diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..88887509 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,3 @@ +/results +/checkpoints +/dataset diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2ee2150e..02008f3b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,20 +14,10 @@ jobs: - uses: actions/setup-python@v4 with: python-version: "3.10" - - name: Install Dependencies - run: | - pip3 install --upgrade pip && \ - pip3 install pdm && \ - pdm sync - - name: Lint with black and isort - run: | - pdm run black . --check - pdm run isort . --check + - uses: pre-commit/action@v3.0.0 docs: runs-on: ubuntu-latest - # Only run this job if the lint job is successful and the current branch is main - needs: lint if: ${{ github.ref == 'refs/heads/main' }} steps: - uses: actions/checkout@v3 diff --git a/.gitignore b/.gitignore index 29802089..c3381c7b 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,4 @@ exp_*.sh exported pitches_editor .pdm-python +.pgx.* diff --git a/configs/_base_/trainers/base.py b/configs/_base_/trainers/base.py index 5a1ba2fa..cde3b1c7 100644 --- a/configs/_base_/trainers/base.py +++ b/configs/_base_/trainers/base.py @@ -13,9 +13,10 @@ log_every_n_steps=10, val_check_interval=5000, check_val_every_n_epoch=None, - max_steps=1_000_000, + max_steps=2_000_000, # Warning: If you are training the model with fs2 (and see nan), you should either use bf16 or fp32 - precision="16-mixed", + precision="bf16-mixed", + accumulate_grad_batches=1, callbacks=[ ModelCheckpoint( filename="{epoch}-{step}-{valid_loss:.4f}", @@ -34,5 +35,7 @@ trainer["strategy"] = DDPStrategy( process_group_backend=process_group_backend, gradient_as_bucket_view=True, + find_unused_parameters=True, + static_graph=True, ddp_comm_hook=default.fp16_compress_hook, ) diff --git a/configs/encodec_decoder.py b/configs/encodec_decoder.py new file mode 100644 index 00000000..ec6de990 --- /dev/null +++ b/configs/encodec_decoder.py @@ -0,0 +1,53 @@ +_base_ = [ + "./_base_/archs/diff_svc_v2.py", + "./_base_/trainers/base.py", + "./_base_/schedulers/warmup_cosine.py", + "./_base_/datasets/audio_folder.py", +] + +speaker_mapping = { + "default": 0, +} + +dataset = dict( + train=dict( + type="NaiveDenoiserDataset", + path="dataset/tts", + speaker_id=0, + ), + valid=dict( + type="NaiveDenoiserDataset", + path="dataset/tts/valid", + speaker_id=0, + ), +) + +model = dict( + text_encoder=dict( + type="NaiveProjectionEncoder", + input_size=128, + output_size=256, + ), + speaker_encoder=dict( + _delete_=True, + ), + pitch_encoder=dict( + _delete_=True, + ), + vocoder=dict( + _delete_=True, + type="ADaMoSHiFiGANV1", + use_natural_log=False, + checkpoint_path="checkpoints/adamos/convnext_hifigan_more_supervised_001560000.ckpt", + ), +) + +preprocessing = dict( + text_features_extractor=dict( + type="Encodec", + model="facebook/encodec_24khz", + first_codebook_only=True, + ), + pitch_extractor=None, + augmentations=[], +) diff --git a/configs/tts_baseline.py b/configs/tts_baseline.py new file mode 100644 index 00000000..ad342912 --- /dev/null +++ b/configs/tts_baseline.py @@ -0,0 +1,174 @@ +# Warning: This config is developing, and subject to change. + +from pathlib import Path + +from fish_diffusion.datasets.naive import NaiveTTSDataset +from fish_diffusion.schedulers.warmup_cosine_scheduler import ( + LambdaWarmUpCosineScheduler, +) + +_base_ = [ + "./_base_/trainers/base.py", + "./_base_/schedulers/warmup_cosine.py", + "./_base_/datasets/naive_svc.py", +] + +speakers = [] + +# Process SVC mixin datasets +mixin_datasets = [ + ("LibriTTS-100", "dataset/LibriTTS/train-clean-100"), + ("LibriTTS-360", "dataset/LibriTTS/train-clean-360"), + ("LibriTTS-500", "dataset/LibriTTS/train-other-500"), +] +train_datasets = [] + +for name, path in mixin_datasets: + for speaker_path in sorted(Path(path).iterdir()): + if not any(speaker_path.rglob("*.npy")): + continue + + speaker_name = f"{name}-{speaker_path.name}" + if speaker_name not in speakers: + speakers.append(speaker_name) + + train_datasets.append( + dict( + type="NaiveTTSDataset", + path=str(speaker_path), + speaker_id=speaker_name, + ) + ) + +# Sort speakers +speakers.sort() +speaker_mapping = {speaker: i for i, speaker in enumerate(speakers)} + +for dataset in train_datasets: + dataset["speaker_id"] = speaker_mapping[dataset["speaker_id"]] + +# Config model +sampling_rate = 44100 +mel_channels = 128 +# bert_dim = 768 +gradient_checkpointing = True + +model = dict( + type="GradTTS", + gradient_checkpointing=gradient_checkpointing, + diffusion=dict( + type="GaussianDiffusion", + mel_channels=mel_channels, + noise_schedule="linear", + timesteps=1000, + max_beta=0.01, + s=0.008, + noise_loss="l1", + denoiser=dict( + type="LlamaDenoiser", + bos_token_id=1, + eos_token_id=2, + hidden_act="silu", + hidden_size=768, + initializer_range=0.02, + intermediate_size=768 * 4, + max_position_embeddings=4096, + model_type="llama", + num_attention_heads=16, + num_hidden_layers=24, + num_key_value_heads=16, + rms_norm_eps=1e-05, + rope_scaling=None, + tie_word_embeddings=False, + vocab_size=32000, + ), + sampler_interval=10, + spec_min=[-5], + spec_max=[0], + ), + # speaker_encoder=dict( + # type="NaiveProjectionEncoder", + # input_size=10000, # len(speaker_mapping), + # output_size=bert_dim, + # use_embedding=True, + # ), + # text_encoder=dict( + # type="BertEncoder", + # model_name="bert-base-cased", + # pretrained=True, + # ), + # duration_predictor=dict( + # type="NaiveProjectionEncoder", + # input_size=bert_dim, + # output_size=1, + # ), + vocoder=dict( + type="ADaMoSHiFiGANV1", + use_natural_log=False, + checkpoint_path="checkpoints/adamos/convnext_hifigan_more_supervised_001560000.ckpt", + ), +) + +dataset = dict( + _delete_=True, + train=dict( + type="ConcatDataset", + datasets=train_datasets, + collate_fn=NaiveTTSDataset.collate_fn, + ), + valid=dict( + type="SampleDataset", + num_samples=8, + dataset=dict( + type="ConcatDataset", + datasets=train_datasets, + collate_fn=NaiveTTSDataset.collate_fn, + ), + collate_fn=NaiveTTSDataset.collate_fn, + ), +) + +dataloader = dict( + train=dict( + batch_size=4, + ), + valid=dict( + batch_size=8, + ), +) + +trainer = dict( + accumulate_grad_batches=4, + # strategy="ddp" +) + +preprocessing = dict( + text_features_extractor=dict( + type="LlamaTokenizer", + model_name="meta-llama/Llama-2-7b-hf", + label_suffix=".normalized.txt", + ), +) + +lambda_func = LambdaWarmUpCosineScheduler( + warm_up_steps=10000, + val_final=1e-5, + val_base=1e-4, + val_start=0, + max_decay_steps=300000, +) + +optimizer = dict( + _delete_=True, + type="AdamW", + lr=1.0, + weight_decay=1e-2, + betas=(0.9, 0.999), + eps=1e-6, +) + +scheduler = dict( + _delete_=True, + type="LambdaLR", + lr_lambda=lambda_func, +) diff --git a/fish_diffusion/archs/diffsinger/__init__.py b/fish_diffusion/archs/diffsinger/__init__.py index 069f6bfa..b87c03d1 100644 --- a/fish_diffusion/archs/diffsinger/__init__.py +++ b/fish_diffusion/archs/diffsinger/__init__.py @@ -1,3 +1,4 @@ from .diffsinger import DiffSinger, DiffSingerLightning +from .grad_tts import GradTTS -__all__ = ["DiffSingerLightning", "DiffSinger"] +__all__ = ["DiffSingerLightning", "DiffSinger", "GradTTS"] diff --git a/fish_diffusion/archs/diffsinger/diffsinger.py b/fish_diffusion/archs/diffsinger/diffsinger.py index b2ae4d72..ebdb07ae 100644 --- a/fish_diffusion/archs/diffsinger/diffsinger.py +++ b/fish_diffusion/archs/diffsinger/diffsinger.py @@ -4,10 +4,8 @@ import torch import torch.nn as nn import wandb -from loguru import logger from mmengine.optim import OPTIMIZERS from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger -from torch.nn import functional as F from fish_diffusion.modules.encoders import ENCODERS from fish_diffusion.modules.vocoders import VOCODERS @@ -16,6 +14,7 @@ from fish_diffusion.utils.viz import viz_synth_sample from .diffusions import DIFFUSIONS +from .grad_tts import GradTTS class DiffSinger(nn.Module): @@ -90,9 +89,13 @@ def forward_features( 1 - mel_masks[:, :, None].float() ) - if speakers.ndim in [2, 3] and torch.is_floating_point(speakers): + if ( + speakers is not None + and speakers.ndim in [2, 3] + and torch.is_floating_point(speakers) + ): speaker_embed = speakers - elif hasattr(self, "speaker_encoder"): + elif speakers is not None and hasattr(self, "speaker_encoder"): speaker_embed = self.speaker_encoder(speakers) else: speaker_embed = None @@ -125,8 +128,9 @@ def forward_features( return dict( features=features, - src_masks=src_masks, - mel_masks=mel_masks, + x_masks=mel_masks, + x_lens=mel_lens, + cond_masks=mel_masks, ) def forward( @@ -157,11 +161,20 @@ def forward( ) output_dict = self.diffusion.train_step( - features["features"], mel, features["mel_masks"] + features["features"], + mel, + x_masks=features["x_masks"], + cond_masks=features["cond_masks"], ) + if "loss" in features and features["loss"] is not None: + output_dict["loss"] = output_dict["loss"] + features["loss"] + # For validation output_dict["features"] = features["features"] + output_dict["x_masks"] = features["x_masks"] + output_dict["x_lens"] = features["x_lens"] + output_dict["cond_masks"] = features["cond_masks"] return output_dict @@ -170,7 +183,8 @@ class DiffSingerLightning(pl.LightningModule): def __init__(self, config): super().