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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)
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