Medix-AI/coqui-tts
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coqui-tts
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config updates for libritts

This commit is contained in:
Eren Golge 2019-07-15 15:38:16 +02:00
parent 1203e72ae6
commit 9f1772a778
2 changed files with 7 additions and 7 deletions
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2
.compute
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@ -10,7 +10,7 @@ wget https://www.dropbox.com/s/wqn5v3wkktw9lmo/install.sh?dl=0 -O install.sh
sudo sh install.sh
python3 setup.py develop
# cp -R ${USER_DIR}/GermanData ../tmp/
python3 distribute.py --config_path config_tacotron_de.json --data_path /data/rw/home/de_DE/
python3 distribute.py --config_path config_libritts.json --data_path /data/rw/home/LibriTTS/train-clean-360/
# cp -R ${USER_DIR}/Mozilla_22050 ../tmp/
# python3 distribute.py --config_path config_tacotron_gst.json --data_path ../tmp/Mozilla_22050/
while true; do sleep 1000000; done

12
config_libritts.json
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@ -6,7 +6,7 @@
// 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": 24000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"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.
@ -31,7 +31,7 @@
"reinit_layers": [],
"model": "Tacotron", // one of the model in models/
"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.
@ -52,16 +52,16 @@
"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.
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"batch_size": 16, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"eval_batch_size":16,
"r": 5, // Number of frames to predict for step.
"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,
"run_eval": false,
"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
@ -71,7 +71,7 @@
"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": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"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.