video-dubbing / TTS /tests /tts_tests2 /test_fastspeech_2_train.py
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import glob
import json
import os
import shutil
from trainer import get_last_checkpoint
from tests import get_device_id, get_tests_output_path, run_cli
from TTS.config.shared_configs import BaseAudioConfig
from TTS.tts.configs.fastspeech2_config import Fastspeech2Config
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
audio_config = BaseAudioConfig(
sample_rate=22050,
do_trim_silence=True,
trim_db=60.0,
signal_norm=False,
mel_fmin=0.0,
mel_fmax=8000,
spec_gain=1.0,
log_func="np.log",
ref_level_db=20,
preemphasis=0.0,
)
config = Fastspeech2Config(
audio=audio_config,
batch_size=8,
eval_batch_size=8,
num_loader_workers=0,
num_eval_loader_workers=0,
text_cleaner="english_cleaners",
use_phonemes=True,
phoneme_language="en-us",
phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
f0_cache_path="tests/data/ljspeech/f0_cache/",
compute_f0=True,
compute_energy=True,
energy_cache_path="tests/data/ljspeech/energy_cache/",
run_eval=True,
test_delay_epochs=-1,
epochs=1,
print_step=1,
print_eval=True,
test_sentences=[
"Be a voice, not an echo.",
],
use_speaker_embedding=False,
)
config.audio.do_trim_silence = True
config.use_speaker_embedding = False
config.model_args.use_speaker_embedding = False
config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.formatter ljspeech "
"--coqpit.datasets.0.meta_file_train metadata.csv "
"--coqpit.datasets.0.meta_file_val metadata.csv "
"--coqpit.datasets.0.path tests/data/ljspeech "
"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
"--coqpit.test_delay_epochs 0"
)
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
# Inference using TTS API
continue_config_path = os.path.join(continue_path, "config.json")
continue_restore_path, _ = get_last_checkpoint(continue_path)
out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
# Check integrity of the config
with open(continue_config_path, "r", encoding="utf-8") as f:
config_loaded = json.load(f)
assert config_loaded["characters"] is not None
assert config_loaded["output_path"] in continue_path
assert config_loaded["test_delay_epochs"] == 0
# Load the model and run inference
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}"
run_cli(inference_command)
# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)