(3 * phase) + ) + spec = torch.stft( + waveform, + n_fft=win_size, + win_length=win_size, + hop_length=hop_size, + window=nuttall_window, + center=True, + return_complex=True, + ).permute( + 0, 2, 1 + ) # [B, n_frames, n_spec] + n_spec_frames, n_specs = spec.shape[1:] + idx = torch.arange(n_specs).unsqueeze(0).unsqueeze(0).to(f0) # [1, 1, n_spec] + center = f0 * win_size / samplerate + start = torch.clip(center - half_width, min=0) + end = torch.clip(center + half_width, max=n_specs) + idx_mask = (center >= 1) & (idx >= start) & (idx < end) # [B, n_frames, n_spec] + if n_f0_frames < n_spec_frames: + idx_mask = F.pad(idx_mask, [0, 0, 0, n_spec_frames - n_f0_frames]) + spec = spec * idx_mask[:, :n_spec_frames, :] + self._harmonics[k] = ( + torch.istft( + spec.permute(0, 2, 1), + n_fft=win_size, + win_length=win_size, + hop_length=hop_size, + window=nuttall_window, + center=True, + length=n_samples, + ) + .squeeze(0) + .cpu() + .numpy() + ) + + return self._harmonics[k] + + def harmonic(self, k: int = None) -> np.ndarray: + """ + Extract the full harmonic part, or the Kth harmonic if `k` is not None, from the waveform. + :param k: an integer representing the harmonic index, starting from 0 + :return: full_harmonics float32[T] or kth_harmonic float32[T] + """ + if k is not None: + return self._kth_harmonic(k) + if self._harmonic_part is not None: + return self._harmonic_part + if self._sp is None or self._ap is None: + self._world_extraction() + # noinspection PyAttributeOutsideInit + self._harmonic_part = pw.synthesize( + self._f0_world, + np.clip(self._sp * (1 - self._ap * self._ap), a_min=1e-16, a_max=None), # clip to avoid zeros + np.zeros_like(self._ap), + self._samplerate, + frame_period=self._time_step * 1000, + ).astype( + np.float32 + ) # synthesize the harmonic part using the parameters + return self._harmonic_part + + def aperiodic(self) -> np.ndarray: + """ + Extract the aperiodic part from the waveform. + :return: aperiodic_part float32[T] + """ + if self._aperiodic_part is not None: + return self._aperiodic_part + if self._sp is None or self._ap is None: + self._world_extraction() + # noinspection PyAttributeOutsideInit + self._aperiodic_part = pw.synthesize( + self._f0_world, + self._sp * self._ap * self._ap, + np.ones_like(self._ap), + self._samplerate, + frame_period=self._time_step * 1000, + ).astype( + np.float32 + ) # synthesize the aperiodic part using the parameters + return self._aperiodic_part + + +SEP_MODEL = None + + +class DecomposedWaveformVocalRemover(DecomposedWaveformPyWorld): + def _init( + self, + waveform, + samplerate, + f0, + hop_size=None, + fft_size=None, + win_size=None, + base_harmonic_radius=3.5, + model_path=None, + device=None, + ): + super()._init( + waveform, + samplerate, + f0, + hop_size=hop_size, + fft_size=fft_size, + win_size=win_size, + base_harmonic_radius=base_harmonic_radius, + device=device, + ) + global SEP_MODEL + if SEP_MODEL is None: + SEP_MODEL = load_sep_model(model_path, self._device) + self.sep_model = SEP_MODEL + + def _infer(self): + with torch.no_grad(): + x = torch.from_numpy(self._waveform).to(self._device).reshape(1, 1, -1) + if not self.sep_model.is_mono: + x = x.repeat(1, 2, 1) + x = self.sep_model.predict_from_audio(x) + x = torch.mean(x, dim=1) + self._harmonic_part = x.squeeze().cpu().numpy() + self._aperiodic_part = self._waveform - self._harmonic_part + + def harmonic(self, k: int = None) -> np.ndarray: + """ + Extract the full harmonic part, or the Kth harmonic if `k` is not None, from the waveform. + :param k: an integer representing the harmonic index, starting from 0 + :return: full_harmonics float32[T] or kth_harmonic float32[T] + """ + if k is not None: + return self._kth_harmonic(k) + if self._harmonic_part is not None: + return self._harmonic_part + self._infer() + return self._harmonic_part + + def aperiodic(self) -> np.ndarray: + """ + Extract the aperiodic part from the waveform. + :return: aperiodic_part float32[T] + """ + if self._aperiodic_part is not None: + return self._aperiodic_part + self._infer() + return self._aperiodic_part diff --git a/paddlemix/models/diffsinger/utils/hparams.py b/paddlemix/models/diffsinger/utils/hparams.py new file mode 100644 index 000000000..00636155f --- /dev/null +++ b/paddlemix/models/diffsinger/utils/hparams.py @@ -0,0 +1,153 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import yaml + + +global_print_hparams = True +hparams = {} + + +class Args: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + self.