mozilla
/
TTS
зеркало из https://github.com/mozilla/TTS.git
1
0
Форкнуть 0
Код Задачи Пакеты Проекты Релизы Вики Активность

remove files

This commit is contained in:
Eren Golge 2019-09-05 16:57:24 +02:00
Родитель 0bb8d780e8
Коммит 16d1f62afa
3 изменённых файлов: 0 добавлений и 123 удалений
Показать статистику Скачать Patch файл Скачать Diff файл Показать все файлы Свернуть все файлы

41
config_kusal.json
Просмотреть файл

@ -1,41 +0,0 @@
{
"model_name": "TTS-larger-kusal",
"audio_processor": "audio",
"num_mels": 80,
"num_freq": 1025,
"sample_rate": 22000,
"frame_length_ms": 50,
"frame_shift_ms": 12.5,
"preemphasis": 0.97,
"min_mel_freq": 125,
"max_mel_freq": 7600,
"min_level_db": -100,
"ref_level_db": 20,
"embedding_size": 256,
"text_cleaner": "english_cleaners",
"epochs": 1000,
"lr": 0.002,
"lr_decay": 0.5,
"decay_step": 100000,
"warmup_steps": 4000,
"batch_size": 32,
"eval_batch_size":-1,
"r": 5,
"griffin_lim_iters": 60,
"power": 1.5,
"num_loader_workers": 8,
"checkpoint": true,
"save_step": 25000,
"print_step": 10,
"run_eval": false,
"data_path": "/snakepit/shared/data/mycroft/kusal/",
"meta_file_train": "prompts.txt",
"meta_file_val": null,
"dataset": "Kusal",
"min_seq_len": 0,
"output_path": "../keep/"
}

82
config_libritts.json
Просмотреть файл

@ -1,82 +0,0 @@
{
"run_name": "libritts-360",
"run_description": "LibriTTS 360 gradual traning with memory queue.",
"audio":{
// Audio processing parameters
"num_mels": 80, // size of the mel spec frame.
"num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"frame_length_ms": 50, // stft window length in ms.
"frame_shift_ms": 12.5, // stft window hop-lengh in ms.
"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"min_level_db": -100, // normalization range
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": false, // move normalization to range [-1, 1]
"max_norm": 1, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
"do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
},
"distributed":{
"backend": "nccl",
"url": "tcp:\/\/localhost:54321"
},
"reinit_layers": [],
"model": "Tacotron", // one of the model in models/
"grad_clip": 1, // upper limit for gradients for clipping.
"epochs": 1000, // total number of epochs to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"memory_size": 7, // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
"attention_norm": "sigmoid", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
"prenet_type": "original", // "original" or "bn".
"prenet_dropout": true, // enable/disable dropout at prenet.
"windowing": false, // Enables attention windowing. Used only in eval mode.
"use_forward_attn": false, // enable/disable forward attention. In general, it aligns faster.
"forward_attn_mask": false,
"transition_agent": false, // enable/disable transition agent of forward attention.
"location_attn": true, // enable_disable location sensitive attention.
"loss_masking": true, // enable / disable loss masking against the sequence padding.
"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
"stopnet": true, // Train stopnet predicting the end of synthesis.
"separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
"tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"eval_batch_size":16,
"r": 7, // Number of frames to predict for step.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
"print_step": 10, // Number of steps to log traning on console.
"batch_group_size": 0, //Number of batches to shuffle after bucketing.
"run_eval": true,
"test_delay_epochs": 5, //Until attention is aligned, testing only wastes computation time.
"test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
"data_path": "/home/erogol/Data/Libri-TTS/train-clean-360/", // DATASET-RELATED: can overwritten from command argument
"meta_file_train": null, // DATASET-RELATED: metafile for training dataloader.
"meta_file_val": null, // DATASET-RELATED: metafile for evaluation dataloader.
"dataset": "libri_tts", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 150, // DATASET-RELATED: maximum text length
"output_path": "/media/erogol/data_ssd/Models/libri_tts/", // DATASET-RELATED: output path for all training outputs.
"num_loader_workers": 12, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_val_loader_workers": 4, // number of evaluation data loader processes.
"phoneme_cache_path": "mozilla_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.
"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
"phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
"text_cleaner": "phoneme_cleaners",
"use_speaker_embedding": true
}

0
server/__init__.py Normal file
Просмотреть файл