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wavegrad config updates

This commit is contained in:
erogol 2020-10-26 16:46:50 +01:00
parent c8a4c771a8
commit 5b5b9fcfdd
1 changed files with 11 additions and 11 deletions
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TTS/vocoder/configs/wavegrad_libritts.json
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@ -30,11 +30,11 @@
"symmetric_norm": true, // move normalization to range [-1, 1] "symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm] "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range. "clip_norm": true, // clip normalized values into the range.
"stats_path": "/home/erogol/Data/libritts/LibriTTS/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": "/home/erogol/Data/libritts/LibriTTS/scale_stats_wavegrad.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
}, },
// DISTRIBUTED TRAINING // DISTRIBUTED TRAINING
"apex_amp_level": null, // APEX amp optimization level. "O1" is currently supported. "apex_amp_level": "O1", // APEX amp optimization level. "O1" is currently supported.
"distributed":{ "distributed":{
"backend": "nccl", "backend": "nccl",
"url": "tcp:\/\/localhost:54322" "url": "tcp:\/\/localhost:54322"
@ -45,8 +45,8 @@
// MODEL PARAMETERS // MODEL PARAMETERS
"generator_model": "wavegrad", "generator_model": "wavegrad",
"model_params":{ "model_params":{
"x_conv_channels":32, "y_conv_channels":32,
"c_conv_channels":768, "x_conv_channels":768,
"ublock_out_channels": [512, 512, 256, 128, 128], "ublock_out_channels": [512, 512, 256, 128, 128],
"dblock_out_channels": [128, 128, 256, 512], "dblock_out_channels": [128, 128, 256, 512],
"upsample_factors": [4, 4, 4, 2, 2], "upsample_factors": [4, 4, 4, 2, 2],
@ -62,15 +62,15 @@
"data_path": "/home/erogol/Data/libritts/LibriTTS/train-clean-360/", // root data path. It finds all wav files recursively from there. "data_path": "/home/erogol/Data/libritts/LibriTTS/train-clean-360/", // root data path. It finds all wav files recursively from there.
"feature_path": null, // if you use precomputed features "feature_path": null, // if you use precomputed features
"seq_len": 6144, // 24 * hop_length "seq_len": 6144, // 24 * hop_length
"pad_short": 2000, // additional padding for short wavs "pad_short": 0, // additional padding for short wavs
"conv_pad": 0, // additional padding against convolutions applied to spectrograms "conv_pad": 0, // additional padding against convolutions applied to spectrograms
"use_noise_augment": false, // add noise to the audio signal for augmentation "use_noise_augment": false, // add noise to the audio signal for augmentation
"use_cache": true, // use in memory cache to keep the computed features. This might cause OOM. "use_cache": false, // use in memory cache to keep the computed features. This might cause OOM.
"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers. "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
// TRAINING // TRAINING
"batch_size": 64, // Batch size for training. "batch_size": 96, // Batch size for training.
"train_noise_schedule":{ "train_noise_schedule":{
"min_val": 1e-6, "min_val": 1e-6,
"max_val": 1e-2, "max_val": 1e-2,
@ -87,7 +87,7 @@
// OPTIMIZER // OPTIMIZER
"epochs": 10000, // total number of epochs to train. "epochs": 10000, // total number of epochs to train.
"clip_grad": 1, // Generator gradient clipping threshold. Apply gradient clipping if > 0 "clip_grad": 1.0, // Generator gradient clipping threshold. Apply gradient clipping if > 0
"lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate "lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"lr_scheduler_params": { "lr_scheduler_params": {
"gamma": 0.5, "gamma": 0.5,
@ -96,16 +96,16 @@
"lr": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate. "lr": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
// TENSORBOARD and LOGGING // TENSORBOARD and LOGGING
"print_step": 25, // Number of steps to log traning on console. "print_step": 50, // Number of steps to log traning on console.
"print_eval": false, // If True, it prints loss values for each step in eval run. "print_eval": false, // If True, it prints loss values for each step in eval run.
"save_step": 10000, // Number of training steps expected to plot training stats on TB and save model checkpoints. "save_step": 10000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
"checkpoint": true, // If true, it saves checkpoints per "save_step" "checkpoint": true, // If true, it saves checkpoints per "save_step"
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. "tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
// DATA LOADING // DATA LOADING
"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values. "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. "num_val_loader_workers": 4, // number of evaluation data loader processes.
"eval_split_size": 10, "eval_split_size": 256,
// PATHS // PATHS
"output_path": "/home/erogol/Models/LJSpeech/" "output_path": "/home/erogol/Models/LJSpeech/"