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[TTS] Implement new TextToSpeech dataset
Signed-off-by: Ryan <rlangman@nvidia.com>
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# This config contains the default values for training a FastPitch model with aligner. | ||
# If you want to train a model on other dataset, you can change config values according to your dataset. | ||
# Most dataset-specific arguments are in the head of the config file, see below. | ||
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name: FastPitch | ||
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max_epochs: ??? | ||
batch_size: 32 | ||
weighted_sample_steps: null | ||
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n_speakers: ??? | ||
speaker_path: null | ||
feature_stats_path: null | ||
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train_ds_meta: ??? | ||
val_ds_meta: ??? | ||
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phoneme_dict_path: ??? | ||
heteronyms_path: ??? | ||
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defaults: | ||
- feature: feature_22050 | ||
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model: | ||
learn_alignment: true | ||
bin_loss_warmup_epochs: 100 | ||
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n_speakers: ${n_speakers} | ||
n_mel_channels: ${feature.mel_feature.mel_dim} | ||
max_token_duration: 75 | ||
symbols_embedding_dim: 384 | ||
pitch_embedding_kernel_size: 3 | ||
energy_embedding_kernel_size: 3 | ||
speaker_emb_condition_prosody: true | ||
speaker_emb_condition_aligner: true | ||
use_log_energy: false | ||
dur_loss_scale: 0.1 | ||
pitch_loss_scale: 0.1 | ||
energy_loss_scale: 0.1 | ||
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preprocessor: | ||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor | ||
features: ${feature.mel_feature.mel_dim} | ||
lowfreq: ${feature.mel_feature.lowfreq} | ||
highfreq: ${feature.mel_feature.highfreq} | ||
n_fft: ${feature.win_length} | ||
n_window_size: ${feature.win_length} | ||
window_size: false | ||
n_window_stride: ${feature.hop_length} | ||
window_stride: false | ||
pad_to: 1 | ||
pad_value: 0 | ||
sample_rate: ${feature.sample_rate} | ||
window: hann | ||
normalize: null | ||
preemph: null | ||
dither: 0.0 | ||
frame_splicing: 1 | ||
log: true | ||
log_zero_guard_type: add | ||
log_zero_guard_value: 1.0 | ||
mag_power: 1.0 | ||
mel_norm: null | ||
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text_tokenizer: | ||
_target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer | ||
punct: true | ||
apostrophe: true | ||
pad_with_space: true | ||
g2p: | ||
_target_: nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p | ||
phoneme_dict: ${phoneme_dict_path} | ||
heteronyms: ${heteronyms_path} | ||
phoneme_probability: 0.8 | ||
# Relies on the heteronyms list for anything that needs to be disambiguated | ||
ignore_ambiguous_words: false | ||
use_chars: true | ||
use_stresses: true | ||
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pitch_processor: | ||
_target_: nemo.collections.tts.parts.preprocessing.feature_processors.MeanVarianceSpeakerNormalization | ||
field: pitch | ||
stats_path: ${feature_stats_path} | ||
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energy_processor: | ||
_target_: nemo.collections.tts.parts.preprocessing.feature_processors.MeanVarianceSpeakerNormalization | ||
field: energy | ||
stats_path: ${feature_stats_path} | ||
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align_prior_config: | ||
_target_: nemo.collections.tts.data.text_to_speech_dataset.AlignPriorConfig | ||
hop_length: ${feature.hop_length} | ||
use_beta_binomial_interpolator: false | ||
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train_ds: | ||
dataset: | ||
_target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset | ||
dataset_meta: ${train_ds_meta} | ||
weighted_sample_steps: ${weighted_sample_steps} | ||
sample_rate: ${feature.sample_rate} | ||
speaker_path: ${speaker_path} | ||
featurizers: ${feature.featurizers} | ||
feature_processors: | ||
pitch: ${model.pitch_processor} | ||
energy: ${model.energy_processor} | ||
align_prior_config: ${model.align_prior_config} | ||
min_duration: 0.1 | ||
max_duration: 10.0 | ||
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dataloader_params: | ||
batch_size: ${batch_size} | ||
drop_last: true | ||
num_workers: 8 | ||
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validation_ds: | ||
dataset: | ||
_target_: nemo.collections.tts.data.text_to_speech_dataset.TextToSpeechDataset | ||
dataset_meta: ${val_ds_meta} | ||
sample_rate: ${feature.sample_rate} | ||
speaker_path: ${speaker_path} | ||
featurizers: ${feature.featurizers} | ||
feature_processors: | ||
pitch: ${model.pitch_processor} | ||
energy: ${model.energy_processor} | ||
align_prior_config: ${model.align_prior_config} | ||
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dataloader_params: | ||
batch_size: ${batch_size} | ||
drop_last: false | ||
num_workers: 2 | ||
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input_fft: | ||
_target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder | ||
n_layer: 6 | ||
n_head: 2 | ||
d_model: ${model.symbols_embedding_dim} | ||
d_head: 64 | ||
d_inner: 1536 | ||
kernel_size: 3 | ||
dropout: 0.1 | ||
dropatt: 0.1 | ||
dropemb: 0.0 | ||
d_embed: ${model.symbols_embedding_dim} | ||
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output_fft: | ||
_target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder | ||
n_layer: 6 | ||
n_head: 1 | ||
d_model: ${model.symbols_embedding_dim} | ||
d_head: 64 | ||
d_inner: 1536 | ||
kernel_size: 3 | ||
dropout: 0.1 | ||
dropatt: 0.1 | ||
dropemb: 0.0 | ||
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alignment_module: | ||
_target_: nemo.collections.tts.modules.aligner.AlignmentEncoder | ||
n_text_channels: ${model.symbols_embedding_dim} | ||
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duration_predictor: | ||
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor | ||
input_size: ${model.symbols_embedding_dim} | ||
kernel_size: 3 | ||
filter_size: 256 | ||
dropout: 0.1 | ||
n_layers: 2 | ||
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pitch_predictor: | ||
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor | ||
input_size: ${model.symbols_embedding_dim} | ||
kernel_size: 3 | ||
filter_size: 256 | ||
dropout: 0.1 | ||
n_layers: 2 | ||
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energy_predictor: | ||
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor | ||
input_size: ${model.symbols_embedding_dim} | ||
kernel_size: 3 | ||
filter_size: 256 | ||
dropout: 0.1 | ||
n_layers: 2 | ||
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optim: | ||
name: adamw | ||
lr: 1e-3 | ||
betas: [0.9, 0.999] | ||
weight_decay: 1e-6 | ||
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sched: | ||
name: NoamAnnealing | ||
warmup_steps: 1000 | ||
last_epoch: -1 | ||
d_model: 1 # Disable scaling based on model dim | ||
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trainer: | ||
num_nodes: 1 | ||
devices: 1 | ||
accelerator: gpu | ||
strategy: ddp | ||
precision: 16 | ||
max_epochs: ${max_epochs} | ||
accumulate_grad_batches: 1 | ||
gradient_clip_val: 10.0 | ||
enable_checkpointing: false # Provided by exp_manager | ||
logger: false # Provided by exp_manager | ||
log_every_n_steps: 100 | ||
check_val_every_n_epoch: 10 | ||
benchmark: false | ||
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exp_manager: | ||
exp_dir: null | ||
name: ${name} | ||
create_tensorboard_logger: true | ||
create_checkpoint_callback: true | ||
checkpoint_callback_params: | ||
monitor: val_loss | ||
resume_if_exists: false | ||
resume_ignore_no_checkpoint: false |
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