mirror of https://github.com/coqui-ai/TTS.git
config updates
This commit is contained in:
Eren Golge
parent
bc51b81aae
commit
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2
.compute
2
.compute
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@ -3,5 +3,5 @@ ls ${SHARED_DIR}/data/
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pip3 install https://download.pytorch.org/whl/cu100/torch-1.0.1.post2-cp36-cp36m-linux_x86_64.whl
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yes | apt-get install espeak
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python3 setup.py develop
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python3 distribute.py --config_path config_cluster.json --data_path ${SHARED_DIR}/data/Blizzard/Nancy/ --restore_path ${USER_DIR}/checkpoint_187000_4378.pth.tar
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python3 distribute.py --config_path config_cluster.json --data_path ${SHARED_DIR}/data/Blizzard/Nancy/ --restore_path ${USER_DIR}/checkpoint_266000_4400.pth.tar
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# python3 distribute.py --config_path config_cluster.json --data_path ${SHARED_DIR}/data/Blizzard/Nancy/
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26
config.json
26
config.json
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@ -21,7 +21,7 @@
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": false // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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},
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"distributed":{
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@ -29,10 +29,10 @@
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": ["model.decoder.attention_layer"], //set which layers to be reinitialized in finetunning. Only used if --restore_model is provided.
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"reinit_layers": [], //set which layers to be reinitialized in finetunning. Only used if --restore_model is provided.
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"model": "Tacotron2", // one of the model in models/
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"grad_clip": 0.02, // upper limit for gradients for clipping.
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"grad_clip": 1, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
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@ -44,25 +44,25 @@
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"batch_size": 16, // Batch size for training. Lower values than 32 might cause hard to learn attention.
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"eval_batch_size":16,
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"r": 1, // Number of frames to predict for step.
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"wd": 0.000002, // Weight decay weight.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 10, // Number of steps to log traning on console.
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"print_step": 100, // Number of steps to log traning on console.
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"tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"batch_group_size": 8, //Number of batches to shuffle after bucketing.
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"run_eval": true,
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"test_delay_epochs": 100, //Until attention is aligned, testing only wastes computation time.
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"data_path": "/media/erogol/data_ssd/Data/LJSpeech-1.1", // DATASET-RELATED: can overwritten from command argument
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"meta_file_train": "metadata_train.csv", // DATASET-RELATED: metafile for training dataloader.
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"meta_file_val": "metadata_val.csv", // DATASET-RELATED: metafile for evaluation dataloader.
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"dataset": "ljspeech", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
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"test_delay_epochs": 2, //Until attention is aligned, testing only wastes computation time.
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"data_path": "/media/erogol/data_ssd/Data/Nancy/", // DATASET-RELATED: can overwritten from command argument
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"meta_file_train": "prompts_train.data", // DATASET-RELATED: metafile for training dataloader.
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"meta_file_val": "prompts_val.data", // DATASET-RELATED: metafile for evaluation dataloader.
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"dataset": "nancy", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
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"min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 1000, // DATASET-RELATED: maximum text length
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"max_seq_len": 120, // DATASET-RELATED: maximum text length
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"output_path": "/media/erogol/data_ssd/Data/models/ljspeech_models/", // DATASET-RELATED: output path for all training outputs.
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4, // number of evaluation data loader processes.
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"phoneme_cache_path": "ljspeech_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.
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"phoneme_cache_path": "nancy_us_phonemes2", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
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"phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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"text_cleaner": "phoneme_cleaners"
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@ -29,7 +29,7 @@
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": ["model.decoder.attention_layer"],
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"reinit_layers": [],
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"model": "Tacotron2", // one of the model in models/
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"grad_clip": 1, // upper limit for gradients for clipping.
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@ -125,8 +125,8 @@ class Attention(nn.Module):
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self._mask_value = -float("inf")
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self.windowing = windowing
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if self.windowing:
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self.win_back = 1
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self.win_front = 3
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self.win_back = 3
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self.win_front = 6
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self.win_idx = None
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self.norm = norm
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@ -37,7 +37,7 @@ def visualize(alignment, spectrogram_postnet, stop_tokens, text, hop_length, CON
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num_plot = 3
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label_fontsize = 16
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plt.figure(figsize=(16, 48))
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plt.figure(figsize=(8, 24))
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plt.subplot(num_plot, 1, 1)
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plt.imshow(alignment.T, aspect="auto", origin="lower", interpolation=None)
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