__init__() - self.model = DiffSinger(config.model) + model_fn = GradTTS if config.model.type == "GradTTS" else DiffSinger + self.model = model_fn(config.model) self.config = config self.ema_momentum = config.get("ema_momentum", None) self.lora = config.get("lora", False) @@ -180,7 +194,7 @@ def __init__(self, config): self.build_lora(self.model) if self.ema_momentum is not None: - self.ema_model = DiffSinger(config.model) + self.ema_model = model_fn(config.model) if self.lora: self.build_lora(self.ema_model) @@ -230,6 +244,9 @@ def configure_optimizers(self): } ) + if getattr(self.config, "scheduler", None) is None: + return optimizer + scheduler = LR_SCHEUDLERS.build( { "optimizer": optimizer, @@ -246,40 +263,72 @@ def _step(self, batch, batch_idx, mode): else self.model ) - assert batch["pitches"].shape[1] == batch["mel"].shape[1] + if "pitches" not in batch: + batch["pitches"] = pitches = None + else: + assert batch["pitches"].shape[1] == batch["mel"].shape[1] + pitches = batch["pitches"].clone() - pitches = batch["pitches"].clone() - batch_size = batch["speaker"].shape[0] + batch_size = batch["mel"].shape[0] output = model( - speakers=batch["speaker"], + speakers=batch.get("speaker", None), contents=batch["contents"], contents_lens=batch["contents_lens"], contents_max_len=batch["contents_max_len"], mel=batch["mel"], mel_lens=batch["mel_lens"], mel_max_len=batch["mel_max_len"], - pitches=batch["pitches"], + pitches=batch.get("pitches", None), pitch_shift=batch.get("key_shift", None), phones2mel=batch.get("phones2mel", None), energy=batch.get("energy", None), ) - self.log(f"{mode}_loss", output["loss"], batch_size=batch_size, sync_dist=True) + self.log( + f"{mode}_loss", + output["loss"], + batch_size=batch_size, + sync_dist=True, + prog_bar=True, + ) + + # If we need to log other metrics + for k, v in output.get("metrics", {}).items(): + self.log( + f"{mode}_{k}", + v, + batch_size=batch_size, + sync_dist=True, + ) if mode != "valid": return output["loss"] - x = model.diffusion(output["features"]) + x = model.diffusion( + output["features"], + x_masks=output["x_masks"], + cond_masks=output["cond_masks"], + ) - for idx, (gt_mel, gt_pitch, predict_mel, predict_mel_len) in enumerate( - zip(batch["mel"], pitches, x, batch["mel_lens"]) + if pitches is None: + pitches = [None] * batch_size + + for idx, ( + gt_mel, + gt_pitch, + predict_mel, + predict_mel_len, + gt_mel_len, + ) in enumerate( + zip(batch["mel"], pitches, x, output["x_lens"], batch["mel_lens"]) ): image_mels, wav_reconstruction, wav_prediction = viz_synth_sample( gt_mel=gt_mel, - gt_pitch=gt_pitch[:, 0], + gt_pitch=gt_pitch[:, 0] if gt_pitch is not None else None, predict_mel=predict_mel, predict_mel_len=predict_mel_len, + gt_mel_len=gt_mel_len, vocoder=self.vocoder, return_image=False, ) diff --git a/fish_diffusion/archs/diffsinger/diffusions/builder.py b/fish_diffusion/archs/diffsinger/diffusions/builder.py index 1206af84..1ae35a5d 100644 --- a/fish_diffusion/archs/diffsinger/diffusions/builder.py +++ b/fish_diffusion/archs/diffsinger/diffusions/builder.py @@ -1,6 +1,7 @@ from mmengine import Registry -from fish_diffusion.modules.convnext import ConvNext +from fish_diffusion.modules.convnext import ConvNext, TransformerDecoderDenoiser +from fish_diffusion.modules.llama import LlamaDenoiser from fish_diffusion.modules.wavenet import WaveNet DIFFUSIONS = Registry("diffusions") @@ -8,3 +9,7 @@ DENOISERS.register_module(name="WaveNetDenoiser", module=WaveNet) DENOISERS.register_module(name="ConvNextDenoiser", module=ConvNext) +DENOISERS.register_module( + name="TransformerDecoderDenoiser", module=TransformerDecoderDenoiser +) +DENOISERS.register_module(name="LlamaDenoiser", module=LlamaDenoiser) diff --git a/fish_diffusion/archs/diffsinger/diffusions/diffusion.py b/fish_diffusion/archs/diffsinger/diffusions/diffusion.py index b352c89a..095e7318 100644 --- a/fish_diffusion/archs/diffsinger/diffusions/diffusion.py +++ b/fish_diffusion/archs/diffsinger/diffusions/diffusion.py @@ -126,20 +126,21 @@ def q_sample(self, x_start, t, noise=None): + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise ) - def p_losses(self, x_start, t, cond, noise=None, mask=None): + def p_losses(self, x_start, t, cond, noise=None, x_masks=None, cond_masks=None): if noise is None: noise = torch.randn_like(x_start) noised_mel = self.q_sample(x_start=x_start, t=t, noise=noise) epsilon = self.denoise_fn(noised_mel, t, cond) - if mask is not None: - # mask: (B, N) -> (B, 1, N) - mask = mask[:, None, :] + if x_masks is not None: + # Apply mask + noised_mel = noised_mel.masked_fill(x_masks[:, None, :], 0.0) + epsilon = epsilon.masked_fill(x_masks[:, None, :], 0.0) + if cond_masks is not None: # Apply mask - noised_mel = noised_mel.masked_fill(mask, 0.0) - epsilon = epsilon.masked_fill(mask, 0.0) + cond = cond.masked_fill(cond_masks[:, None, :], 0.0) loss = self.get_mel_loss(self.noise_loss, noise, epsilon) @@ -168,7 +169,7 @@ def get_mel_loss(self, loss_fn, noise, epsilon): return loss - def train_step(self, features, mel, mel_mask=None): + def train_step(self, features, mel, x_masks=None, cond_masks=None): # Cond 基本就是 hubert / fs2 参数 b, *_, device = *features.shape, features.device features = features.transpose(1, 2) @@ -177,7 +178,9 @@ def train_step(self, features, mel, mel_mask=None): t = torch.randint(0, self.num_timesteps, (b,), device=device).long() x = self.norm_spec(mel).transpose(1, 2) # [B, M, T] - noised_mels, epsilon, loss = self.p_losses(x, t, features, mask=mel_mask) + noised_mels, epsilon, loss = self.p_losses( + x, t, features, x_masks=x_masks, cond_masks=cond_masks + ) return dict( loss=loss, @@ -199,6 +202,8 @@ def forward( skip_steps: int = 0, original_mel: torch.Tensor = None, noise_predictor: str = None, + x_masks: torch.Tensor = None, + cond_masks: torch.Tensor = None, ): if sampler_interval is None: sampler_interval = self.sampler_interval @@ -212,7 +217,8 @@ def forward( features = features.transpose(1, 2) if original_mel is None: - shape = (features.shape[0], self.mel_bins, features.shape[2]) + temp = features if x_masks is None else x_masks + shape = (temp.shape[0], self.mel_bins, temp.shape[-1]) x = torch.randn(shape, device=device) else: x = self.norm_spec(original_mel) @@ -239,7 +245,9 @@ def forward( # If using naive sampling if noise_predictor == "naive": for t in chunks: - noise = self.denoise_fn(x, t, features) + noise = self.denoise_fn( + x, t, features, x_masks=x_masks, cond_masks=cond_masks + ) x = self.naive_noise_predictor(x=x, t=t, noise=noise) return self.denorm_spec(x.transpose(1, 2)) @@ -252,6 +260,8 @@ def forward( cond=features, progress=progress, sampler_interval=sampler_interval, + x_masks=x_masks, + cond_masks=cond_masks, ) return self.denorm_spec(x.transpose(1, 2)) @@ -264,7 +274,9 @@ def forward( noise_list = torch.zeros((0, *shape), device=device) for t in chunks: - noise_pred = self.denoise_fn(x, t, features) + noise_pred = self.denoise_fn( + x, t, features, x_masks=x_masks, cond_masks=cond_masks + ) t_prev = t - sampler_interval t_prev = t_prev * (t_prev > 0) diff --git a/fish_diffusion/archs/diffsinger/diffusions/noise_predictor.py b/fish_diffusion/archs/diffsinger/diffusions/noise_predictor.py index ae9925ff..0f91eb9e 100644 --- a/fish_diffusion/archs/diffsinger/diffusions/noise_predictor.py +++ b/fish_diffusion/archs/diffsinger/diffusions/noise_predictor.py @@ -174,7 +174,16 @@ def close_tqdm(self, bar): bar.close() @torch.jit.unused # Not support jit as of now - def forward(self, denoise_fn, x, cond, progress=False, sampler_interval=10): + def forward( + self, + denoise_fn, + x, + cond, + progress=False, + sampler_interval=10, + x_masks=None, + cond_masks=None, + ): steps = self.noise_schedule.total_N // sampler_interval if progress: @@ -187,7 +196,11 @@ def forward(self, denoise_fn, x, cond, progress=False, sampler_interval=10): denoise_fn, self.noise_schedule, model_type="noise", # or "x_start" or "v" or "score" - model_kwargs={self.condition_key: cond}, + model_kwargs={ + self.condition_key: cond, + "x_masks": x_masks, + "cond_masks": cond_masks, + }, ) # 3. Define uni_pc and sample by multistep UniPC. diff --git a/fish_diffusion/archs/diffsinger/grad_tts.py b/fish_diffusion/archs/diffsinger/grad_tts.py new file mode 100644 index 00000000..076072f3 --- /dev/null +++ b/fish_diffusion/archs/diffsinger/grad_tts.py @@ -0,0 +1,124 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fish_diffusion.modules.encoders import ENCODERS + +from .diffusions import DIFFUSIONS + + +class GradTTS(nn.Module): + """GradTTS""" + + def __init__(self, model_config): + super(GradTTS, self).