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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 BaseDatasetConfig
from TTS.tts.configs.vits_config import VitsConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
dataset_config_en = BaseDatasetConfig(
formatter="ljspeech",
meta_file_train="metadata.csv",
meta_file_val="metadata.csv",
path="tests/data/ljspeech",
language="en",
)
dataset_config_pt = BaseDatasetConfig(
formatter="ljspeech",
meta_file_train="metadata.csv",
meta_file_val="metadata.csv",
path="tests/data/ljspeech",
language="pt-br",
)
config = VitsConfig(
batch_size=2,
eval_batch_size=2,
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/",
run_eval=True,
test_delay_epochs=-1,
epochs=1,
print_step=1,
print_eval=True,
test_sentences=[
["Be a voice, not an echo.", "ljspeech", None, "en"],
["Be a voice, not an echo.", "ljspeech", None, "pt-br"],
],
datasets=[dataset_config_en, dataset_config_pt],
)
# set audio config
config.audio.do_trim_silence = True
config.audio.trim_db = 60
# active multilingual mode
config.model_args.use_language_embedding = True
config.use_language_embedding = True
# active multispeaker mode
config.model_args.use_speaker_embedding = True
config.use_speaker_embedding = True
# deactivate multispeaker d-vec mode
config.model_args.use_d_vector_file = False
config.use_d_vector_file = False
# duration predictor
config.model_args.use_sdp = False
config.use_sdp = False
# active language sampler
config.use_language_weighted_sampler = True
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.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")
speaker_id = "ljspeech"
languae_id = "en"
continue_speakers_path = os.path.join(continue_path, "speakers.json")
continue_languages_path = os.path.join(continue_path, "language_ids.json")
# 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.' --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}"
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)
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