Medix-AI/coqui-tts
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coqui-tts
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adjusted script for afrotts finetuning

This commit is contained in:
owos 2024-03-04 14:04:24 +00:00
parent 6148baa952
commit 0a05795334
1 changed files with 18 additions and 17 deletions
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35
recipes/ljspeech/xtts_v2/train_gpt_xtts.py
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@ -8,7 +8,7 @@ from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrai
from TTS.utils.manage import ModelManager
# Logging parameters
RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT"
RUN_NAME = "GPT_XTTS_v2.0_AfroTTS_FT"
PROJECT_NAME = "XTTS_trainer"
DASHBOARD_LOGGER = "tensorboard"
LOGGER_URI = None
@ -18,17 +18,18 @@ OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "trai
# Training Parameters
OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False
START_WITH_EVAL = True # if True it will star with evaluation
BATCH_SIZE = 3 # set here the batch size
GRAD_ACUMM_STEPS = 84 # set here the grad accumulation steps
START_WITH_EVAL = False # if True it will star with evaluation
BATCH_SIZE = 16 # set here the batch size
GRAD_ACUMM_STEPS = 4 # set here the grad accumulation steps
# 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.
# Define here the dataset that you want to use for the fine-tuning on.
config_dataset = BaseDatasetConfig(
formatter="ljspeech",
dataset_name="ljspeech",
path="/raid/datasets/LJSpeech-1.1_24khz/",
meta_file_train="/raid/datasets/LJSpeech-1.1_24khz/metadata.csv",
formatter="afrotts",
dataset_name="afrotts",
path="/data4/data/AfriSpeech-TTS-D/",
meta_file_train="/data4/abraham/tts/AfriSpeech-TTS/data/afritts-train-clean.csv",
meta_file_val="/data4/abraham/tts/AfriSpeech-TTS/data/afritts-dev-clean.csv",
language="en",
)
@ -72,7 +73,7 @@ if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
# Training sentences generations
SPEAKER_REFERENCE = [
"./tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences
"/data4/data/AfriSpeech-TTS-D/train/1dddeb9f-18ec-4498-b74b-84ac59f2fcf1/e9af9831281555e8685e511f7becdf32_P2L385Vp.wav" # speaker reference to be used in training test sentences
]
LANGUAGE = config_dataset.language
@ -83,8 +84,8 @@ def main():
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,
max_wav_length=255995, # ~11.6 seconds 661500, #~ 30 seconds #
max_text_length=300,
mel_norm_file=MEL_NORM_FILE,
dvae_checkpoint=DVAE_CHECKPOINT,
xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune
@ -110,18 +111,18 @@ def main():
logger_uri=LOGGER_URI,
audio=audio_config,
batch_size=BATCH_SIZE,
batch_group_size=48,
batch_group_size=64,
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,
log_model_step=100,
save_step=1000,
save_n_checkpoints=3,
save_checkpoints=True,
# target_loss="loss",
print_eval=False,
print_eval=True,
# Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
optimizer="AdamW",
optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
@ -154,7 +155,6 @@ def main():
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(
@ -174,3 +174,4 @@ def main():
if __name__ == "__main__":
main()
DATASETS_CONFIG_LIST