__init__() + + self.diffusion = DIFFUSIONS.build(model_config.diffusion) + + if getattr(model_config, "gradient_checkpointing", False): + self.diffusion.denoise_fn.gradient_checkpointing_enable() + + @staticmethod + def get_mask_from_lengths(lengths, max_len=None): + batch_size = lengths.shape[0] + if max_len is None: + max_len = torch.max(lengths).item() + + ids = ( + torch.arange(0, max_len) + .unsqueeze(0) + .expand(batch_size, -1) + .to(lengths.device) + ) + mask = ids >= lengths.unsqueeze(1).expand(-1, max_len) + + return mask + + def forward_features( + self, + speakers, + contents, + contents_lens, + contents_max_len, + mel=None, + mel_lens=None, + mel_max_len=None, + pitches=None, + pitch_shift=None, + phones2mel=None, + energy=None, + ): + src_masks = self.get_mask_from_lengths(contents_lens, contents_max_len) + + if self.training is False: + # Random shift mel_lens by 10% + mel_lens = mel_lens * (0.9 + 0.2 * torch.rand_like(mel_lens.float())) + mel_lens = mel_lens.long() + mel_max_len = torch.max(mel_lens).item() + + mel_masks = self.get_mask_from_lengths(mel_lens, mel_max_len) + + return dict( + features=contents, + cond_masks=src_masks, + x_masks=mel_masks, + x_lens=mel_lens, + ) + + def forward( + self, + speakers, + contents, + contents_lens, + contents_max_len, + mel=None, + mel_lens=None, + mel_max_len=None, + pitches=None, + pitch_shift=None, + phones2mel=None, + energy=None, + ): + features = self.forward_features( + speakers=speakers, + contents=contents, + contents_lens=contents_lens, + contents_max_len=contents_max_len, + mel=mel, + mel_lens=mel_lens, + mel_max_len=mel_max_len, + pitches=pitches, + pitch_shift=pitch_shift, + phones2mel=phones2mel, + energy=energy, + ) + + if self.training: + output_dict = self.diffusion.train_step( + features["features"], + mel, + x_masks=features["x_masks"], + cond_masks=features["cond_masks"], + ) + else: + output_dict = { + "loss": 0.0, + } + + if "loss" in features: + output_dict["loss"] = output_dict["loss"] + features["loss"] + + if "metrics" in features: + metrics = output_dict.get("metrics", {}) + metrics.update(features["metrics"]) + output_dict["metrics"] = metrics + + # For validation + output_dict["features"] = features["features"] + output_dict["x_masks"] = features["x_masks"] + output_dict["x_lens"] = features["x_lens"] + output_dict["cond_masks"] = features["cond_masks"] + + return output_dict diff --git a/fish_diffusion/datasets/__init__.py b/fish_diffusion/datasets/__init__.py index 3ac1b2c3..7c73ddde 100644 --- a/fish_diffusion/datasets/__init__.py +++ b/fish_diffusion/datasets/__init__.py @@ -3,6 +3,7 @@ from .hifisinger import HiFiSVCDataset from .naive import NaiveDataset, NaiveSVCDataset from .repeat import RepeatDataset +from .sample import SampleDataset __all__ = [ "DATASETS", @@ -11,4 +12,5 @@ "NaiveDataset", "NaiveSVCDataset", "HiFiSVCDataset", + "SampleDataset", ] diff --git a/fish_diffusion/datasets/naive.py b/fish_diffusion/datasets/naive.py index f7198e38..31b3e835 100644 --- a/fish_diffusion/datasets/naive.py +++ b/fish_diffusion/datasets/naive.py @@ -246,3 +246,55 @@ class NaiveSVSDataset(NaiveDataset): keys=[("pitches", -1), ("time_stretch", -1), ("key_shift", -1)], ), ] + + +@DATASETS.register_module() +class NaiveTTSDataset(NaiveDataset): + processing_pipeline = [ + dict( + type="PickKeys", + keys=[ + "path", + "mel", + "contents", + "speaker", + ], + ), + dict(type="Transpose", keys=[("mel", 1, 0)]), + ] + + collating_pipeline = [ + dict(type="FilterByLength", key="mel", dim=0, min_length=1, max_length=2048), + dict(type="ListToDict"), + dict( + type="PadStack", + keys=[("mel", -2), ("contents", -1)], + ), + dict( + type="ToTensor", + keys=[ + ("speaker", torch.int64), + ("contents", torch.int64), + ], + ), + ] + + +@DATASETS.register_module() +class NaiveDenoiserDataset(NaiveDataset): + processing_pipeline = [ + dict( + type="PickKeys", + keys=[ + "path", + "mel", + "contents", + ], + ), + dict(type="Transpose", keys=[("mel", 1, 0), ("contents", 1, 0)]), + ] + + collating_pipeline = [ + dict(type="ListToDict"), + dict(type="PadStack", keys=[("mel", -2), ("contents", -2)]), + ] diff --git a/fish_diffusion/datasets/repeat.py b/fish_diffusion/datasets/repeat.py index 9fc63fa4..e9828012 100644 --- a/fish_diffusion/datasets/repeat.py +++ b/fish_diffusion/datasets/repeat.py @@ -1,4 +1,4 @@ -from typing import Iterable, Union +from typing import Union from torch.utils.data import Dataset diff --git a/fish_diffusion/datasets/sample.py b/fish_diffusion/datasets/sample.py new file mode 100644 index 00000000..ce5d1e50 --- /dev/null +++ b/fish_diffusion/datasets/sample.py @@ -0,0 +1,37 @@ +import random +from typing import Union + +from torch.utils.data import Dataset + +from .builder import DATASETS + + +@DATASETS.register_module() +class SampleDataset(Dataset): + def __init__( + self, dataset: Union[dict, Dataset], num_samples: int = 8, collate_fn=None + ) -> None: + """Sample a dataset. Useful for DDP training. + + Args: + dataset (Union[dict, Dataset]): Dataset to sample. + num_samples (int): Number of samples to sample. + collate_fn (Callable, optional): Collate function. Defaults to None. + """ + + super().__init__() + + self.num_samples = num_samples + self.collate_fn = collate_fn + + if isinstance(dataset, dict): + self.dataset = DATASETS.build(dataset) + else: + self.dataset = dataset + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + idx = random.randint(0, len(self.dataset) - 1) + return self.dataset[idx] diff --git a/fish_diffusion/datasets/utils.py b/fish_diffusion/datasets/utils.py index 1a0f6c84..a7346eaf 100644 --- a/fish_diffusion/datasets/utils.py +++ b/fish_diffusion/datasets/utils.py @@ -7,6 +7,7 @@ from .builder import DATASETS from .repeat import RepeatDataset +from .sample import SampleDataset def build_loader_from_config(cfg, num_devices=1): @@ -19,7 +20,7 @@ def build_loader_from_config(cfg, num_devices=1): valid_dataset = DATASETS.build(cfg.dataset.valid) - if num_devices > 1: + if num_devices > 1 and isinstance(valid_dataset, (RepeatDataset, SampleDataset)): valid_dataset = RepeatDataset( valid_dataset, repeat=num_devices, collate_fn=valid_dataset.collate_fn ) @@ -122,6 +123,8 @@ def transform_pipeline(pipeline, data): for k, t in step["keys"]: if isinstance(data[k], np.ndarray): data[k] = torch.from_numpy(data[k]).type(t) + elif isinstance(data[k], torch.Tensor): + data[k] = data[k].type(t) else: data[k] = torch.tensor(data[k], dtype=t) elif step["type"] == "Transpose": @@ -133,6 +136,14 @@ def transform_pipeline(pipeline, data): data[k] = np.expand_dims(data[k], *args) else: data[k] = data[k].unsqueeze(*args) + elif step["type"] == "FilterByLength": + data = [ + i + for i in data + if step["min_length"] + <= i[step["key"]].shape[step["dim"]] + <= step["max_length"] + ] else: raise NotImplementedError(f"Unknown transform type: {step['type']}") diff --git a/fish_diffusion/modules/convnext.py b/fish_diffusion/modules/convnext.py index bc20d112..99c320cd 100644 --- a/fish_diffusion/modules/convnext.py +++ b/fish_diffusion/modules/convnext.py @@ -3,6 +3,7 @@ import torch import torch.nn.functional as F +import torch.utils.checkpoint from torch import nn from fish_diffusion.modules.wavenet import DiffusionEmbedding @@ -55,14 +56,22 @@ def forward( x: torch.Tensor, condition: Optional[torch.Tensor] = None, diffusion_step: Optional[torch.Tensor] = None, + x_masks: Optional[torch.Tensor] = None, + cond_masks: Optional[torch.Tensor] = None, ) -> torch.Tensor: residual = x - x = ( - x - + self.diffusion_step_projection(diffusion_step) - + self.condition_projection(condition) - ) + if diffusion_step is not None: + x = x + self.diffusion_step_projection(diffusion_step) + + if condition is not None: + if cond_masks is not None: + condition = condition.masked_fill(cond_masks[:, None, :], 0.0) + + x = x + self.condition_projection(condition) + + if x_masks is not None: + x = x.masked_fill(x_masks[:, None, :], 0.0) x = self.dwconv(x) x = x.transpose(1, 2) # (B, C, T) -> (B, T, C) @@ -75,9 +84,74 @@ def forward( x = x.transpose(1, 2) # (B, T, C) -> (B, C, T) x = residual + x + + if x_masks is not None: + x = x.masked_fill(x_masks[:, None, :], 0.0) + return x +class CrossAttentionBlock(nn.TransformerDecoderLayer): + def __init__( + self, + dim: int, + intermediate_dim: int, + nhead: int = 8, + ): + super().__init__( + d_model=dim, + nhead=nhead, + dim_feedforward=intermediate_dim, + activation="gelu", + batch_first=True, + ) + + self.diffusion_step_projection = nn.Conv1d(dim, dim, 1) + self.register_buffer("positional_embedding", self.get_embedding(dim)) + self.position_scale_query = nn.Parameter(torch.ones(1)) + self.position_scale_key = nn.Parameter(torch.ones(1)) + + def get_embedding(self, embedding_dim, num_embeddings=4096): + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( + 1 + ) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( + num_embeddings, -1 + ) + + return emb + + def forward(self, x, condition, diffusion_step, x_masks=None, cond_masks=None): + if diffusion_step is not None: + x = x + self.diffusion_step_projection(diffusion_step) + + # Apply positional encoding to both x and condition + x = x.transpose(1, 2) + condition = condition.transpose(1, 2) + + # self.get_embedding(dim) + x = x + self.positional_embedding[: x.size(1)][None] * self.position_scale_query + condition = ( + condition + + self.positional_embedding[: condition.size(1)][None] + * self.position_scale_key + ) + + return ( + super() + .forward( + tgt=x, + memory=condition, + tgt_key_padding_mask=x_masks, + memory_key_padding_mask=cond_masks, + ) + .transpose(1, 2) + ) + + class ConvNext(nn.Module): def __init__( self, @@ -88,6 +162,8 @@ def __init__( num_layers=20, dilation_cycle=4, gradient_checkpointing=False, + cross_attention=False, + cross_every_n_layers=5, ): super(ConvNext, self).