__setattr__(k, v) + + +def override_config(old_config: dict, new_config: dict): + for k, v in new_config.items(): + if isinstance(v, dict) and k in old_config: + override_config(old_config[k], new_config[k]) + else: + old_config[k] = v + + +def set_hparams(config="", exp_name="", hparams_str="", print_hparams=True, global_hparams=True): + """ + Load hparams from multiple sources: + 1. config chain (i.e. first load base_config, then load config); + 2. if reset == True, load from the (auto-saved) complete config file ('config.yaml') + which contains all settings and do not rely on base_config; + 3. load from argument --hparams or hparams_str, as temporary modification. + """ + if config == "": + parser = argparse.ArgumentParser(description="neural music") + parser.add_argument("-c", "--config", type=str, default="", help="location of the data corpus") + parser.add_argument("-e", "--exp_name", type=str, default="", help="exp_name") + parser.add_argument("-hp", "--hparams", type=str, default="", help="location of the data corpus") + parser.add_argument("-i", "--infer", action="store_true", help="infer") + parser.add_argument("-r", "--reset", action="store_true", help="reset hparams") + args, unknown = parser.parse_known_args() + + tmp_args_hparams = args.hparams.split(",") if args.hparams.strip() != "" else [] + tmp_args_hparams.extend(hparams_str.split(",") if hparams_str.strip() != "" else []) + args.hparams = ",".join(tmp_args_hparams) + else: + args = Args(config=config, exp_name=exp_name, hparams=hparams_str, infer=False, reset=False) + + args_work_dir = "" + if args.exp_name != "": + args.work_dir = args.exp_name + args_work_dir = os.path.join("checkpoint", args.work_dir) + + config_chains = [] + loaded_config = set() + + def load_config(config_fn): # deep first + with open(config_fn, encoding="utf-8") as f: + hparams_ = yaml.safe_load(f) + loaded_config.add(config_fn) + if "base_config" in hparams_: + ret_hparams = {} + if not isinstance(hparams_["base_config"], list): + hparams_["base_config"] = [hparams_["base_config"]] + for c in hparams_["base_config"]: + if c not in loaded_config: + if c.startswith("."): + c = f"{os.path.dirname(config_fn)}/{c}" + c = os.path.normpath(c) + override_config(ret_hparams, load_config(c)) + override_config(ret_hparams, hparams_) + else: + ret_hparams = hparams_ + config_chains.append(config_fn) + return ret_hparams + + global hparams + assert args.config != "" or args_work_dir != "", "Either config or exp name should be specified." + saved_hparams = {} + ckpt_config_path = os.path.join(args_work_dir, "config.yaml") + if args_work_dir != "" and os.path.exists(ckpt_config_path): + with open(ckpt_config_path, encoding="utf-8") as f: + saved_hparams.update(yaml.safe_load(f)) + + hparams_ = {} + if args.config != "": + hparams_.update(load_config(args.config)) + + if not args.reset: + hparams_.update(saved_hparams) + hparams_["work_dir"] = args_work_dir + + if args.hparams != "": + for new_hparam in args.hparams.split(","): + if new_hparam.strip() == "": + continue + k, v = new_hparam.split("=") + if k not in hparams_: + hparams_[k] = eval(v) + if v in ["True", "False"] or type(hparams_[k]) == bool: + hparams_[k] = eval(v) + else: + hparams_[k] = type(hparams_[k])(v) + + # @rank_zero_only + def dump_hparams(): + if args_work_dir != "" and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer: + os.makedirs(hparams_["work_dir"], exist_ok=True) + if True: + # Only the main process will save