mirror of https://github.com/coqui-ai/TTS.git
CI update and test fix
This commit is contained in:
erogol
parent
9033070b8f
commit
eb830f4bd7
|
|
@ -16,8 +16,8 @@ fi
|
|||
|
||||
if [[ "$TEST_SUITE" == "testscripts" ]]; then
|
||||
# Test server package
|
||||
./tts/tests/test_server_package.sh
|
||||
./tests/test_server_package.sh
|
||||
# test model training scripts
|
||||
./tts/tests/test_tts_train.sh
|
||||
./vocoder/tests/test_vocoder_train.sh
|
||||
./tests/test_tts_train.sh
|
||||
./tests/test_vocoder_train.sh
|
||||
fi
|
||||
|
|
|
|||
4
setup.py
4
setup.py
|
|
@ -56,11 +56,11 @@ class develop(setuptools.command.develop.develop):
|
|||
|
||||
|
||||
# The documentation for this feature is in server/README.md
|
||||
package_data = ['server/templates/*']
|
||||
package_data = ['TTS/server/templates/*']
|
||||
|
||||
if 'bdist_wheel' in unknown_args and args.checkpoint and args.model_config:
|
||||
print('Embedding model in wheel file...')
|
||||
model_dir = os.path.join('server', 'model')
|
||||
model_dir = os.path.join('TTS', 'server', 'model')
|
||||
tts_dir = os.path.join(model_dir, 'tts')
|
||||
os.makedirs(tts_dir, exist_ok=True)
|
||||
embedded_checkpoint_path = os.path.join(tts_dir, 'checkpoint.pth.tar')
|
||||
|
|
|
|||
|
|
@ -0,0 +1,88 @@
|
|||
{
|
||||
"run_name": "mozilla-no-loc-fattn-stopnet-sigmoid-loss_masking",
|
||||
"run_description": "using forward attention, with original prenet, loss masking,separate stopnet, sigmoid. Compare this with 4817. Pytorch DPP",
|
||||
|
||||
"audio":{
|
||||
// Audio processing parameters
|
||||
"num_mels": 80, // size of the mel spec frame.
|
||||
"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
|
||||
"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
|
||||
"hop_length": 256,
|
||||
"win_length": 1024,
|
||||
"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": "Tacotron2", // 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"
|
||||
"windowing": false, // Enables attention windowing. Used only in eval mode.
|
||||
"memory_size": 5, // 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", // ONLY TACOTRON2 - "original" or "bn".
|
||||
"prenet_dropout": true, // ONLY TACOTRON2 - enable/disable dropout at prenet.
|
||||
"use_forward_attn": true, // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
|
||||
"forward_attn_mask": false,
|
||||
"attention_type": "original",
|
||||
"attention_heads": 5,
|
||||
"bidirectional_decoder": false,
|
||||
"transition_agent": false, // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
|
||||
"location_attn": false, // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
|
||||
"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": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
|
||||
"use_gst": false,
|
||||
"double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
|
||||
"ddc_r": 7, // reduction rate for coarse decoder.
|
||||
|
||||
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
|
||||
"eval_batch_size":16,
|
||||
"r": 1, // 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": "/media/erogol/data_ssd/Data/Mozilla/", // DATASET-RELATED: can overwritten from command argument
|
||||
"meta_file_train": "metadata_train.txt", // DATASET-RELATED: metafile for training dataloader.
|
||||
"meta_file_val": "metadata_val.txt", // DATASET-RELATED: metafile for evaluation dataloader.
|
||||
"dataset": "mozilla", // 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": 0, // DATASET-RELATED: minimum text length to use in training
|
||||
"max_seq_len": 150, // DATASET-RELATED: maximum text length
|
||||
"output_path": "../keep/", // DATASET-RELATED: output path for all training outputs.
|
||||
"num_loader_workers": 4, // 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": false, // 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": false // whether to use additional embeddings for separate speakers
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue