mirror of https://github.com/coqui-ai/TTS.git
Update LJspeech XTTS recipe
Edresson Casanova
parent
9e3598c3b7
commit
469d624615
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@ -1,11 +1,29 @@
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import os
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from trainer import Trainer, TrainerArgs
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
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# Define here the dataset used
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config_ljspeech = BaseDatasetConfig(
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# Logging parameters
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RUN_NAME = "GPT_XTTS_LJSpeech_FT"
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PROJECT_NAME = "XTTS_trainer"
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DASHBOARD_LOGGER = "tensorboard"
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LOGGER_URI = None
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# Set here the path that the checkpoints will be saved. Default: ./run/training/
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OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "training")
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# Training Parameters
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OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False
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START_WITH_EVAL = True # if True it will star with evaluation
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BATCH_SIZE = 3 # set here the batch size
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GRAD_ACUMM_STEPS = 84 # set here the grad accumulation steps
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# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly.
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# Define here the dataset that you want to use for the fine tuning
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config_dataset = BaseDatasetConfig(
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formatter="ljspeech",
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dataset_name="ljspeech",
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path="/raid/datasets/LJSpeech-1.1_24khz/",
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@ -13,11 +31,26 @@ config_ljspeech = BaseDatasetConfig(
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language="en",
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)
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DATASETS_CONFIG_LIST = [config_ljspeech]
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DATASETS_CONFIG_LIST = [config_dataset]
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# ToDo: update with the latest released checkpoints
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# DVAE parameters: For the training we need the dvae to extract the dvae tokens, given that you must provide the paths for this model
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DVAE_CHECKPOINT = "/raid/datasets/xtts_models/dvae.pth" # DVAE checkpoint
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MEL_NORM_FILE = (
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"/raid/datasets/xtts_models/mel_stats.pth" # Mel spectrogram norms, required for dvae mel spectrogram extraction
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)
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# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
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TOKENIZER_FILE = "/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_style_emb_repetition_fix_gt/tokenizer_merged_5.json" # vocab.json file
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XTTS_CHECKPOINT = "/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_style_emb_repetition_fix_gt/132500_gpt_ema_coqui_tts_with_enhanced_hifigan.pth" # model.pth file
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def freeze_layers(trainer):
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pass
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# Training sentences generations
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SPEAKER_REFERENCE = (
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"./tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences
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)
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LANGUAGE = config_dataset.language
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def main():
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@ -28,18 +61,18 @@ def main():
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debug_loading_failures=False,
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max_wav_length=255995, # ~11.6 seconds
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max_text_length=200,
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mel_norm_file="/raid/datasets/xtts_models/mel_stats.pth",
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dvae_checkpoint="/raid/datasets/xtts_models/dvae.pth",
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mel_norm_file=MEL_NORM_FILE,
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dvae_checkpoint=DVAE_CHECKPOINT,
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# tokenizer_file="/raid/datasets/xtts_models/vocab.json", # vocab path of the model that you want to fine-tune
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# xtts_checkpoint="https://huggingface.co/coqui/XTTS-v1/resolve/hifigan/model.pth",
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xtts_checkpoint="/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_style_emb_repetition_fix_gt/132500_gpt_ema_coqui_tts_with_enhanced_hifigan.pth", # checkpoint path of the model that you want to fine-tune
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tokenizer_file="/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_style_emb_repetition_fix_gt/tokenizer_merged_5.json",
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xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune
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tokenizer_file=TOKENIZER_FILE,
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gpt_num_audio_tokens=8194,
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gpt_start_audio_token=8192,
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gpt_stop_audio_token=8193,
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)
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audio_config = XttsAudioConfig(
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sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000 # GPT SR
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sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000
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)
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config = GPTTrainerConfig(
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output_path=OUT_PATH,
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@ -67,7 +100,7 @@ def main():
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print_eval=False,
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# Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
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optimizer="AdamW",
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optimizer_wd_only_on_weights=True, # for multi-gpu training turn it off
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optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
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optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
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lr=5e-06, # learning rate
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lr_scheduler="MultiStepLR",
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@ -76,18 +109,13 @@ def main():
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test_sentences=[
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{
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"text": "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
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"speaker_wav": "/raid/edresson/dev/ref-ljspeech.wav",
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"language": "en",
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"speaker_wav": SPEAKER_REFERENCE,
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"language": LANGUAGE,
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},
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{
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"text": "This cake is great. It's so delicious and moist.",
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"speaker_wav": "/raid/edresson/dev/ref-ljspeech.wav",
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"language": "en",
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},
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{
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"text": "Levei muito tempo para desenvolver uma voz e agora que a tenho não vou ficar calado .",
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"speaker_wav": "/raid/edresson/dev/ref-ljspeech.wav",
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"language": "pt",
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"speaker_wav": SPEAKER_REFERENCE,
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"language": LANGUAGE,
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},
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],
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)
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@ -106,8 +134,8 @@ def main():
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# init the trainer and 🚀
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trainer = Trainer(
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TrainerArgs(
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restore_path=RESTORE_PATH,
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skip_train_epoch=SKIP_TRAIN_EPOCH,
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restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
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skip_train_epoch=False,
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start_with_eval=START_WITH_EVAL,
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grad_accum_steps=GRAD_ACUMM_STEPS,
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),
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@ -116,30 +144,9 @@ def main():
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model=model,
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train_samples=train_samples,
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eval_samples=eval_samples,
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callbacks={"on_epoch_start": freeze_layers},
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)
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trainer.fit()
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if __name__ == "__main__":
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RUN_NAME = "GPT_XTTS_LJSpeech_fixed"
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PROJECT_NAME = "XTTS_trainer"
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OUT_PATH = "/raid/edresson/dev/Checkpoints/XTTS_v1_FT/"
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# DASHBOARD_LOGGER = "clearml"
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# LOGGER_URI = "s3://coqui-ai-models/TTS/Checkpoints/XTTS_v1/"
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DASHBOARD_LOGGER = "tensorboard"
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LOGGER_URI = None
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RESTORE_PATH = None
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SKIP_TRAIN_EPOCH = False
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START_WITH_EVAL = True
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BATCH_SIZE = 3
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GRAD_ACUMM_STEPS = 28 * 3
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# debug
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# DASHBOARD_LOGGER = "tensorboard"
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# LOGGER_URI = None
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# RESTORE_PATH = None
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# BATCH_SIZE = 2
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# GRAD_ACUMM_STEPS = 1
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main()
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@ -1,376 +0,0 @@
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from trainer import Trainer, TrainerArgs
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
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config_coqui_MLS_metadata_train_with_previous_audio_key_de = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_german",
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meta_file_train="metadata_train_with_previous_audio_key.csv",
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language="de",
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)
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config_coqui_MLS_metadata_test_with_previous_audio_key_de = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_german",
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meta_file_train="metadata_test_with_previous_audio_key.csv",
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language="de",
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)
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config_coqui_MLS_metadata_dev_with_previous_audio_key_de = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_german",
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meta_file_train="metadata_dev_with_previous_audio_key.csv",
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language="de",
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)
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config_coqui_mls_french_metadata_with_previous_audio_key_fr = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_french/",
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meta_file_train="metadata_with_previous_audio_key.csv",
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language="fr",
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)
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config_coqui_mls_spanish_metadata_with_previous_audio_key_es = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_spanish/",
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meta_file_train="/raid/datasets/MLS/mls_spanish/metadata_with_previous_audio_key.csv",
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language="es",
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)
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config_coqui_mls_italian_metadata_with_previous_audio_key_it = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_italian/",
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meta_file_train="/raid/datasets/MLS/mls_italian/metadata_with_previous_audio_key.csv",
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language="it",
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)
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config_coqui_mls_portuguese_metadata_with_previous_audio_key_pt = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_portuguese/",
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meta_file_train="/raid/datasets/MLS/mls_portuguese/metadata_with_previous_audio_key.csv",
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language="pt",
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)
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config_coqui_mls_polish_metadata_with_previous_audio_key_pl = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/MLS/mls_polish/",
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meta_file_train="/raid/datasets/MLS/mls_polish/metadata_with_previous_audio_key.csv",
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language="pl",
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)
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config_coqui_common_voice_metafile_it_train_with_scores_it = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_it_train_with_scores.csv",
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language="it",
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)
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config_coqui_common_voice_metafile_it_test_with_scores_it = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_it_test_with_scores.csv",
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language="it",
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)
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config_coqui_common_voice_metafile_it_dev_with_scores_it = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_it_dev_with_scores.csv",
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language="it",
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)
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config_coqui_common_voice_metafile_pt_train_with_scores_pt = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_pt_train_with_scores.csv",
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language="pt",
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)
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config_coqui_common_voice_metafile_pt_test_with_scores_pt = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_pt_test_with_scores.csv",
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language="pt",
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)
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config_coqui_common_voice_metafile_pt_dev_with_scores_pt = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_pt_dev_with_scores.csv",
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language="pt",
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)
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config_coqui_common_voice_metafile_en_train_en = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_en_train.csv",
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language="en",
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)
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config_coqui_common_voice_metafile_en_test_en = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_en_test.csv",
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language="en",
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)
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config_coqui_common_voice_metafile_en_dev_en = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_en_dev.csv",
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language="en",
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)
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config_coqui_common_voice_metafile_tr_validated_tr = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_tr_validated.csv",
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language="tr",
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)
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config_coqui_common_voice_metafile_ru_validated_ru = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_ru_validated.csv",
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language="ru",
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)
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config_coqui_common_voice_metafile_nl_validated_nl = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_nl_validated.csv",
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language="nl",
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)
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config_coqui_common_voice_metafile_cs_validated_cs = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_cs_validated.csv",
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language="cs",
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)
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config_coqui_common_voice_metafile_fr_validated_fr = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_fr_validated.csv",
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language="fr",
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)
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config_coqui_common_voice_metafile_es_validated_es = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_es_validated.csv",
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language="es",
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)
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config_coqui_common_voice_metafile_pl_validated_pl = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_pl_validated.csv",
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language="pl",
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)
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config_coqui_common_voice_metafile_ar_validated_ar = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_ar_validated.csv",
