mirror of https://github.com/coqui-ai/TTS.git
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
import glob
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import os
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import shutil
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from tests import get_device_id, get_tests_output_path, run_cli
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.configs.vits_config import VitsConfig
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from TTS.utils.trainer_utils import get_last_checkpoint
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config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
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output_path = os.path.join(get_tests_output_path(), "train_outputs")
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dataset_config_en = BaseDatasetConfig(
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name="ljspeech_test",
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meta_file_train="metadata.csv",
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meta_file_val="metadata.csv",
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path="tests/data/ljspeech",
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language="en",
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)
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dataset_config_pt = BaseDatasetConfig(
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name="ljspeech_test",
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meta_file_train="metadata.csv",
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meta_file_val="metadata.csv",
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path="tests/data/ljspeech",
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language="pt-br",
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)
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config = VitsConfig(
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batch_size=2,
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eval_batch_size=2,
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num_loader_workers=0,
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num_eval_loader_workers=0,
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text_cleaner="multilingual_cleaners",
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use_phonemes=False,
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phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
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run_eval=True,
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test_delay_epochs=-1,
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epochs=1,
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print_step=1,
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print_eval=True,
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test_sentences=[
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["Be a voice, not an echo.", "ljspeech-0", None, "en"],
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["Be a voice, not an echo.", "ljspeech-1", None, "pt-br"],
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],
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datasets=[dataset_config_en, dataset_config_pt],
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)
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# set audio config
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config.audio.do_trim_silence = True
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config.audio.trim_db = 60
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# active multilingual mode
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config.model_args.use_language_embedding = True
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config.use_language_embedding = True
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# deactivate multispeaker mode
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config.model_args.use_speaker_embedding = False
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config.use_speaker_embedding = False
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# active multispeaker d-vec mode
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config.model_args.use_d_vector_file = True
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config.use_d_vector_file = True
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config.model_args.d_vector_file = "tests/data/ljspeech/speakers.json"
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config.d_vector_file = "tests/data/ljspeech/speakers.json"
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config.model_args.d_vector_dim = 256
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config.d_vector_dim = 256
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# duration predictor
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config.model_args.use_sdp = True
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config.use_sdp = True
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# deactivate language sampler
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config.use_language_weighted_sampler = False
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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_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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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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# Find latest folder
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continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
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# Inference using TTS API
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continue_config_path = os.path.join(continue_path, "config.json")
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continue_restore_path, _ = get_last_checkpoint(continue_path)
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out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
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speaker_id = "ljspeech-1"
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languae_id = "en"
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continue_speakers_path = config.d_vector_file
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continue_languages_path = os.path.join(continue_path, "language_ids.json")
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inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --language_ids_file_path {continue_languages_path} --language_idx {languae_id} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
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run_cli(inference_command)
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# restore the model and continue training for one more epoch
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command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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run_cli(command_train)
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shutil.rmtree(continue_path)
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