mirror of
https://github.com/coqui-ai/TTS.git
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3b8b105b0d
* Adding encoder * currently modifying hmm * Adding hmm * Adding overflow * Adding overflow setting up flat start * Removing runs * adding normalization parameters * Fixing models on same device * Training overflow and plotting evaluations * Adding inference * At the end of epoch the test sentences are coming on cpu instead of gpu * Adding figures from model during training to monitor * reverting tacotron2 training recipe * fixing inference on gpu for test sentences on config * moving helpers and texts within overflows source code * renaming to overflow * moving loss to the model file * Fixing the rename * Model training but not plotting the test config sentences's audios * Formatting logs * Changing model name to camelcase * Fixing test log * Fixing plotting bug * Adding some tests * Adding more tests to overflow * Adding all tests for overflow * making changes to camel case in config * Adding information about parameters and docstring * removing compute_mel_statistics moved statistic computation to the model instead * Added overflow in readme * Adding more test cases, now it doesn't saves transition_p like tensor and can be dumped as json
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
import glob
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import json
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import os
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import shutil
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import torch
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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.tts.configs.overflow_config import OverflowConfig
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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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parameter_path = os.path.join(get_tests_output_path(), "lj_parameters.pt")
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torch.save({"mean": -5.5138, "std": 2.0636, "init_transition_prob": 0.3212}, parameter_path)
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config = OverflowConfig(
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batch_size=3,
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eval_batch_size=3,
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num_loader_workers=0,
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num_eval_loader_workers=0,
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text_cleaner="phoneme_cleaners",
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use_phonemes=True,
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phoneme_language="en-us",
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phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
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run_eval=True,
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test_delay_epochs=-1,
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mel_statistics_parameter_path=parameter_path,
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epochs=1,
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print_step=1,
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test_sentences=[
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"Be a voice, not an echo.",
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],
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print_eval=True,
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max_sampling_time=50,
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)
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config.audio.do_trim_silence = 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 when mel parameters exists
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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.test_delay_epochs 0 "
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)
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run_cli(command_train)
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# train the model for one epoch when mel parameters have to be computed from the dataset
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if os.path.exists(parameter_path):
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os.remove(parameter_path)
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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.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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