Medix-AI/coqui-tts
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coqui-tts
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Merge pull request #642 from Edresson/main-trainer 

Bug fix on train encoder
This commit is contained in:
Eren Gölge 2021-07-11 21:57:19 +02:00 committed by GitHub
parent 8fbadad68e 4eac1c4651
commit d4b1c17b59
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 31 additions and 20 deletions
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2
TTS/bin/train_encoder.py
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@ -164,7 +164,7 @@ def main(args): # pylint: disable=redefined-outer-name
elif c.loss == "angleproto": elif c.loss == "angleproto":
criterion = AngleProtoLoss() criterion = AngleProtoLoss()
elif c.loss == "softmaxproto": elif c.loss == "softmaxproto":
criterion = SoftmaxAngleProtoLoss(c.model["proj_dim"], num_speakers) criterion = SoftmaxAngleProtoLoss(c.model_params["proj_dim"], num_speakers)
else: else:
raise Exception("The %s not is a loss supported" % c.loss) raise Exception("The %s not is a loss supported" % c.loss)

49
tests/test_speaker_encoder_train.py
View File

@ -6,7 +6,18 @@ from tests import get_device_id, get_tests_output_path, run_cli
from TTS.config.shared_configs import BaseAudioConfig from TTS.config.shared_configs import BaseAudioConfig
from TTS.speaker_encoder.speaker_encoder_config import SpeakerEncoderConfig from TTS.speaker_encoder.speaker_encoder_config import SpeakerEncoderConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json") 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.name ljspeech "
"--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") output_path = os.path.join(get_tests_output_path(), "train_outputs")
config = SpeakerEncoderConfig( config = SpeakerEncoderConfig(
@ -24,16 +35,9 @@ config.audio.do_trim_silence = True
config.audio.trim_db = 60 config.audio.trim_db = 60
config.save_json(config_path) config.save_json(config_path)
print(config)
# train the model for one epoch # train the model for one epoch
command_train = ( run_test_train()
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.name ljspeech "
"--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_train)
# Find latest folder # Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime) continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
@ -50,15 +54,7 @@ config.model_params["model_name"] = "resnet"
config.save_json(config_path) config.save_json(config_path)
# train the model for one epoch # train the model for one epoch
command_train = ( run_test_train()
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.name ljspeech "
"--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_train)
# Find latest folder # Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime) continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
@ -69,3 +65,18 @@ command_train = (
) )
run_cli(command_train) run_cli(command_train)
shutil.rmtree(continue_path) 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()