the config file + with open(ckpt_config_path, "w", encoding="utf-8") as f: + hparams_non_recursive = hparams_.copy() + hparams_non_recursive["base_config"] = [] + yaml.safe_dump(hparams_non_recursive, f, allow_unicode=True, encoding="utf-8") + + dump_hparams() + + hparams_["infer"] = args.infer + if global_hparams: + hparams.clear() + hparams.update(hparams_) + + if hparams.get("exp_name") is None: + hparams["exp_name"] = args.exp_name + if hparams_.get("exp_name") is None: + hparams_["exp_name"] = args.exp_name + + # @rank_zero_only + def print_out_hparams(): + global global_print_hparams + if True and print_hparams and global_print_hparams and global_hparams: + print("| Hparams chains: ", config_chains) + print("| Hparams: ") + for i, (k, v) in enumerate(sorted(hparams_.items())): + print(f"\033[0;33m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "") + print("") + global_print_hparams = False + + print_out_hparams() + + return hparams_ diff --git a/paddlemix/models/diffsinger/utils/indexed_datasets.py b/paddlemix/models/diffsinger/utils/indexed_datasets.py new file mode 100644 index 000000000..b3df10076 --- /dev/null +++ b/paddlemix/models/diffsinger/utils/indexed_datasets.py @@ -0,0 +1,109 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import multiprocessing +import pathlib +from collections import deque + +import h5py +import numpy as np +import paddle + + +class IndexedDataset: + def __init__(self, path, prefix, num_cache=0): + super().__init__() + self.path = pathlib.Path(path) / f"{prefix}.data" + if not self.path.exists(): + raise FileNotFoundError(f"IndexedDataset not found: {self.path}") + self.dset = None + self.cache = deque(maxlen=num_cache) + self.num_cache = num_cache + + def check_index(self, i): + if i < 0 or i >= len(self.dset): + raise IndexError("index out of range") + + def __del__(self): + if self.dset: + self.dset.close() + + def __getitem__(self, i): + if self.dset is None: + self.dset = h5py.File(self.path, "r") + self.check_index(i) + if self.num_cache > 0: + for c in self.cache: + if c[0] == i: + return c[1] + item = { + k: (v[()].item() if tuple(v.shape) == () else paddle.to_tensor(data=v[()])) + for k, v in self.dset[str(i)].items() + } + if self.num_cache > 0: + self.cache.appendleft((i, item)) + return item + + def __len__(self): + if self.dset is None: + self.dset = h5py.File(self.path, "r") + return len(self.dset) + + +class IndexedDatasetBuilder: + def __init__(self, path, prefix, allowed_attr=None, auto_increment=True): + self.path = pathlib.Path(path) / f"{prefix}.data" + self.prefix = prefix + self.dset = h5py.File(self.path, "w") + self.counter = 0 + self.auto_increment = auto_increment + if allowed_attr is not None: + self.allowed_attr = set(allowed_attr) + else: + self.allowed_attr = None + + def add_item(self, item, item_no=None): + if self.auto_increment and item_no is not None or not self.auto_increment and item_no is None: + raise ValueError("auto_increment and provided item_no are mutually exclusive") + if self.allowed_attr is not None: + item = {k: item[k] for k in self.allowed_attr if k in item} + if self.auto_increment: + item_no = self.counter + self.counter += 1 + for k, v in item.items(): + if v is None: + continue + self.dset.create_dataset(f"{item_no}/{k}", data=v) + return item_no + + def finalize(self): + self.dset.close() + + +if __name__ == "__main__": + import random + + from tqdm import tqdm + + ds_path = "./checkpoints/indexed_ds_example" + size = 100 + items = [{"a": np.random.normal(size=[10000, 10]), "b": np.random.normal(size=[10000, 10])} for i in range(size)] + builder = IndexedDatasetBuilder(ds_path, "example") + for i in tqdm(range(size)): + builder.add_item(items[i]) + builder.finalize() + ds = IndexedDataset(ds_path, "example") + for i in tqdm(range(10000)): + idx = random.randint(0, size - 1) + assert (ds[idx]["a"] == items[idx]["a"]).astype("bool").all() diff --git a/paddlemix/models/diffsinger/utils/infer_utils.py