Medix-AI/coqui-tts
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coqui-tts
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update glow-tts ljspeech config

This commit is contained in:
erogol 2021-01-05 14:21:12 +01:00
parent f352b3534c
commit 228ada04b5
1 changed files with 16 additions and 4 deletions
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20
TTS/tts/configs/glow_tts_tdsep.json → TTS/tts/configs/glow_tts_ljspeech.json
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@ -31,12 +31,12 @@
"spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.00
// Normalization parameters
"signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
"signal_norm": false, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
"min_level_db": -100, // lower bound for normalization
"symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 1.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy" // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
"stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
},
// VOCABULARY PARAMETERS
@ -63,6 +63,19 @@
// MODEL PARAMETERS
// "use_mas": false, // use Monotonic Alignment Search if true. Otherwise use pre-computed attention alignments.
"hidden_channels_encoder": 192,
"hidden_channels_decoder": 192,
"hidden_channels_duration_predictor": 256,
"use_encoder_prenet": true,
"encoder_type": "rel_pos_transformer",
"encoder_params": {
"kernel_size":3,
"dropout_p": 0.1,
"num_layers": 6,
"num_heads": 2,
"hidden_channels_ffn": 768,
"input_length": null
},
// TRAINING
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
@ -86,8 +99,6 @@
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
"encoder_type": "residual_conv_bn",
// TENSORBOARD and LOGGING
"print_step": 25, // Number of steps to log training on console.
"tb_plot_step": 100, // Number of steps to plot TB training figures.
@ -105,6 +116,7 @@
"min_seq_len": 3, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 500, // DATASET-RELATED: maximum text length
"compute_f0": false, // compute f0 values in data-loader
"use_noise_augment": true, //add a random noise to audio signal for augmentation at training .
"compute_input_seq_cache": true,
// PATHS