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import glob
import os
import shutil
from tests import get_device_id, get_tests_output_path, run_cli
from TTS.config.shared_configs import BaseAudioConfig
from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig
def run_test_train():
command = (
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.formatter ljspeech_test "
"--coqpit.datasets.0.meta_file_train metadata.csv "
"--coqpit.datasets.0.meta_file_val metadata.csv "
"--coqpit.datasets.0.path tests/data/ljspeech "
)
run_cli(command)
config_path = os.path.join(get_tests_output_path(), "test_speaker_encoder_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
config = SpeakerEncoderConfig(
batch_size=4,
num_classes_in_batch=4,
num_utter_per_class=2,
eval_num_classes_in_batch=4,
eval_num_utter_per_class=2,
num_loader_workers=1,
epochs=1,
print_step=1,
save_step=2,
print_eval=True,
run_eval=True,
audio=BaseAudioConfig(num_mels=80),
)
config.audio.do_trim_silence = True
config.audio.trim_db = 60
config.loss = "ge2e"
config.save_json(config_path)
print(config)
# train the model for one epoch
run_test_train()
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
)
run_cli(command_train)
shutil.rmtree(continue_path)
# test resnet speaker encoder
config.model_params["model_name"] = "resnet"
config.save_json(config_path)
# train the model for one epoch
run_test_train()
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# restore the model and continue training for one more epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
)
run_cli(command_train)
shutil.rmtree(continue_path)
# test model with ge2e loss function
# config.loss = "ge2e"
# config.save_json(config_path)
# run_test_train()
# test model with angleproto loss function
# config.loss = "angleproto"
# config.save_json(config_path)
# run_test_train()
# test model with softmaxproto loss function
config.loss = "softmaxproto"
config.save_json(config_path)
run_test_train()
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