b/paddlemix/models/diffsinger/utils/infer_utils.py new file mode 100644 index 000000000..de2365ba0 --- /dev/null +++ b/paddlemix/models/diffsinger/utils/infer_utils.py @@ -0,0 +1,115 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re + +import librosa +import numpy as np +from scipy.io import wavfile + + +def trans_f0_seq(feature_pit, transform): + feature_pit = feature_pit * 2 ** (transform / 12) + return round(feature_pit, 1) + + +def trans_key(raw_data, key): + warning_tag = False + for i in raw_data: + note_seq_list = i["note_seq"].split(" ") + new_note_seq_list = [] + for note_seq in note_seq_list: + if note_seq != "rest": + new_note_seq = librosa.midi_to_note(librosa.note_to_midi(note_seq) + key, unicode=False) + new_note_seq_list.append(new_note_seq) + else: + new_note_seq_list.append(note_seq) + i["note_seq"] = " ".join(new_note_seq_list) + if i.get("f0_seq"): + f0_seq_list = i["f0_seq"].split(" ") + f0_seq_list = [float(x) for x in f0_seq_list] + new_f0_seq_list = [] + for f0_seq in f0_seq_list: + new_f0_seq = trans_f0_seq(f0_seq, key) + new_f0_seq_list.append(str(new_f0_seq)) + i["f0_seq"] = " ".join(new_f0_seq_list) + else: + warning_tag = True + if warning_tag: + print("Warning: parts of f0_seq do not exist, please freeze the pitch line in the editor.\r\n") + return raw_data + + +def resample_align_curve(points: np.ndarray, original_timestep: float, target_timestep: float, align_length: int): + t_max = (len(points) - 1) * original_timestep + curve_interp = np.interp( + np.arange(0, t_max, target_timestep), original_timestep * np.arange(len(points)), points + ).astype(points.dtype) + delta_l = align_length - len(curve_interp) + if delta_l < 0: + curve_interp = curve_interp[:align_length] + elif delta_l > 0: + curve_interp = np.concatenate((curve_interp, np.full(delta_l, fill_value=curve_interp[-1])), axis=0) + return curve_interp + + +def parse_commandline_spk_mix(mix: str) -> dict: + """ + Parse speaker mix info from commandline + :param mix: Input like "opencpop" or "opencpop|qixuan" or "opencpop:0.5|qixuan:0.5" + :return: A dict whose keys are speaker names and values are proportions + """ + name_pattern = "[0-9A-Za-z_-]+" + proportion_pattern = "\\d+(\\.\\d+)?" + single_pattern = f"{name_pattern}(:{proportion_pattern})?" + assert re.fullmatch(f"{single_pattern}(\\|{single_pattern})*", mix) is not None, f"Invalid mix pattern: {mix}" + without_proportion = set() + proportion_map = {} + for component in mix.split("|"): + name_and_proportion = component.split(":") + assert ( + name_and_proportion[0] not in without_proportion and name_and_proportion[0] not in proportion_map + ), f"Duplicate speaker name: {name_and_proportion[0]}" + if ":" in component: + proportion_map[name_and_proportion[0]] = float(name_and_proportion[1]) + else: + without_proportion.add(name_and_proportion[0]) + sum_given_proportions = sum(proportion_map.values()) + assert ( + sum_given_proportions < 1 or len(without_proportion) == 0 + ), "Proportion of all speakers should be specified if the sum of all given proportions are larger than 1." + for name in without_proportion: + proportion_map[name] = (1 - sum_given_proportions) / len(without_proportion) + sum_all_proportions = sum(proportion_map.values()) + assert sum_all_proportions > 0, "Sum of all proportions should be positive." + for name in proportion_map: + proportion_map[name] /= sum_all_proportions + return proportion_map + + +def cross_fade(a: np.ndarray, b: np.ndarray, idx: int): + result = np.zeros(idx + b.shape[0]) + fade_len = a.shape[0] - idx + np.copyto(dst=result[:idx], src=a[:idx]) + k = np.linspace(0, 1.0, num=fade_len, endpoint=True) + result[idx : a.shape[0]] = (1 - k) * a[idx:] + k * b[:fade_len] + np.copyto(dst=result[a.shape[0] :], src=b[fade_len:]) + return result + + +def save_wav(wav, path, sr, norm=False): + if norm: + wav = wav / np.abs(wav).max() + wav *= 32767 + wavfile.write(path, sr, wav.astype(np.int16)) diff --git a/paddlemix/models/diffsinger/utils/multiprocess_utils.py