mirror of
https://github.com/coqui-ai/TTS.git
synced 2025-12-25 04:39:29 +01:00
update tts training tests to use the trainer
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@@ -30,12 +30,13 @@ config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_align_tts.py --config_path {config_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.test_delay_epochs -1"
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)
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run_cli(command_train)
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@@ -44,7 +45,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_align_tts.py --continue_path {continue_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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@@ -31,13 +31,14 @@ config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_glow_tts.py --config_path {config_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt"
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
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"--coqpit.test_delay_epochs 0"
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)
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run_cli(command_train)
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@@ -46,7 +47,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_glow_tts.py --continue_path {continue_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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@@ -30,13 +30,14 @@ config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_speedy_speech.py --config_path {config_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt"
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
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"--coqpit.test_delay_epochs 0"
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)
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run_cli(command_train)
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@@ -45,7 +46,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_speedy_speech.py --continue_path {continue_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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@@ -31,12 +31,13 @@ config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --config_path {config_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.test_delay_epochs 0 "
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)
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run_cli(command_train)
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@@ -45,7 +46,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --continue_path {continue_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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@@ -30,12 +30,13 @@ config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --config_path {config_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.test_delay_epochs 0"
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)
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run_cli(command_train)
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@@ -44,7 +45,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tacotron.py --continue_path {continue_path} "
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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