__init__() @@ -104,16 +180,25 @@ def __init__( nn.Conv1d(dim * mlp_factor, dim, 1), ) - self.residual_layers = nn.ModuleList( - [ + self.residual_layers = nn.ModuleList([]) + + for i in range(num_layers): + if cross_attention and i % cross_every_n_layers == 0: + self.residual_layers.append( + CrossAttentionBlock( + dim=dim, + intermediate_dim=dim * mlp_factor, + ) + ) + + self.residual_layers.append( ConvNeXtBlock( dim=dim, intermediate_dim=dim * mlp_factor, dilation=2 ** (i % dilation_cycle), ) - for i in range(num_layers) - ] - ) + ) + self.output_projection = nn.Sequential( nn.Conv1d(dim, dim, kernel_size=1), nn.GELU(), @@ -121,13 +206,16 @@ def __init__( ) self.gradient_checkpointing = gradient_checkpointing + self.cross_attention = cross_attention - def forward(self, x, diffusion_step, conditioner): + def forward(self, x, diffusion_step, conditioner, x_masks=None, cond_masks=None): """ :param x: [B, M, T] :param diffusion_step: [B,] - :param conditioner: [B, M, T] + :param conditioner: [B, M, E] + :param x_masks: [B, T] bool mask + :param cond_masks: [B, E] bool mask :return: """ @@ -145,14 +233,176 @@ def forward(self, x, diffusion_step, conditioner): diffusion_step = self.diffusion_embedding(diffusion_step).unsqueeze(-1) condition = self.conditioner_projection(conditioner) + if x_masks is not None: + x = x.masked_fill(x_masks[:, None, :], 0.0) + + if cond_masks is not None: + condition = condition.masked_fill(cond_masks[:, None, :], 0.0) + for layer in self.residual_layers: + is_cross_layer = isinstance(layer, CrossAttentionBlock) + temp_condition = ( + condition + if ((self.cross_attention is False) or is_cross_layer) + else None + ) + if self.training and self.gradient_checkpointing: x = torch.utils.checkpoint.checkpoint( - layer, x, condition, diffusion_step + layer, x, temp_condition, diffusion_step, x_masks, cond_masks ) else: - x = layer(x, condition, diffusion_step) + x = layer(x, temp_condition, diffusion_step, x_masks, cond_masks) x = self.output_projection(x) # [B, 128, T] + if x_masks is not None: + x = x.masked_fill(x_masks[:, None, :], 0.0) return x[:, None] if use_4_dim else x + + +class TransformerDecoderDenoiser(nn.Module): + def __init__( + self, + mel_channels=128, + dim=512, + mlp_factor=4, + condition_dim=256, + num_layers=12, + gradient_checkpointing=False, + ): + super().__init__() + + self.input_projection = nn.Sequential( + nn.Conv1d(mel_channels, dim * mlp_factor, 1), + nn.GELU(), + nn.Conv1d(dim * mlp_factor, dim, 1), + ) + self.diffusion_embedding = nn.Sequential( + DiffusionEmbedding(dim), + nn.Linear(dim, dim * mlp_factor), + nn.GELU(), + nn.Linear(dim * mlp_factor, dim), + ) + self.condition_projection = nn.Sequential( + nn.Conv1d(condition_dim, dim * mlp_factor, 1), + nn.GELU(), + nn.Conv1d(dim * mlp_factor, dim, 1), + ) + + self.register_buffer("positional_embedding", self.get_embedding(dim)) + self.position_scale_query = nn.Parameter(torch.ones(1)) + self.position_scale_key = nn.Parameter(torch.ones(1)) + + self.layers = nn.ModuleList( + [ + nn.TransformerDecoderLayer( + d_model=dim, + nhead=8, + dim_feedforward=dim * mlp_factor, + activation="gelu", + batch_first=True, + ) + for _ in range(num_layers) + ] + ) + + self.output_projection = nn.Sequential( + nn.Conv1d(dim, dim, kernel_size=1), + nn.GELU(), + nn.Conv1d(dim, mel_channels, kernel_size=1), + ) + + self.gradient_checkpointing = gradient_checkpointing + + def get_embedding(self, embedding_dim, num_embeddings=4096): + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( + 1 + ) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( + num_embeddings, -1 + ) + + return emb + + def forward(self, x, diffusion_step, conditioner, x_masks=None, cond_masks=None): + """ + + :param x: [B, M, T] + :param diffusion_step: [B,] + :param conditioner: [B, M, E] + :param x_masks: [B, T] bool mask + :param cond_masks: [B, E] bool mask + :return: + """ + + assert x.dim() == 3, f"mel must be 3 dim tensor, but got {x.dim()}" + + x = self.input_projection(x).transpose(1, 2) # x [B, T, residual_channel] + x_pos = self.positional_embedding[None, : x.size(1)] * self.position_scale_query + x = x + x_pos + + condition = self.condition_projection(conditioner).transpose(1, 2) + diffusion_step = self.diffusion_embedding(diffusion_step).unsqueeze(1) + condition_pos = ( + self.positional_embedding[None, : condition.size(1)] + * self.position_scale_key + ) + condition = condition + condition_pos + diffusion_step + + if x_masks is not None: + x = x.masked_fill(x_masks[..., None], 0.0) + + if cond_masks is not None: + condition = condition.masked_fill(cond_masks[..., None], 0.0) + + for layer in self.layers: + if self.training and self.gradient_checkpointing: + x = torch.utils.checkpoint.checkpoint( + layer, + x, + condition, + x_masks, + cond_masks, + use_reentrant=False, + ) + else: + x = layer( + tgt=x, + memory=condition, + tgt_key_padding_mask=x_masks, + memory_key_padding_mask=cond_masks, + ) + + x = x.transpose(1, 2) + x = self.output_projection(x) # [B, 128, T] + if x_masks is not None: + x = x.masked_fill(x_masks[:, None], 0.0) + + return x + + +if __name__ == "__main__": + import torch + + gpu_memory_usage = torch.cuda.memory_allocated() / 1024**3 + torch.cuda.empty_cache() + torch.cuda.synchronize() + + model = TransformerDecoderDenoiser().cuda() + x = torch.randn(8, 128, 1024).cuda() + diffusion_step = torch.randint(0, 1000, (8,)).cuda() + conditioner = torch.randn(8, 256, 256).cuda() + x_masks = torch.randint(0, 2, (8, 1024)).bool().cuda() + cond_masks = torch.randint(0, 2, (8, 256)).bool().cuda() + y = model(x, diffusion_step, conditioner, x_masks, cond_masks) + print(y.shape) + + torch.cuda.empty_cache() + torch.cuda.synchronize() + + gpu_memory_usage = torch.cuda.memory_allocated() / 1024**3 - gpu_memory_usage + print(f"GPU memory usage: {gpu_memory_usage:.2f} GB") diff --git a/fish_diffusion/modules/encoders/__init__.py b/fish_diffusion/modules/encoders/__init__.py index e278aa23..200b5282 100644 --- a/fish_diffusion/modules/encoders/__init__.py +++ b/fish_diffusion/modules/encoders/__init__.py @@ -1,17 +1,19 @@ -from .attention import AttentionEncoder +from .bert import BertEncoder from .builder import ENCODERS from .fast_speech import FastSpeech2Encoder from .identity import IdentityEncoder from .naive_projection import NaiveProjectionEncoder from .pitch_quant import QuantizedPitchEncoder from .similar_cluster import SimilarClusterEncoder +from .transformer import TransformerEncoder __all__ = [ "ENCODERS", "NaiveProjectionEncoder", "FastSpeech2Encoder", "IdentityEncoder", - "AttentionEncoder", "SimilarClusterEncoder", "QuantizedPitchEncoder", + "BertEncoder", + "TransformerEncoder", ] diff --git a/fish_diffusion/modules/encoders/attention.py b/fish_diffusion/modules/encoders/attention.py deleted file mode 100644 index 55f15f3d..00000000 --- a/fish_diffusion/modules/encoders/attention.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Iterable - -from torch import Tensor, nn -from torch.nn import functional as F -from whisper.model import AudioEncoder, LayerNorm, ResidualAttentionBlock, sinusoids - -from .builder import ENCODERS - - -@ENCODERS.register_module() -class AttentionEncoder(nn.Module): - def __init__( - self, - input_size: int, - output_size: int, - n_head: int = 4, - n_layer: int = 4, - max_len: int = 10000, - ): - super().__init__() - - self.proj = nn.Linear(input_size, output_size) - self.register_buffer("positional_embedding", sinusoids(max_len, output_size)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(output_size, n_head) for _ in range(n_layer)] - ) - self.ln_post = LayerNorm(output_size) - - def forward(self, x: Tensor, encoder_padding_mask: Tensor = None): - x = F.gelu(self.proj(x)) - - x = (x + self.positional_embedding[: x.shape[1]]).to(x.dtype) - - for block in self.blocks: - x = block(x) - - x = self.ln_post(x) - - return x diff --git a/fish_diffusion/modules/encoders/bert.py b/fish_diffusion/modules/encoders/bert.py new file mode 100644 index 00000000..f5bd7c1b --- /dev/null +++ b/fish_diffusion/modules/encoders/bert.py @@ -0,0 +1,44 @@ +from typing import Optional + +from torch import nn +from transformers import AutoModel + +from .builder import ENCODERS + + +@ENCODERS.register_module() +class BertEncoder(nn.Module): + def __init__( + self, + model_name: str, + output_size: Optional[int] = None, + pretrained: bool = True, + ): + super(BertEncoder, self).__init__() + + if pretrained: + self.bert = AutoModel.from_pretrained(model_name) + else: + self.bert = AutoModel.from_config(model_name) + + if output_size is None: + output_size = self.bert.config.hidden_size + + self.proj = None + if self.bert.config.hidden_size != output_size: + self.proj = nn.Linear(self.bert.config.hidden_size, output_size) + + self.output_size = output_size + + def forward(self, attention_mask=None, *args, **kwargs): + x = self.bert(*args, attention_mask=attention_mask, **kwargs).last_hidden_state + + if self.proj is not None: + x = self.proj(x) + + if attention_mask.dim() == 2: + attention_mask = attention_mask.unsqueeze(-1) + + x = x * attention_mask + + return x diff --git a/fish_diffusion/modules/encoders/transformer.py b/fish_diffusion/modules/encoders/transformer.py new file mode 100644 index 00000000..4b563e6f --- /dev/null +++ b/fish_diffusion/modules/encoders/transformer.py @@ -0,0 +1,51 @@ +from torch import nn + +from .builder import ENCODERS + + +@ENCODERS.register_module() +class TransformerEncoder(nn.Module): + def __init__( + self, + input_size: int, + output_size: int, + hidden_size: int = 1024, + num_layers: int = 4, + num_heads: int = 16, + dropout: float = 0.1, + mlp_ratio: float = 4.0, + ): + super(TransformerEncoder, self).__init__() + + layer = nn.TransformerEncoderLayer( + d_model=hidden_size, + nhead=num_heads, + dim_feedforward=int(hidden_size * mlp_ratio), + dropout=dropout, + batch_first=True, + ) + + self.encoder = nn.TransformerEncoder( + encoder_layer=layer, + num_layers=num_layers, + ) + + if input_size != hidden_size: + self.in_proj = nn.Linear(input_size, hidden_size) + + if output_size != hidden_size: + self.out_proj = nn.Linear(hidden_size, output_size) + + def forward(self, x, x_mask): + if x_mask.dim() == 2: + x_mask = x_mask.unsqueeze(-1) + + if hasattr(self, "in_proj"): + x = self.in_proj(x * x_mask) + + x = self.encoder(x * x_mask, src_key_padding_mask=(~x_mask.bool()).squeeze(-1)) + + if hasattr(self, "out_proj"): + x = self.out_proj(x * x_mask) + + return x * x_mask diff --git a/fish_diffusion/modules/feature_extractors/__init__.py b/fish_diffusion/modules/feature_extractors/__init__.py index 536d7762..13a88fc1 100644 --- a/fish_diffusion/modules/feature_extractors/__init__.py +++ b/fish_diffusion/modules/feature_extractors/__init__.py @@ -1,7 +1,10 @@ +from .bert_tokenizer import BertTokenizer from .builder import FEATURE_EXTRACTORS from .chinese_hubert import ChineseHubertSoft from .content_vec import ContentVec +from .encodec import Encodec from .hubert_soft import HubertSoft +from .llama_tokenizer import LlamaTokenizer from .opencpop_transcription import OpenCpopTranscriptionToPhonemesDuration from .whisper import AlignedWhisper @@ -12,4 +15,7 @@ "OpenCpopTranscriptionToPhonemesDuration", "AlignedWhisper", "ContentVec", + "BertTokenizer", + "LlamaTokenizer", + "Encodec", ] diff --git a/fish_diffusion/modules/feature_extractors/base.py b/fish_diffusion/modules/feature_extractors/base.py index a593877a..3c0f567c 100644 --- a/fish_diffusion/modules/feature_extractors/base.py +++ b/fish_diffusion/modules/feature_extractors/base.py @@ -5,6 +5,8 @@ class BaseFeatureExtractor(nn.Module): + sampling_rate = 16000 + def preprocess(self, path_or_audio, sampling_rate=None): if isinstance(path_or_audio, str): audio, sampling_rate = torchaudio.load(path_or_audio) @@ -15,13 +17,15 @@ def preprocess(self, path_or_audio, sampling_rate=None): # To mono audio = audio.mean(0, keepdim=True) - if sampling_rate != 16000: + if sampling_rate != self.sampling_rate: # There is a memory leak in torchaudio resampling # https://github.com/pytorch/audio/issues/2338 audio = ( torch.from_numpy( librosa.resample( - audio.cpu().numpy(), orig_sr=sampling_rate, target_sr=16000 + audio.cpu().numpy(), + orig_sr=sampling_rate, + target_sr=self.sampling_rate, ) ) .float() diff --git a/fish_diffusion/modules/feature_extractors/bert_tokenizer.py b/fish_diffusion/modules/feature_extractors/bert_tokenizer.py new file mode 100644 index 00000000..bfde8fde --- /dev/null +++ b/fish_diffusion/modules/feature_extractors/bert_tokenizer.py @@ -0,0 +1,27 @@ +from pathlib import Path + +import torch +from transformers import AutoTokenizer + +from .base import BaseFeatureExtractor +from .builder import FEATURE_EXTRACTORS + + +@FEATURE_EXTRACTORS.register_module() +class BertTokenizer(BaseFeatureExtractor): + def __init__( + self, + model_name: str, + label_suffix: str = ".txt", + ): + super().__init__() + + self.tokenizer = AutoTokenizer.from_pretrained(model_name) + self.label_suffix = label_suffix + + @torch.no_grad() + def forward(self, audio_path: Path): + transcript = audio_path.with_suffix(self.label_suffix).read_text().strip() + input_ids = self.tokenizer.encode(transcript, return_tensors="pt") + + return input_ids diff --git a/fish_diffusion/modules/feature_extractors/encodec.py b/fish_diffusion/modules/feature_extractors/encodec.py new file mode 100644 index 00000000..57af8a8f --- /dev/null +++ b/fish_diffusion/modules/feature_extractors/encodec.py @@ -0,0 +1,56 @@ +from typing import Optional + +import torch +from transformers import AutoProcessor, EncodecModel + +from .base import BaseFeatureExtractor +from .builder import FEATURE_EXTRACTORS + + +class EncodecOverrideModel(EncodecModel): + def _decode_frame( + self, codes: torch.Tensor, scale: Optional[torch.Tensor] = None + ) -> torch.Tensor: + codes = codes.transpose(0, 1) + return self.quantizer.decode(codes) + + +@FEATURE_EXTRACTORS.register_module() +class Encodec(BaseFeatureExtractor): + sampling_rate = 24000 + + def __init__( + self, + model: str = "facebook/encodec_24khz", + bandwidth: float = 1.5, + first_codebook_only: bool = False, + ): + super().__init__() + + self.processor = AutoProcessor.from_pretrained(model) + self.model = EncodecOverrideModel.from_pretrained(model) + self.model.eval() + + self.bandwidth = bandwidth + self.first_codebook_only = first_codebook_only + + @torch.no_grad() + def forward(self, path_or_audio, sampling_rate=None): + audios = self.preprocess(path_or_audio, sampling_rate) + return self._forward(audios) + + @torch.no_grad() + def _forward(self, audio): + if audio.dim() == 1: + audio = audio[None] + + audio = audio.to(self.device) + assert audio.dim() == 2 + + x = self.model.encode( + audio[:, None], bandwidth=self.bandwidth, return_dict=True + ) + if self.first_codebook_only: + x.audio_codes = x.audio_codes[:, :, :1, :] + + return self.model.decode(x.audio_codes, x.audio_scales).audio_values diff --git a/fish_diffusion/modules/feature_extractors/llama_tokenizer.py b/fish_diffusion/modules/feature_extractors/llama_tokenizer.py new file mode 100644 index 00000000..d72b5105 --- /dev/null +++ b/fish_diffusion/modules/feature_extractors/llama_tokenizer.py @@ -0,0 +1,32 @@ +from pathlib import Path + +import torch +from transformers import AutoTokenizer + +from .base import BaseFeatureExtractor +from .builder import FEATURE_EXTRACTORS + + +@FEATURE_EXTRACTORS.register_module() +class LlamaTokenizer(BaseFeatureExtractor): + def __init__( + self, + model_name: str, + label_suffix: str = ".txt", + speaker_mode: str = "libritts", + ): + super().__init__() + + self.tokenizer = AutoTokenizer.from_pretrained(model_name) + self.label_suffix = label_suffix + self.speaker_mode = speaker_mode + + @torch.no_grad() + def forward(self, audio_path: Path): + transcript = audio_path.with_suffix(self.label_suffix).read_text().strip() + speaker = audio_path.parent.parent.name + + transcript = f"[spk] {speaker} [txt] {transcript} [mel]" + input_ids = self.tokenizer.encode(transcript, return_tensors="pt") + + return input_ids diff --git a/fish_diffusion/modules/llama.py b/fish_diffusion/modules/llama.py new file mode 100644 index 00000000..60fb8cae --- /dev/null +++ b/fish_diffusion/modules/llama.py @@ -0,0 +1,144 @@ +from typing import List, Optional, Tuple, Union + +import torch +from torch import FloatTensor, LongTensor, Tensor, nn +from transformers.modeling_outputs import BaseModelOutputWithPast +from transformers.models.llama import LlamaConfig, LlamaModel + +from fish_diffusion.modules.wavenet import DiffusionEmbedding + + +class LlamaDenosierConfig(LlamaConfig): + def __init__( + self, + *args, + diffusion_channels: int = 128, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.diffusion_channels = diffusion_channels + + +class LlamaDenoiser(LlamaModel): + def __init__(self, **config): + if isinstance(config, LlamaDenosierConfig) is False: + config = LlamaDenosierConfig(**config) + + super().