b/paddlemix/models/diffsinger/utils/multiprocess_utils.py new file mode 100644 index 000000000..585ba301e --- /dev/null +++ b/paddlemix/models/diffsinger/utils/multiprocess_utils.py @@ -0,0 +1,63 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import platform +import traceback + +is_main_process = True + + +def main_process_print(self, *args, sep=" ", end="\n", file=None): + if is_main_process: + print(self, *args, sep=sep, end=end, file=file) + + +def chunked_worker_run(map_func, args, results_queue=None): + for a in args: + # noinspection PyBroadException + try: + res = map_func(*a) + results_queue.put(res) + except KeyboardInterrupt: + break + except Exception: + traceback.print_exc() + results_queue.put(None) + + +def chunked_multiprocess_run(map_func, args, num_workers, q_max_size=1000): + num_jobs = len(args) + if num_jobs < num_workers: + num_workers = num_jobs + + queues = [Manager().Queue(maxsize=q_max_size // num_workers) for _ in range(num_workers)] + if platform.system().lower() != "windows": + process_creation_func = get_context("spawn").Process + else: + process_creation_func = Process + + workers = [] + for i in range(num_workers): + worker = process_creation_func( + target=chunked_worker_run, args=(map_func, args[i::num_workers], queues[i]), daemon=True + ) + workers.append(worker) + worker.start() + + for i in range(num_jobs): + yield queues[i % num_workers].get() + + for worker in workers: + worker.join() + worker.close() diff --git a/paddlemix/models/diffsinger/utils/paddle_aux.py b/paddlemix/models/diffsinger/utils/paddle_aux.py new file mode 100644 index 000000000..166fb905d --- /dev/null +++ b/paddlemix/models/diffsinger/utils/paddle_aux.py @@ -0,0 +1,215 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This file is generated by PaConvert ToolKit, please Don't edit it! +import paddle + + +def split_tensor_func(self, split_size, dim=0): + if isinstance(split_size, int): + return paddle.split(self, self.shape[dim] // split_size, dim) + else: + return paddle.split(self, split_size, dim) + + +setattr(paddle.Tensor, "split", split_tensor_func) + + +def reshape(self, *args, **kwargs): + if args: + if len(args) == 1 and isinstance(args[0], (tuple, list)): + return paddle.reshape(self, args[0]) + else: + return paddle.reshape(self, list(args)) + elif kwargs: + assert "shape" in kwargs + return paddle.reshape(self, shape=kwargs["shape"]) + + +setattr(paddle.Tensor, "reshape", reshape) + + +def add(self, *args, **kwargs): + if "other" in kwargs: + y = kwargs["other"] + elif "y" in kwargs: + y = kwargs["y"] + else: + y = args[0] + + if "alpha" in kwargs: + alpha = kwargs["alpha"] + if alpha != 1: + if not isinstance(y, paddle.Tensor): + y = paddle.to_tensor(alpha * y) + else: + y = alpha * y + else: + if not isinstance(y, paddle.Tensor): + y = paddle.to_tensor(y) + + return paddle.add(self, y) + + +setattr(paddle.Tensor, "add", add) + + +def min(*args, **kwargs): + if "input" in kwargs: + kwargs["x"] = kwargs.pop("input") + + out_v = None + if "out" in kwargs: + out_v = kwargs.pop("out") + + if "other" in kwargs: + kwargs["y"] = kwargs.pop("other") + ret = paddle.minimum(*args, **kwargs) + elif len(args) == 2 and isinstance(args[1], paddle.Tensor): + ret = paddle.minimum(*args, **kwargs) + else: + if "dim" in kwargs: + kwargs["axis"] = kwargs.pop("dim") + + if "axis" in kwargs or len(args) >= 2: + if out_v: + ret = paddle.min(*args, **kwargs), paddle.argmin(*args, **kwargs) + paddle.assign(ret[0], out_v[0]) + paddle.assign(ret[1], out_v[1]) + return out_v + else: + ret = paddle.min(*args, **kwargs), paddle.argmin(*args, **kwargs) + return ret + else: + ret = paddle.min(*args, **kwargs) + return ret + + if out_v: + paddle.assign(ret, out_v) + return out_v + else: + return ret + + +def max(*args, **kwargs): + if "input" in kwargs: + kwargs["x"] = kwargs.pop("input") + + out_v = None + if "out" in kwargs: + out_v = kwargs.pop("out") + + if "other" in kwargs: + kwargs["y"] = kwargs.pop("other") + ret = paddle.maximum(*args, **kwargs) + elif len(args) == 2 and isinstance(args[1], paddle.Tensor): + ret = paddle.maximum(*args, **kwargs) + else: + if "dim" in kwargs: + kwargs["axis"] = kwargs.pop("dim") + + if "axis" in kwargs or len(args) >= 2: + if out_v: + ret = paddle.max(*args, **kwargs), paddle.argmax(*args, **kwargs) + paddle.assign(ret[0], out_v[0]) + paddle.assign(ret[1], out_v[1]) + return out_v + else: + ret = paddle.max(*args, **kwargs), paddle.argmax(*args, **kwargs) + return ret + return out_v + else: + ret = paddle.max(*args, **kwargs) + return ret + + if out_v: + paddle.assign(ret, out_v) + return out_v + else: + return ret + + +def min_class_func(self, *args, **kwargs): + if "other" in kwargs: + kwargs["y"] = kwargs.pop("other") + ret = paddle.minimum(self, *args, **kwargs) + elif len(args) == 1 and isinstance(args[0], paddle.Tensor): + ret = paddle.minimum(self, *args, **kwargs) + else: + if "dim" in kwargs: + kwargs["axis"] = kwargs.pop("dim") + + if "axis" in kwargs or len(args) >= 1: + ret = paddle.min(self, *args, **kwargs), paddle.argmin(self, *args, **kwargs) + else: + ret = paddle.min(self, *args, **kwargs) + + return ret + + +def max_class_func(self, *args, **kwargs): + if "other" in kwargs: + kwargs["y"] = kwargs.pop("other") + ret = paddle.maximum(self, *args, **kwargs) + elif len(args) == 1 and isinstance(args[0], paddle.Tensor): + ret = paddle.maximum(self, *args, **kwargs) + else: + if "dim" in kwargs: + kwargs["axis"] = kwargs.pop("dim") + + if "axis" in kwargs or len(args) >= 1: + ret = paddle.max(self, *args, **kwargs), paddle.argmax(self, *args, **kwargs) + else: + ret = paddle.max(self, *args, **kwargs) + + return ret + + +setattr(paddle.Tensor, "min", min_class_func) +setattr(paddle.Tensor, "max", max_class_func) + + +def view(self, *args, **kwargs): + if args: + if len(args) == 1 and isinstance(args[0], (tuple, list, str)): + return paddle.view(self, args[0]) + else: + return paddle.view(self, list(args)) + elif kwargs: + return paddle.view(self, shape_or_dtype=list(kwargs.values())[0]) + + +setattr(paddle.Tensor, "view", view) + + +def transpose_aux_func(dims, dim0, dim1): + perm = list(range(dims)) + perm[dim0], perm[dim1] = perm[dim1], perm[dim0] + return perm + + +def split(x, num_or_sections, axis=0): + if isinstance(num_or_sections, int): + return paddle.split(x, x.shape[axis] // num_or_sections, axis) + else: + return paddle.split(x, num_or_sections, axis) + + +class Embedding(paddle.nn.Embedding): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.padding_idx = self._padding_idx + + +setattr(paddle.nn, "Embedding", Embedding) diff --git a/paddlemix/models/diffsinger/utils/phoneme_utils.py b/paddlemix/models/diffsinger/utils/phoneme_utils.py new file mode 100644 index 000000000..e8ae4242a --- /dev/null +++ b/paddlemix/models/diffsinger/utils/phoneme_utils.py @@ -0,0 +1,102 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pathlib +from paddlemix.models.diffsinger.utils.hparams import hparams + +_initialized = False +_ALL_CONSONANTS_SET = set() +_ALL_VOWELS_SET = set() +_dictionary = {"AP": ["AP"], "SP": ["SP"]} +_phoneme_list: list + + +def locate_dictionary(): + """ + Search and locate the dictionary file. + Order: + 1. hparams['dictionary'] + 2. hparams['g2p_dictionary'] + 3. 'dictionary.txt' in hparams['work_dir'] + 4. file with same name as hparams['g2p_dictionary'] in hparams['work_dir'] + :return: pathlib.Path of the dictionary file + """ + assert "dictionary" in hparams or "g2p_dictionary" in hparams, "Please specify a dictionary file in your config." + config_dict_path = pathlib.Path(hparams["dictionary"]) + if config_dict_path.exists(): + return config_dict_path + work_dir = pathlib.Path(hparams["work_dir"]) + ckpt_dict_path = work_dir / config_dict_path.name + if ckpt_dict_path.exists(): + return ckpt_dict_path + ckpt_dict_path = work_dir / "dictionary.txt" + if ckpt_dict_path.exists(): + return ckpt_dict_path + raise FileNotFoundError( + "Unable to locate the dictionary file. Please