mirror of https://github.com/coqui-ai/TTS.git
140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
import os
|
|
|
|
from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig
|
|
|
|
# from TTS.encoder.configs.emotion_encoder_config import EmotionEncoderConfig
|
|
from TTS.tts.configs.shared_configs import BaseDatasetConfig
|
|
|
|
CURRENT_PATH = os.getcwd()
|
|
# change the root path to the TTS root path
|
|
os.chdir("../../../")
|
|
|
|
### Definitions ###
|
|
# dataset
|
|
VCTK_PATH = "/raid/datasets/VCTK_NEW_16khz_removed_silence_silero_vad/" # download: https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zipdddddddddd
|
|
RIR_SIMULATED_PATH = "/raid/datasets/DA/RIRS_NOISES/simulated_rirs/" # download: https://www.openslr.org/17/
|
|
MUSAN_PATH = "/raid/datasets/DA/musan/" # download: https://www.openslr.org/17/
|
|
|
|
# training
|
|
OUTPUT_PATH = os.path.join(
|
|
CURRENT_PATH, "resnet_speaker_encoder_training_output/"
|
|
) # path to save the train logs and checkpoint
|
|
CONFIG_OUT_PATH = os.path.join(OUTPUT_PATH, "config_se.json")
|
|
RESTORE_PATH = None # Checkpoint to use for transfer learning if None ignore
|
|
|
|
# instance the config
|
|
# to speaker encoder
|
|
config = SpeakerEncoderConfig()
|
|
# to emotion encoder
|
|
# config = EmotionEncoderConfig()
|
|
|
|
|
|
#### DATASET CONFIG ####
|
|
# The formatter need to return the key "speaker_name" for the speaker encoder and the "emotion_name" for the emotion encoder
|
|
dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", language="en-us", path=VCTK_PATH)
|
|
|
|
# add the dataset to the config
|
|
config.datasets = [dataset_config]
|
|
|
|
|
|
#### TRAINING CONFIG ####
|
|
# The encoder data loader balancer the dataset item equally to guarantee better training and to attend the losses requirements
|
|
# It have two parameters to control the final batch size the number total of speaker used in each batch and the number of samples for each speaker
|
|
|
|
# number total of speaker in batch in training
|
|
config.num_classes_in_batch = 100
|
|
# number of utterance per class/speaker in the batch in training
|
|
config.num_utter_per_class = 4
|
|
# final batch size = config.num_classes_in_batch * config.num_utter_per_class
|
|
|
|
# number total of speaker in batch in evaluation
|
|
config.eval_num_classes_in_batch = 100
|
|
# number of utterance per class/speaker in the batch in evaluation
|
|
config.eval_num_utter_per_class = 4
|
|
|
|
# number of data loader workers
|
|
config.num_loader_workers = 8
|
|
config.num_val_loader_workers = 8
|
|
|
|
# number of epochs
|
|
config.epochs = 10000
|
|
# loss to be used in training
|
|
config.loss = "softmaxproto"
|
|
|
|
# run eval
|
|
config.run_eval = False
|
|
|
|
# output path for the checkpoints
|
|
config.output_path = OUTPUT_PATH
|
|
|
|
# Save local checkpoint every save_step steps
|
|
config.save_step = 2000
|
|
|
|
### Model Config ###
|
|
config.model_params = {
|
|
"model_name": "resnet", # supported "lstm" and "resnet"
|
|
"input_dim": 64,
|
|
"use_torch_spec": True,
|
|
"log_input": True,
|
|
"proj_dim": 512, # embedding dim
|
|
}
|
|
|
|
### Audio Config ###
|
|
# To fast train the model divides the audio in small parts. it parameter defines the length in seconds of these "parts"
|
|
config.voice_len = 2.0
|
|
# all others configs
|
|
config.audio = {
|
|
"fft_size": 512,
|
|
"win_length": 400,
|
|
"hop_length": 160,
|
|
"frame_shift_ms": None,
|
|
"frame_length_ms": None,
|
|
"stft_pad_mode": "reflect",
|
|
"sample_rate": 16000,
|
|
"resample": False,
|
|
"preemphasis": 0.97,
|
|
"ref_level_db": 20,
|
|
"do_sound_norm": False,
|
|
"do_trim_silence": False,
|
|
"trim_db": 60,
|
|
"power": 1.5,
|
|
"griffin_lim_iters": 60,
|
|
"num_mels": 64,
|
|
"mel_fmin": 0.0,
|
|
"mel_fmax": 8000.0,
|
|
"spec_gain": 20,
|
|
"signal_norm": False,
|
|
"min_level_db": -100,
|
|
"symmetric_norm": False,
|
|
"max_norm": 4.0,
|
|
"clip_norm": False,
|
|
"stats_path": None,
|
|
"do_rms_norm": True,
|
|
"db_level": -27.0,
|
|
}
|
|
|
|
|
|
### Augmentation Config ###
|
|
config.audio_augmentation = {
|
|
# additive noise and room impulse response (RIR) simulation similar to: https://arxiv.org/pdf/2009.14153.pdf
|
|
"p": 0.5, # probability to the use of one of the augmentation - 0 means disabled
|
|
"rir": {"rir_path": RIR_SIMULATED_PATH, "conv_mode": "full"}, # download: https://www.openslr.org/17/
|
|
"additive": {
|
|
"sounds_path": MUSAN_PATH,
|
|
"speech": {"min_snr_in_db": 13, "max_snr_in_db": 20, "min_num_noises": 1, "max_num_noises": 1},
|
|
"noise": {"min_snr_in_db": 0, "max_snr_in_db": 15, "min_num_noises": 1, "max_num_noises": 1},
|
|
"music": {"min_snr_in_db": 5, "max_snr_in_db": 15, "min_num_noises": 1, "max_num_noises": 1},
|
|
},
|
|
"gaussian": {"p": 0.7, "min_amplitude": 0.0, "max_amplitude": 1e-05},
|
|
}
|
|
|
|
config.save_json(CONFIG_OUT_PATH)
|
|
|
|
print(CONFIG_OUT_PATH)
|
|
if RESTORE_PATH is not None:
|
|
command = f"python TTS/bin/train_encoder.py --config_path {CONFIG_OUT_PATH} --restore_path {RESTORE_PATH}"
|
|
else:
|
|
command = f"python TTS/bin/train_encoder.py --config_path {CONFIG_OUT_PATH}"
|
|
|
|
os.system(command)
|