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language="ar",
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)
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config_coqui_common_voice_metafile_zh_CN_validated_zh_cn = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_zh-CN_validated.csv",
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language="zh-cn",
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)
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config_coqui_common_voice_metafile_ja_validated_ja = BaseDatasetConfig(
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formatter="coqui",
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dataset_name="coqui",
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path="/raid/datasets/common_voice/",
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meta_file_train="/raid/datasets/common_voice/metafile_ja_validated.csv",
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language="ja",
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)
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# DATASETS_CONFIG_LIST=[config_coqui_MLS_metadata_train_with_previous_audio_key_de, config_coqui_MLS_metadata_test_with_previous_audio_key_de, config_coqui_MLS_metadata_dev_with_previous_audio_key_de, config_coqui_mls_french_metadata_with_previous_audio_key_fr, config_coqui_mls_spanish_metadata_with_previous_audio_key_es, config_coqui_mls_italian_metadata_with_previous_audio_key_it, config_coqui_mls_portuguese_metadata_with_previous_audio_key_pt, config_coqui_mls_polish_metadata_with_previous_audio_key_pl, config_coqui_common_voice_metafile_it_train_with_scores_it, config_coqui_common_voice_metafile_it_test_with_scores_it, config_coqui_common_voice_metafile_it_dev_with_scores_it, config_coqui_common_voice_metafile_pt_train_with_scores_pt, config_coqui_common_voice_metafile_pt_test_with_scores_pt, config_coqui_common_voice_metafile_pt_dev_with_scores_pt, config_coqui_common_voice_metafile_en_train_en, config_coqui_common_voice_metafile_en_test_en, config_coqui_common_voice_metafile_en_dev_en, config_coqui_common_voice_metafile_tr_validated_tr, config_coqui_common_voice_metafile_ru_validated_ru, config_coqui_common_voice_metafile_nl_validated_nl, config_coqui_common_voice_metafile_cs_validated_cs, config_coqui_common_voice_metafile_fr_validated_fr, config_coqui_common_voice_metafile_es_validated_es, config_coqui_common_voice_metafile_pl_validated_pl, config_coqui_common_voice_metafile_ar_validated_ar, config_coqui_common_voice_metafile_zh_CN_validated_zh_cn, config_coqui_common_voice_metafile_ja_validated_ja]
|
||||
|
||||
# DATASETS_CONFIG_LIST = [config_coqui_mls_french_metadata_with_previous_audio_key_fr, config_coqui_MLS_metadata_test_with_previous_audio_key_de, config_coqui_mls_spanish_metadata_with_previous_audio_key_es, config_coqui_mls_italian_metadata_with_previous_audio_key_it]
|
||||
|
||||
DATASETS_CONFIG_LIST = [
|
||||
config_coqui_MLS_metadata_test_with_previous_audio_key_de,
|
||||
config_coqui_mls_italian_metadata_with_previous_audio_key_it,
|
||||
]
|
||||
|
||||
|
||||
def freeze_layers(trainer):
|
||||
pass
|
||||
|
||||
|
||||
def main():
|
||||
# init args and config
|
||||
model_args = GPTArgs(
|
||||
max_conditioning_length=132300, # 6 secs
|
||||
min_conditioning_length=66150, # 3 secs
|
||||
debug_loading_failures=False,
|
||||
max_wav_length=255995, # ~11.6 seconds
|
||||
max_text_length=200,
|
||||
mel_norm_file="/raid/datasets/xtts_models/mel_stats.pth",
|
||||
dvae_checkpoint="/raid/datasets/xtts_models/dvae.pth",
|
||||
tokenizer_file="/raid/datasets/xtts_models/vocab.json", # vocab path of the model that you want to fine-tune
|
||||
xtts_checkpoint="https://huggingface.co/coqui/XTTS-v1/resolve/hifigan/model.pth", # checkpoint path of the model that you want to fine-tune
|
||||
gpt_num_audio_tokens=8194,
|
||||
gpt_start_audio_token=8192,
|
||||
gpt_stop_audio_token=8193,
|
||||
)
|
||||
audio_config = XttsAudioConfig(
|
||||
sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000 # GPT SR
|
||||
)
|
||||
config = GPTTrainerConfig(
|
||||
output_path=OUT_PATH,
|
||||
model_args=model_args,
|
||||
run_name=RUN_NAME,
|
||||
project_name=PROJECT_NAME,
|
||||
run_description="""
|
||||
GPT XTTS training
|
||||
""",
|
||||
dashboard_logger=DASHBOARD_LOGGER,
|
||||
logger_uri=LOGGER_URI,
|
||||
audio=audio_config,
|
||||
batch_size=BATCH_SIZE,
|
||||
batch_group_size=48,
|
||||
eval_batch_size=BATCH_SIZE,
|
||||
num_loader_workers=8,
|
||||
eval_split_max_size=256,
|
||||
print_step=50,
|
||||
plot_step=100,
|
||||
log_model_step=1000,
|
||||
save_step=10000,
|
||||
save_n_checkpoints=1,
|
||||
save_checkpoints=True,
|
||||
# target_loss="loss",
|
||||
print_eval=False,
|
||||
# Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
|
||||
optimizer="AdamW",
|
||||
optimizer_wd_only_on_weights=True, # for multi-gpu training turn it off
|
||||
optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
|
||||
lr=5e-06, # learning rate
|
||||
lr_scheduler="MultiStepLR",
|
||||
# it was adjusted accordly for the new step scheme
|
||||
lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
|
||||
test_sentences=[
|
||||
{
|
||||
"text": "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
|
||||
"speaker_wav": "/raid/edresson/dev/ref.wav",
|
||||
"language": "en",
|
||||
},
|
||||
{
|
||||
"text": "This cake is great. It's so delicious and moist.",
|
||||
"speaker_wav": "/raid/edresson/dev/ref.wav",
|
||||
"language": "en",
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
# init the model from config
|
||||
model = GPTTrainer.init_from_config(config)
|
||||
|
||||
# load training samples
|
||||
train_samples, eval_samples = load_tts_samples(
|
||||
DATASETS_CONFIG_LIST,
|
||||
eval_split=True,
|
||||
eval_split_max_size=config.eval_split_max_size,
|
||||
eval_split_size=config.eval_split_size,
|
||||
)
|
||||
|
||||
# init the trainer and 🚀
|
||||
trainer = Trainer(
|
||||
TrainerArgs(
|
||||
restore_path=RESTORE_PATH,
|
||||
skip_train_epoch=SKIP_TRAIN_EPOCH,
|
||||
start_with_eval=START_WITH_EVAL,
|
||||
grad_accum_steps=GRAD_ACUMM_STEPS,
|
||||
),
|
||||
config,
|
||||
output_path=OUT_PATH,
|
||||
model=model,
|
||||
train_samples=train_samples,
|
||||
eval_samples=eval_samples,
|
||||
callbacks={"on_epoch_start": freeze_layers},
|
||||
)
|
||||
trainer.fit()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
RUN_NAME = "GPT_XTTS"
|
||||
PROJECT_NAME = "XTTS_trainer"
|
||||
OUT_PATH = "/raid/edresson/dev/Checkpoints/XTTS_style_emb/"
|
||||
DASHBOARD_LOGGER = "clearml"
|
||||
LOGGER_URI = "s3://coqui-ai-models/TTS/Checkpoints/XTTS_style_emb/"
|
||||
RESTORE_PATH = None
|
||||
SKIP_TRAIN_EPOCH = False
|
||||
START_WITH_EVAL = True
|
||||
BATCH_SIZE = 9
|
||||
GRAD_ACUMM_STEPS = 28
|
||||
|
||||
# debug
|
||||
# DASHBOARD_LOGGER = "tensorboard"
|
||||
# LOGGER_URI = None
|
||||
# RESTORE_PATH = None
|
||||
BATCH_SIZE = 2
|
||||
GRAD_ACUMM_STEPS = 1
|
||||
|
||||
main()
|
||||
Loading…
Reference in New Issue