specify the right dictionary in your config." + ) + + +def _build_dict_and_list(): + global _dictionary, _phoneme_list + _set = set() + with open(locate_dictionary(), "r", encoding="utf8") as _df: + _lines = _df.readlines() + for _line in _lines: + _pinyin, _ph_str = _line.strip().split("\t") + _dictionary[_pinyin] = _ph_str.split() + for _list in _dictionary.values(): + [_set.add(ph) for ph in _list] + _phoneme_list = sorted(list(_set)) + print("| load phoneme set: " + str(_phoneme_list)) + + +def _initialize_consonants_and_vowels(): + for _ph_list in _dictionary.values(): + _ph_count = len(_ph_list) + if _ph_count == 0 or _ph_list[0] in ["AP", "SP"]: + continue + elif len(_ph_list) == 1: + _ALL_VOWELS_SET.add(_ph_list[0]) + else: + _ALL_CONSONANTS_SET.add(_ph_list[0]) + _ALL_VOWELS_SET.add(_ph_list[1]) + + +def _initialize(): + global _initialized + if not _initialized: + _build_dict_and_list() + _initialize_consonants_and_vowels() + _initialized = True + + +def get_all_consonants(): + _initialize() + return sorted(_ALL_CONSONANTS_SET) + + +def get_all_vowels(): + _initialize() + return sorted(_ALL_VOWELS_SET) + + +def build_dictionary() -> dict: + _initialize() + return _dictionary + + +def build_phoneme_list() -> list: + _initialize() + return _phoneme_list diff --git a/paddlemix/models/diffsinger/utils/pitch_utils.py b/paddlemix/models/diffsinger/utils/pitch_utils.py new file mode 100644 index 000000000..f7036ec5f --- /dev/null +++ b/paddlemix/models/diffsinger/utils/pitch_utils.py @@ -0,0 +1,41 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np + + +def norm_f0(f0, uv=None): + if uv is None: + uv = f0 == 0 + f0 = np.log2(f0 + uv) + f0[uv] = -np.inf + return f0 + + +def interp_f0(f0, uv=None): + if uv is None: + uv = f0 == 0 + f0 = norm_f0(f0, uv) + if uv.any() and not uv.all(): + f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) + return denorm_f0(f0, uv=None), uv + + +def denorm_f0(f0, uv, pitch_padding=None): + f0 = 2**f0 + if uv is not None: + f0[uv > 0] = 0 + if pitch_padding is not None: + f0[pitch_padding] = 0 + return f0 diff --git a/paddlemix/models/diffsinger/utils/plot.py b/paddlemix/models/diffsinger/utils/plot.py new file mode 100644 index 000000000..48590bd1e --- /dev/null +++ b/paddlemix/models/diffsinger/utils/plot.py @@ -0,0 +1,145 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import matplotlib.pyplot as plt +import numpy as np +import paddle +from matplotlib.ticker import MultipleLocator + + +def spec_to_figure(spec, vmin=None, vmax=None, title=None): + if isinstance(spec, paddle.Tensor): + spec = spec.cpu().numpy() + fig = plt.figure(figsize=(12, 9)) + plt.pcolor(spec.T, vmin=vmin, vmax=vmax) + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def dur_to_figure(dur_gt, dur_pred, txt, title=None): + if isinstance(dur_gt, paddle.Tensor): + dur_gt = dur_gt.cpu().numpy() + if isinstance(dur_pred, paddle.Tensor): + dur_pred = dur_pred.cpu().numpy() + dur_gt = dur_gt.astype(np.int64) + dur_pred = dur_pred.astype(np.int64) + dur_gt = np.cumsum(dur_gt) + dur_pred = np.cumsum(dur_pred) + width = max(12, min(48, len(txt) // 2)) + fig = plt.figure(figsize=(width, 8)) + plt.vlines(dur_pred, 12, 22, colors="r", label="pred") + plt.vlines(dur_gt, 0, 10, colors="b", label="gt") + for i in range(len(txt)): + shift = i % 8 + 1 + plt.text( + (dur_pred[i - 1] + dur_pred[i]) / 2 if i > 0 else dur_pred[i] / 2, + 12 + shift, + txt[i], + size=16, + horizontalalignment="center", + ) + plt.text( + (dur_gt[i - 1] + dur_gt[i]) / 2 if i > 0 else dur_gt[i] / 2, + shift, + txt[i], + size=16, + horizontalalignment="center", + ) + plt.plot([dur_pred[i], dur_gt[i]], [12, 10], color="black", linewidth=2, linestyle=":") + plt.yticks([]) + plt.xlim(0, max(dur_pred[-1], dur_gt[-1])) + plt.legend() + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def pitch_note_to_figure(pitch_gt, pitch_pred=None, note_midi=None, note_dur=None, note_rest=None, title=None): + if isinstance(pitch_gt, paddle.Tensor): + pitch_gt = pitch_gt.cpu().numpy() + if isinstance(pitch_pred, paddle.Tensor): + pitch_pred = pitch_pred.cpu().numpy() + if isinstance(note_midi, paddle.Tensor): + note_midi = note_midi.cpu().numpy() + if isinstance(note_dur, paddle.Tensor): + note_dur = note_dur.cpu().numpy() + if isinstance(note_rest, paddle.Tensor): + note_rest = note_rest.cpu().numpy() + fig = plt.figure() + if note_midi is not None and note_dur is not None: + note_dur_acc = np.cumsum(note_dur) + if note_rest is None: + note_rest = np.zeros_like(note_midi, dtype=np.bool_) + for i in range(len(note_midi)): + plt.gca().add_patch( + plt.Rectangle( + xy=(note_dur_acc[i - 1] if i > 0 else 0, note_midi[i] - 0.5), + width=note_dur[i], + height=1, + edgecolor="grey", + fill=False, + linewidth=1.5, + linestyle="--" if note_rest[i] else "-", + ) + ) + plt.plot(pitch_gt, color="b", label="gt") + if pitch_pred is not None: + plt.plot(pitch_pred, color="r", label="pred") + plt.gca().yaxis.set_major_locator(MultipleLocator(1)) + plt.grid(axis="y") + plt.legend() + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def curve_to_figure(curve_gt, curve_pred=None, curve_base=None, grid=None, title=None): + if isinstance(curve_gt, paddle.Tensor): + curve_gt = curve_gt.cpu().numpy() + if isinstance(curve_pred, paddle.Tensor): + curve_pred = curve_pred.cpu().numpy() + if isinstance(curve_base, paddle.Tensor): + curve_base = curve_base.cpu().numpy() + fig = plt.figure() + if curve_base is not None: + plt.plot(curve_base, color="g", label="base") + plt.plot(curve_gt, color="b", label="gt") + if curve_pred is not None: + plt.plot(curve_pred, color="r", label="pred") + if grid is not None: + plt.gca().yaxis.set_major_locator(MultipleLocator(grid)) + plt.grid(axis="y") + plt.legend() + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def distribution_to_figure(title, x_label, y_label, items: list, values: list, zoom=0.8): + fig = plt.figure(figsize=(int(len(items) * zoom), 10)) + plt.bar(x=items, height=values) + plt.tick_params(labelsize=15) + plt.xlim(-1, len(items)) + for a, b in zip(items, values): + plt.text(a, b, b, ha="center", va="bottom", fontsize=15) + plt.grid() + plt.title(title, fontsize=30) + plt.xlabel(x_label, fontsize=20) + plt.ylabel(y_label, fontsize=20) + return fig diff --git a/paddlemix/models/diffsinger/utils/text_encoder.py b/paddlemix/models/diffsinger/utils/text_encoder.py new file mode 100644 index 000000000..2d6dd8ba6 --- /dev/null +++ b/paddlemix/models/diffsinger/utils/text_encoder.py @@ -0,0 +1,64 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np + +PAD = "" +PAD_INDEX = 0 + + +class TokenTextEncoder: + """Encoder based on a user-supplied vocabulary (file or list).""" + + def __init__(self, vocab_list): + """Initialize from a file or list, one token per line. + + Handling of reserved tokens works as follows: + - When initializing from a list, we add reserved tokens to the vocab. + + Args: + vocab_list: If not None, a list of elements of the vocabulary. + """ + self.vocab_list = sorted(vocab_list) + + def encode(self, sentence): + """Converts a space-separated string of phones to a list of ids.""" + phones = sentence.strip().split() if isinstance(sentence, str) else sentence + return [(self.vocab_list.index(ph) + 1 if ph != PAD else PAD_INDEX) for ph in phones] + + def decode(self, ids, strip_padding=False): + if strip_padding: + ids = np.trim_zeros(ids) + ids = list(ids) + return " ".join([(self.vocab_list[_id - 1] if _id >= 1 else PAD) for _id in ids]) + + @property + def vocab_size(self): + return len(self.vocab_list) + 1 + + def __len__(self): + return self.vocab_size + + def store_to_file(self, filename): + """Write vocab file to disk. + + Vocab files have one token per line. The file ends in a newline. Reserved + tokens are written to the vocab file as well. + + Args: + filename: Full path of the file to store the vocab to. + """ + with open(filename, "w", encoding="utf8") as f: + print(PAD, file=f) + [print(tok, file=f) for tok in self.vocab_list]