__init__(config) + + self.diffusion_embedding = DiffusionEmbedding(config.hidden_size) + self.mel_len_embedding = nn.Linear(1, config.hidden_size) + self.in_proj = nn.Linear(config.diffusion_channels, config.hidden_size) + self.merge_proj = nn.Linear(config.hidden_size * 3, config.hidden_size) + self.out_proj = nn.Linear(config.hidden_size, config.diffusion_channels) + + def forward(self, x, diffusion_step, conditioner, x_masks=None, cond_masks=None): + """ + + :param x: [B, M, T] + :param diffusion_step: [B,] + :param conditioner: [B, E] tokens + :param x_masks: [B, T] float mask + :param cond_masks: [B, E] float mask + :return: + """ + + x = x.transpose(1, 2) + + if x_masks is None: + x_masks = torch.ones(x.shape[0], x.shape[1], device=x.device, dtype=x.dtype) + elif x_masks.dtype == torch.bool: + x_masks = (~x_masks).float() + + if conditioner.ndim == 3: + assert conditioner.shape[-1] == 1 + conditioner = conditioner.squeeze(-1) + + if cond_masks is None: + cond_masks = torch.ones_like(conditioner, device=x.device, dtype=x.dtype) + elif cond_masks.dtype == torch.bool: + cond_masks = (~cond_masks).float() + + inputs_embeds = self.embed_tokens(conditioner) + attention_mask = torch.concatenate([x_masks, cond_masks], dim=1) + + # Diffusion embedding + diffusion_embeds = self.diffusion_embedding(diffusion_step) + if diffusion_embeds.ndim != 3: + diffusion_embeds = diffusion_embeds[:, None] + + if diffusion_step.shape[0] != x.shape[0]: + diffusion_embeds = diffusion_embeds.repeat(x.shape[0], 1, 1) + + diffusion_embeds = diffusion_embeds.repeat(1, x.shape[1], 1) + + # Mel length embedding + mel_lens = x_masks.sum(dim=1, keepdim=True).float() + mel_lens = torch.log(mel_lens)[:, None] + mel_lens_embeds = self.mel_len_embedding(mel_lens) + mel_lens_embeds = mel_lens_embeds.repeat(1, x.shape[1], 1) + + # Merge + x = self.in_proj(x) + x = torch.concatenate([diffusion_embeds, mel_lens_embeds, x], dim=2) + x = self.merge_proj(x) + + # Control tokens concat ((diffusion + mel length) embedding + x) + inputs_embeds = torch.concatenate([inputs_embeds, x], dim=1) + + outputs = ( + super() + .forward( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + output_attentions=False, + output_hidden_states=False, + ) + .last_hidden_state + ) + + # Extract x + x = outputs[:, -diffusion_embeds.shape[1] :, :] + x = self.out_proj(x) + + return x.transpose(1, 2) + + +if __name__ == "__main__": + # Print trainable parameters + model = LlamaDenoiser( + bos_token_id=1, + eos_token_id=2, + hidden_act="silu", + hidden_size=768, + initializer_range=0.02, + intermediate_size=768 * 4, + max_position_embeddings=4096, + model_type="llama", + num_attention_heads=16, + num_hidden_layers=24, + num_key_value_heads=16, + rms_norm_eps=1e-05, + rope_scaling=None, + tie_word_embeddings=False, + vocab_size=32000, + ) + pms = list(filter(lambda p: p.requires_grad, model.parameters())) + print("Trainable parameters:") + # for p in pms: + # print(p.shape) + print("Total:", sum([p.numel() for p in pms]) / 1_000_000, "M") + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf") + print(tokenizer.decode(tokenizer.encode("[spk] Aria [txt] 真的难绷 [mel]"))) + print(tokenizer.bos_token) + print(tokenizer.eos_token) + + x = torch.randn(1, 128, 100) + diffusion_step = torch.randint(1, 100, (1, 1)) + condition = torch.randint(1, 200, (1, 10)) + x = model(x, diffusion_step, condition) + print(x.shape) diff --git a/fish_diffusion/modules/monotonic_align/__init__.py b/fish_diffusion/modules/monotonic_align/__init__.py new file mode 100644 index 00000000..e0e29bb1 --- /dev/null +++ b/fish_diffusion/modules/monotonic_align/__init__.py @@ -0,0 +1,16 @@ +from numpy import float32, int32, zeros +from torch import from_numpy + +from .core import maximum_path_jit + + +def maximum_path(neg_cent, mask): + device = neg_cent.device + dtype = neg_cent.dtype + neg_cent = neg_cent.data.cpu().numpy().astype(float32) + path = zeros(neg_cent.shape, dtype=int32) + + t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32) + t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32) + maximum_path_jit(path, neg_cent, t_t_max, t_s_max) + return from_numpy(path).to(device=device, dtype=dtype) diff --git a/fish_diffusion/modules/monotonic_align/core.py b/fish_diffusion/modules/monotonic_align/core.py new file mode 100644 index 00000000..ffa489da --- /dev/null +++ b/fish_diffusion/modules/monotonic_align/core.py @@ -0,0 +1,46 @@ +import numba + + +@numba.jit( + numba.void( + numba.int32[:, :, ::1], + numba.float32[:, :, ::1], + numba.int32[::1], + numba.int32[::1], + ), + nopython=True, + nogil=True, +) +def maximum_path_jit(paths, values, t_ys, t_xs): + b = paths.shape[0] + max_neg_val = -1e9 + for i in range(int(b)): + path = paths[i] + value = values[i] + t_y = t_ys[i] + t_x = t_xs[i] + + v_prev = v_cur = 0.0 + index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[y - 1, x] + if x == 0: + if y == 0: + v_prev = 0.0 + else: + v_prev = max_neg_val + else: + v_prev = value[y - 1, x - 1] + value[y, x] += max(v_prev, v_cur) + + for y in range(t_y - 1, -1, -1): + path[y, index] = 1 + if index != 0 and ( + index == y or value[y - 1, index] < value[y - 1, index - 1] + ): + index = index - 1 diff --git a/fish_diffusion/modules/vocoders/nsf_hifigan/nsf_hifigan.py b/fish_diffusion/modules/vocoders/nsf_hifigan/nsf_hifigan.py index 0d7e7f03..870a84c6 100644 --- a/fish_diffusion/modules/vocoders/nsf_hifigan/nsf_hifigan.py +++ b/fish_diffusion/modules/vocoders/nsf_hifigan/nsf_hifigan.py @@ -72,7 +72,9 @@ def __init__( @torch.no_grad() def spec2wav(self, mel, f0, key_shift=0): c = mel[None] - f0 *= 2 ** (key_shift / 12) + + if key_shift is not None and key_shift != 0: + f0 *= 2 ** (key_shift / 12) if self.use_natural_log is False: c = 2.30259 * c diff --git a/fish_diffusion/modules/wavenet.py b/fish_diffusion/modules/wavenet.py index df45754c..1f9ed45f 100644 --- a/fish_diffusion/modules/wavenet.py +++ b/fish_diffusion/modules/wavenet.py @@ -191,7 +191,7 @@ def __init__( ) nn.init.zeros_(self.output_projection.conv.weight) - def forward(self, x, diffusion_step, conditioner): + def forward(self, x, diffusion_step, conditioner, x_masks=None, cond_masks=None): """ :param x: [B, M, T] @@ -214,6 +214,12 @@ def forward(self, x, diffusion_step, conditioner): diffusion_step = self.diffusion_embedding(diffusion_step) diffusion_step = self.mlp(diffusion_step) + if x_masks is not None: + x = x.masked_fill(x_masks[:, None], 0.0) + + if cond_masks is not None: + conditioner = conditioner.masked_fill(cond_masks[:, None], 0.0) + skip = [] for layer in self.residual_layers: x, skip_connection = layer(x, conditioner, diffusion_step) @@ -224,4 +230,7 @@ def forward(self, x, diffusion_step, conditioner): x = F.relu(x) x = self.output_projection(x) # [B, 128, T] + if x_masks is not None: + x = x.masked_fill(x_masks[:, None], 0.0) + return x[:, None] if use_4_dim else x diff --git a/fish_diffusion/utils/audio.py b/fish_diffusion/utils/audio.py index b5197eef..02f7c8d1 100644 --- a/fish_diffusion/utils/audio.py +++ b/fish_diffusion/utils/audio.py @@ -173,7 +173,7 @@ def separate_vocals( device: Union[str, torch.device] = "cpu", separate_model: torch.nn.Module = None, ): - if separate_model is None: + if separate_model is not None: model = separate_model.to(device) else: model = separate_audio.init_model("htdemucs", device=device) diff --git a/fish_diffusion/utils/viz.py b/fish_diffusion/utils/viz.py index a6d58a12..0c11f026 100644 --- a/fish_diffusion/utils/viz.py +++ b/fish_diffusion/utils/viz.py @@ -34,32 +34,45 @@ def viz_synth_sample( gt_pitch, predict_mel, predict_mel_len, + gt_mel_len, vocoder, return_image=False, ): - mel_len = predict_mel_len.item() - pitch = gt_pitch[:mel_len] - mel_target = gt_mel[:mel_len].float().detach().T - mel_prediction = predict_mel[:mel_len].float().detach().T - - fig_mels = plot_mel( - [ - mel_prediction.cpu().numpy(), - mel_target.cpu().numpy(), - (mel_prediction - mel_target).abs().cpu().numpy(), - ], - ["Sampled Spectrogram", "Ground-Truth Spectrogram", "Difference"], - ) + predict_mel_len = predict_mel_len.item() + gt_mel_len = gt_mel_len.item() + + pitch = gt_pitch[:gt_mel_len] if gt_pitch is not None else None + mel_target = gt_mel[:gt_mel_len].float().detach().T + mel_prediction = predict_mel[:predict_mel_len].float().detach().T + + mels = [ + mel_prediction.cpu().numpy(), + mel_target.cpu().numpy(), + ] + titles = ["Sampled Spectrogram", "Ground-Truth Spectrogram"] + + if mel_prediction.shape == mel_target.shape: + mels.append((mel_prediction - mel_target).abs().cpu().numpy()) + titles.append("Difference") + + fig_mels = plot_mel(mels, titles) wav_reconstruction = vocoder.spec2wav(mel_target, pitch) wav_prediction = vocoder.spec2wav(mel_prediction, pitch) - wav_reconstruction = loudness_norm( - wav_reconstruction.cpu().float().numpy(), 44100, block_size=0.1 - ) - wav_prediction = loudness_norm( - wav_prediction.cpu().float().numpy(), 44100, block_size=0.1 - ) + try: + wav_reconstruction = loudness_norm( + wav_reconstruction.cpu().float().numpy(), 44100, block_size=0.1 + ) + except: + wav_reconstruction = wav_reconstruction.cpu().float().numpy() + + try: + wav_prediction = loudness_norm( + wav_prediction.cpu().float().numpy(), 44100, block_size=0.1 + ) + except: + wav_prediction = wav_prediction.cpu().float().numpy() wav_reconstruction = torch.from_numpy(wav_reconstruction) wav_prediction = torch.from_numpy(wav_prediction) diff --git a/pdm.lock b/pdm.lock index 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"tensorboard<3.0.0,>=2.11.2", @@ -38,6 +38,11 @@ dependencies = [ "pre-commit>=3.3.3", "onnx>=1.14.0", "loralib>=0.1.1", + "diffusers>=0.21.2", + "deepspeed>=0.10.3", + "sentencepiece>=0.1.99", + "datasets>=2.14.5", + "accelerate>=0.23.0", ] [tool.pdm] diff --git a/run.sh b/run.sh new file mode 100644 index 00000000..293d51c2 --- /dev/null +++ b/run.sh @@ -0,0 +1 @@ +python tools/diffusion/train.py --config configs/tts_baseline.py --entity fish-audio --tensorboard diff --git a/tools/diffusion/inference.py b/tools/diffusion/inference.py index bd73076a..d3559ddf 100644 --- a/tools/diffusion/inference.py +++ b/tools/diffusion/inference.py @@ -54,11 +54,13 @@ def __init__(self, config, checkpoint, model_cls=DiffSingerLightning): self.text_features_extractor = FEATURE_EXTRACTORS.build( config.preprocessing.text_features_extractor ) - self.pitch_extractor = PITCH_EXTRACTORS.build( - config.preprocessing.pitch_extractor - ) - if hasattr(config.preprocessing, "energy_extractor"): + if getattr(config.preprocessing, "pitch_extractor", None): + self.pitch_extractor = PITCH_EXTRACTORS.build( + config.preprocessing.pitch_extractor + ) + + if getattr(config.preprocessing, "energy_extractor", None): self.energy_extractor = ENERGY_EXTRACTORS.build( config.preprocessing.energy_extractor ) @@ -102,15 +104,16 @@ def forward( mel_len = audio.shape[-1] // 512 # Extract and process pitch - if pitches is None: - pitches = self.pitch_extractor(audio, sr, pad_to=mel_len).float() - else: - pitches = repeat_expand(pitches, mel_len) + if hasattr(self, "pitch_extractor") and self.pitch_extractor is not None: + if pitches is None: + pitches = self.pitch_extractor(audio, sr, pad_to=mel_len).float() + else: + pitches = repeat_expand(pitches, mel_len) - if (pitches == 0).all(): - return np.zeros((audio.shape[-1],)) + if (pitches == 0).all(): + return np.zeros((audio.shape[-1],)) - pitches *= 2 ** (pitch_adjust / 12) + pitches *= 2 ** (pitch_adjust / 12) # Extract and process text features text_features = self.text_features_extractor(audio, sr)[0] @@ -141,7 +144,7 @@ def forward( contents_max_len=max(contents_lens), mel_lens=contents_lens, mel_max_len=max(contents_lens), - pitches=pitches[None].to(self.device), + pitches=pitches[None].to(self.device) if pitches is not None else None, pitch_shift=pitch_shift, energy=energy, ) diff --git a/tools/diffusion/onnx/export.py b/tools/diffusion/onnx/export.py index 82ff04ae..d67349f8 100644 --- a/tools/diffusion/onnx/export.py +++ b/tools/diffusion/onnx/export.py @@ -105,7 +105,9 @@ def export_diffusion(config, model, device): step = torch.randint( 0, config.model.diffusion.timesteps, (1,), device=device, dtype=torch.long ) - cond = torch.randn((1, config.hidden_size, n_frames), device=device) + cond = torch.randn( + (1, config.model.diffusion.denoiser.condition_dim, n_frames), device=device + ) model.diffusion.denoise_fn = torch.jit.trace( model.diffusion.denoise_fn, (x, step, cond), check_trace=True @@ -146,7 +148,9 @@ def export_diffusion(config, model, device): logger.info("PLMS noise predictor traced.") - condition = torch.rand((1, 20, config.hidden_size), device=device) + condition = torch.rand( + (1, 20, config.model.diffusion.denoiser.condition_dim), device=device + ) sampler_interval = torch.tensor(100, dtype=torch.long, device=device) torch.manual_seed(0) diff --git a/tools/preprocessing/extract_features.py b/tools/preprocessing/extract_features.py index 405140ec..c670ba0a 100644 --- a/tools/preprocessing/extract_features.py +++ b/tools/preprocessing/extract_features.py @@ -48,7 +48,7 @@ def init( logger.info(f"Rank {rank} uses device {device}") text_features_extractor = None - if hasattr(config.preprocessing, "text_features_extractor"): + if getattr(config.preprocessing, "text_features_extractor", None): text_features_extractor = FEATURE_EXTRACTORS.build( config.preprocessing.text_features_extractor ) @@ -56,7 +56,7 @@ def init( text_features_extractor.eval() pitch_extractor = None - if hasattr(config.preprocessing, "pitch_extractor"): + if getattr(config.preprocessing, "pitch_extractor", None): if config.preprocessing.pitch_extractor.type == "CrepePitchExtractor": torchcrepe.load.model(device, "full") @@ -65,7 +65,7 @@ def init( pitch_extractor.eval() energy_extractor = None - if hasattr(config.preprocessing, "energy_extractor"): + if getattr(config.preprocessing, "energy_extractor", None): energy_extractor = ENERGY_EXTRACTORS.build( config.preprocessing.energy_extractor ) @@ -73,7 +73,7 @@ def init( energy_extractor.eval() vocoder = None - if hasattr(config.model, "vocoder"): + if getattr(config.model, "vocoder", None): vocoder = VOCODERS.build(config.model.vocoder) vocoder.to(device) vocoder.eval() @@ -149,6 +149,8 @@ def process( if config.model.type == "DiffSinger": contents, phones2mel = text_features_extractor(audio_path, mel_length) sample["phones2mel"] = phones2mel.cpu().numpy() + if config.model.type == "GradTTS": + contents = text_features_extractor(audio_path) else: contents = text_features_extractor(audio, sr)[0] contents = repeat_expand(contents, mel_length) @@ -212,7 +214,9 @@ def safe_process(args, config, audio_path: Path): return aug_count + 1 except Exception as e: logger.error(f"Error processing {audio_path}") - logger.exception(e) + + if args.debug: + logger.exception(e) def parse_args(): @@ -223,6 +227,7 @@ def parse_args(): parser.add_argument("--clean", action="store_true") parser.add_argument("--num-workers", type=int, default=1) parser.add_argument("--no-augmentation", action="store_true") + parser.add_argument("--debug", action="store_true") return parser.parse_args() diff --git a/tools/tts/batch/clean_wenet_speech.py b/tools/tts/batch/clean_wenet_speech.py new file mode 100644 index 00000000..3d73b429 --- /dev/null +++ b/tools/tts/batch/clean_wenet_speech.py @@ -0,0 +1,119 @@ +import json +import os +import subprocess +import tempfile +import time +from pathlib import Path + +import librosa +import soundfile as sf +import torch +import torchaudio +from fish_audio_preprocess.utils.separate_audio import ( + init_model, + merge_tracks, + separate_audio, +) +from tqdm import tqdm + +rank = int(os.environ.get("SLURM_PROCID", 0)) +world_size = int(os.environ.get("SLURM_NTASKS", 1)) +device = torch.device("cuda:0") +print(f"Rank {rank}/{world_size} on {device}") + + +def main(): + meta_path = Path("dataset/tts/WenetSpeech/WenetSpeech.json") + dataset_path = Path("dataset/tts/WenetSpeech") + cleaned_path = Path("dataset/tts/WenetSpeech/cleaned") + if not cleaned_path.exists(): + cleaned_path.mkdir(parents=True) + + demucs = init_model("htdemucs", device) + print("Model loaded") + + with open(meta_path) as f: + dataset = json.load(f)["audios"] + + print(f"Dataset loaded, {len(dataset)} samples") + dataset = dataset[rank::world_size] + print(f"Dataset split, {len(dataset)} samples") + + for data_idx, data in enumerate(dataset): + done_path = cleaned_path / data["aid"] / "done" + done_path.parent.mkdir(parents=True, exist_ok=True) + + if done_path.exists(): + continue + + print(f"Processing {data_idx}/{len(dataset)} at rank {rank}") + + try: + with tempfile.NamedTemporaryFile(suffix=".wav") as f: + subprocess.check_call( + [ + "ffmpeg", + "-y", + "-i", + str(dataset_path / data["path"]), + "-c:a", + "pcm_s16le", + "-threads", + "0", + "-ar", + "24000", + str(f.name), + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + raw_audio, sr = librosa.load(f.name, sr=None, mono=True) + + raw_audio = torch.from_numpy(raw_audio[None]).to(device) + audio = torchaudio.functional.resample( + raw_audio, orig_freq=sr, new_freq=demucs.samplerate + ) + # Make it 2 channels + audio = torch.cat([audio, audio], dim=0) + tracks = separate_audio( + demucs, audio, shifts=1, num_workers=0, progress=False + ) + audio = merge_tracks(tracks, filter=["vocals"])[0] + vocals, sr = ( + torchaudio.functional.resample( + audio, orig_freq=demucs.samplerate, new_freq=24000 + ), + 24000, + ) + vocals = vocals.cpu().numpy() + + for idx, segment in enumerate(data["segments"]): + if segment["confidence"] <= 0.95: + continue + + # Load audio + begin = int(segment["begin_time"] * sr) + end = int(segment["end_time"] * sr) + segment_audio = vocals[begin:end] + + # Write audio + temp_path = cleaned_path / data["aid"] / f"S{idx:05d}.wav" + temp_path.parent.mkdir(parents=True, exist_ok=True) + sf.write(temp_path, segment_audio, samplerate=sr) + + # Write text + temp_path = temp_path.with_suffix(".txt") + temp_path.write_text(segment["text"]) + + # Write done file + done_path.write_text("") + except Exception as e: + print(f"Error {e} on {data_idx}/{len(dataset)} at rank {rank}") + time.sleep(10) + continue + + print("Done") + + +if __name__ == "__main__": + main() diff --git a/tools/tts/batch/convert_to_wav.py b/tools/tts/batch/convert_to_wav.py new file mode 100644 index 00000000..e326b70b --- /dev/null +++ b/tools/tts/batch/convert_to_wav.py @@ -0,0 +1,65 @@ +import random +import subprocess +from multiprocessing import Pool, cpu_count +from pathlib import Path + +from tqdm import tqdm + + +def convert_to_wav(src_file_path): + dst_file_path = dst_dir / src_file_path.relative_to(src_dir).with_suffix(".wav") + dst_file_path.parent.mkdir(parents=True, exist_ok=True) + + if dst_file_path.exists(): + return True + + try: + subprocess.check_call( + [ + "ffmpeg", + "-y", + "-i", + str(src_file_path), + "-c:a", + "pcm_s16le", + "-threads", + "0", + "-ar", + "24000", + str(dst_file_path), + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + + # remove the output file + dst_file_path.unlink() + return True + except subprocess.CalledProcessError: + return False + + +src_dir = Path("dataset/tts/WenetSpeech/audio/") +dst_dir = Path("dataset/tts/WenetSpeech/audio_wav/") + +opus_files = list(src_dir.rglob("*.opus")) +random.shuffle(opus_files) +print(f"Found {len(opus_files)} opus files") + +success_counter = 0 +fail_counter = 0 + +with Pool(processes=cpu_count() * 2, maxtasksperchild=100) as pool: + with tqdm( + pool.imap_unordered(convert_to_wav, opus_files), total=len(opus_files) + ) as pbar: + for success in pbar: + if success: + success_counter += 1 + else: + fail_counter += 1 + + pbar.set_description(f"Success: {success_counter}, Fail: {fail_counter}") + +print(f"Successfully converted: {success_counter}") +print(f"Failed conversions: {fail_counter}") diff --git a/tools/tts/batch/launch.py b/tools/tts/batch/launch.py new file mode 100644 index 00000000..e72fe1ed --- /dev/null +++ b/tools/tts/batch/launch.py @@ -0,0 +1,27 @@ +import os +import subprocess as sp +import sys + +SLURM_NTASKS = 6 + +processes = [] +for i in range(SLURM_NTASKS): + env = os.environ.copy() + env["SLURM_PROCID"] = str(i) + env["SLURM_NTASKS"] = str(SLURM_NTASKS) + env["CUDA_VISIBLE_DEVICES"] = str((i + 1) % 8) + + processes.append( + sp.Popen( + f"python tools/tts/clean_wenet_speech.py", + shell=True, + env=env, + stdout=sys.stdout, + stderr=sys.stderr, + ) + ) + + +for p in processes: + p.wait() + print(p.communicate()) diff --git a/tools/tts/batch/to_flac.py b/tools/tts/batch/to_flac.py new file mode 100644 index 00000000..b1f4e92d --- /dev/null +++ b/tools/tts/batch/to_flac.py @@ -0,0 +1,60 @@ +import random +import subprocess +from multiprocessing import Pool, cpu_count +from pathlib import Path + +from tqdm import tqdm + + +def convert_to_flac(src_file_path): + dst_file_path = src_file_path.with_suffix(".flac") + dst_file_path.parent.mkdir(parents=True, exist_ok=True) + + try: + subprocess.check_call( + [ + "ffmpeg", + "-y", + "-i", + str(src_file_path), + "-acodec", + "flac", + "-threads", + "0", + str(dst_file_path), + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + + # remove the input file + src_file_path.unlink() + return True + except subprocess.CalledProcessError: + return False + + +if __name__ == "__main__": + src_dir = Path("dataset/tts/WenetSpeech/cleaned") + + wav_files = list(src_dir.rglob("*.wav")) + random.shuffle(wav_files) + print(f"Found {len(wav_files)} wav files") + + success_counter = 0 + fail_counter = 0 + + with Pool(processes=cpu_count(), maxtasksperchild=100) as pool: + with tqdm( + pool.imap_unordered(convert_to_flac, wav_files), total=len(wav_files) + ) as pbar: + for success in pbar: + if success: + success_counter += 1 + else: + fail_counter += 1 + + pbar.set_description(f"Success: {success_counter}, Fail: {fail_counter}") + + print(f"Successfully converted: {success_counter}") + print(f"Failed conversions: {fail_counter}") diff --git a/tools/tts/ds_config.json b/tools/tts/ds_config.json new file mode 100644 index 00000000..388fdfef --- /dev/null +++ b/tools/tts/ds_config.json @@ -0,0 +1,22 @@ +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu" :"auto", + "gradient_accumulation_steps": "auto", + "gradient_clipping": 1.0, + "bf16": { + "enabled": "auto" + }, + "zero_optimization": { + "stage": 3, + "overlap_comm": true, + "stage3_gather_16bit_weights_on_model_save": true + }, + "flops_profiler": { + "enabled": false, + "profile_step": 1, + "module_depth": -1, + "top_modules": 1, + "detailed": true, + "output_file": null + } +} \ No newline at end of file diff --git a/tools/tts/extend_vocab.py b/tools/tts/extend_vocab.py new file mode 100644 index 00000000..dd8315d0 --- /dev/null +++ b/tools/tts/extend_vocab.py @@ -0,0 +1,48 @@ +import math + +import torch +from torch import nn +from transformers import AutoModelForCausalLM, AutoTokenizer + +# pick the model type +model_type = "baichuan-inc/Baichuan2-7B-Base" +tokenizer = AutoTokenizer.from_pretrained( + model_type, use_fast=False, trust_remote_code=True +) +model = AutoModelForCausalLM.from_pretrained(model_type, trust_remote_code=True) + + +def _get_resized_lm_head( + old_lm_head, new_num_tokens=None, transposed=None +) -> nn.Linear: + new_weights = torch.empty( + (new_num_tokens - old_lm_head.weight.shape[0], old_lm_head.weight.shape[1]) + ) + nn.init.kaiming_normal_(new_weights, a=math.sqrt(5)) + + old_lm_head.weight.data = torch.cat((old_lm_head.weight.data, new_weights), dim=0) + + return old_lm_head + + +model._get_resized_lm_head = _get_resized_lm_head + +# new tokens +new_tokens = [f"" for i in range(1024)] + +# add the tokens to the tokenizer vocabulary +tokenizer.add_tokens(new_tokens) + +# add new, random embeddings for the new tokens +model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=8) + +# Try tokenizing a new sequence +sequence = "Test " +encoded = tokenizer.encode(sequence) +print(encoded) + +model.save_pretrained("./checkpoints/baichuan2-7b-base-extend") +tokenizer.save_pretrained("./checkpoints/baichuan2-7b-base-extend") + +tokenizer.push_to_hub("fishaudio/baichuan2-7b-extend") +model.push_to_hub("fishaudio/baichuan2-7b-extend") diff --git a/tools/tts/fine-tune.py b/tools/tts/fine-tune.py new file mode 100644 index 00000000..d04516e5 --- /dev/null +++ b/tools/tts/fine-tune.py @@ -0,0 +1,91 @@ +from dataclasses import dataclass, field +from typing import Optional + +from datasets import load_dataset, load_from_disk +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + HfArgumentParser, + Trainer, +) +from transformers import TrainingArguments as _TrainingArguments + + +@dataclass +class ModelArguments: + model_name_or_path: Optional[str] = field(default="baichuan-inc/Baichuan2-7B-Base") + + +@dataclass +class DataArguments: + data_path: str = field( + default=None, metadata={"help": "Path to the training data."} + ) + + +@dataclass +class TrainingArguments(_TrainingArguments): + cache_dir: Optional[str] = field(default=None) + optim: str = field(default="adamw_torch") + model_max_length: int = field( + default=512, + metadata={ + "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)." + }, + ) + use_lora: bool = field(default=False) + + +def train(): + parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + model = AutoModelForCausalLM.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=True, + cache_dir=training_args.cache_dir, + ) + + tokenizer = AutoTokenizer.from_pretrained( + model_args.model_name_or_path, + use_fast=False, + trust_remote_code=True, + model_max_length=training_args.model_max_length, + cache_dir=training_args.cache_dir, + ) + + if training_args.use_lora: + from peft import LoraConfig, TaskType, get_peft_model + + peft_config = LoraConfig( + task_type=TaskType.CAUSAL_LM, + target_modules=["W_pack"], + inference_mode=False, + r=16, + lora_alpha=64, + lora_dropout=0.1, + ) + model.enable_input_require_grads() + model = get_peft_model(model, peft_config) + model.print_trainable_parameters() + + try: + dataset = load_from_disk(data_args.data_path) + except: + dataset = load_dataset(data_args.data_path, split="train") + + trainer = Trainer( + model=model, + args=training_args, + train_dataset=dataset, + tokenizer=tokenizer, + data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False), + ) + trainer.train() + trainer.save_state() + trainer.save_model(output_dir=training_args.output_dir) + + +if __name__ == "__main__": + train() diff --git a/tools/tts/finetune.sh b/tools/tts/finetune.sh new file mode 100755 index 00000000..132b7191 --- /dev/null +++ b/tools/tts/finetune.sh @@ -0,0 +1,26 @@ +hostfile="" +deepspeed --hostfile=$hostfile tools/tts/fine-tune.py \ + --deepspeed tools/tts/ds_config.json \ + --report_to "tensorboard" \ + --data_path "fishaudio/libritts-r-tokenized" \ + --model_name_or_path "checkpoints/baichuan2-7b-base-extend" \ + --output_dir "results" \ + --model_max_length 2048 \ + --num_train_epochs 5 \ + --per_device_train_batch_size 16 \ + --gradient_accumulation_steps 1 \ + --save_strategy steps \ + --save_steps 1000 \ + --learning_rate 2e-4 \ + --lr_scheduler_type cosine \ + --adam_beta1 0.9 \ + --adam_beta2 0.98 \ + --adam_epsilon 1e-8 \ + --max_grad_norm 1.0 \ + --weight_decay 1e-4 \ + --warmup_ratio 0.05 \ + --logging_steps 1 \ + --gradient_checkpointing True \ + --use_lora False \ + --bf16 True \ + --tf32 True