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Fastspeech2 (#2073)
* added EnergyDataset * add energy to Dataset * add comupte_energy * added energy params * added energy to forward_tts * added plot_avg_energy for visualisation * Update forward_tts.py * create file * added fastspeech2 recipe * add fastspeech2 config * removed energy from fast pitch * add energy loss to forward tts * Update fastspeech2_config.py * change run_name * Update numpy_transforms.py * fix typo * fix typo * fix typo * linting issues * use_energy default value --> False * Update numpy_transforms.py * linting fixes * fix typo * liniting_fix * liniting_fix * fix * fixes * fixes * lint fix * lint fixws * added training test * wrong import * wrong import * trailing whitespace * style fix * changed class name because of error * class name change * class name change * change class name * fixed styles
This commit is contained in:
manmay nakhashi
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import glob
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import json
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import os
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import shutil
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from trainer import get_last_checkpoint
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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 BaseAudioConfig
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from TTS.tts.configs.fastspeech2_config import Fastspeech2Config
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config_path = os.path.join(get_tests_output_path(), "fast_pitch_speaker_emb_config.json")
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output_path = os.path.join(get_tests_output_path(), "train_outputs")
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audio_config = BaseAudioConfig(
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sample_rate=22050,
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do_trim_silence=True,
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trim_db=60.0,
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signal_norm=False,
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mel_fmin=0.0,
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mel_fmax=8000,
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spec_gain=1.0,
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log_func="np.log",
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ref_level_db=20,
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preemphasis=0.0,
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)
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config = Fastspeech2Config(
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audio=audio_config,
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batch_size=8,
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eval_batch_size=8,
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num_loader_workers=0,
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num_eval_loader_workers=0,
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text_cleaner="english_cleaners",
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use_phonemes=True,
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phoneme_language="en-us",
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phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
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f0_cache_path="tests/data/ljspeech/f0_cache/",
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compute_f0=True,
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compute_energy=True,
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energy_cache_path="tests/data/ljspeech/f0_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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use_speaker_embedding=True,
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test_sentences=[
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"Be a voice, not an echo.",
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],
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)
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config.audio.do_trim_silence = True
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config.use_speaker_embedding = True
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config.model_args.use_speaker_embedding = True
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config.audio.trim_db = 60
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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.datasets.0.formatter ljspeech_test "
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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.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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continue_speakers_path = os.path.join(continue_path, "speakers.json")
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# Check integrity of the config
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with open(continue_config_path, "r", encoding="utf-8") as f:
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config_loaded = json.load(f)
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assert config_loaded["characters"] is not None
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assert config_loaded["output_path"] in continue_path
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assert config_loaded["test_delay_epochs"] == 0
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# Load the model and run inference
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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} --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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@@ -0,0 +1,94 @@
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import glob
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import json
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import os
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import shutil
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from trainer import get_last_checkpoint
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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 BaseAudioConfig
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from TTS.tts.configs.fastspeech2_config import Fastspeech2Config
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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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audio_config = BaseAudioConfig(
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sample_rate=22050,
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do_trim_silence=True,
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trim_db=60.0,
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signal_norm=False,
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mel_fmin=0.0,
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mel_fmax=8000,
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spec_gain=1.0,
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log_func="np.log",
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ref_level_db=20,
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preemphasis=0.0,
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)
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config = Fastspeech2Config(
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audio=audio_config,
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batch_size=8,
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eval_batch_size=8,
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num_loader_workers=0,
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num_eval_loader_workers=0,
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text_cleaner="english_cleaners",
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use_phonemes=True,
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phoneme_language="en-us",
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phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
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f0_cache_path="tests/data/ljspeech/f0_cache/",
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compute_f0=True,
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compute_energy=True,
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energy_cache_path="tests/data/ljspeech/f0_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.",
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],
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use_speaker_embedding=False,
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)
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config.audio.do_trim_silence = True
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config.use_speaker_embedding = False
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config.model_args.use_speaker_embedding = False
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config.audio.trim_db = 60
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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.datasets.0.formatter 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.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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# Check integrity of the config
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with open(continue_config_path, "r", encoding="utf-8") as f:
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config_loaded = json.load(f)
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assert config_loaded["characters"] is not None
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assert config_loaded["output_path"] in continue_path
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assert config_loaded["test_delay_epochs"] == 0
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# Load the model and run inference
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inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --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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