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{
"run_name": "wavegrad-ljspeech",
"run_description": "wavegrad ljspeech",
"audio":{
"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
"win_length": 1024, // stft window length in ms.
"hop_length": 256, // stft window hop-lengh in ms.
"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
// Audio processing parameters
"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"ref_level_db": 0, // reference level db, theoretically 20db is the sound of air.
// Silence trimming
"do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
// MelSpectrogram parameters
"num_mels": 80, // size of the mel spec frame.
"mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
"spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
// Normalization parameters
"signal_norm": true, // 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": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"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
},
// DISTRIBUTED TRAINING
"mixed_precision": false,
"distributed":{
"backend": "nccl",
"url": "tcp:\/\/localhost:54322"
},
"target_loss": "avg_wavegrad_loss", // loss value to pick the best model to save after each epoch
// MODEL PARAMETERS
"generator_model": "wavegrad",
"model_params":{
"y_conv_channels":32,
"x_conv_channels":768,
"ublock_out_channels": [512, 512, 256, 128, 128],
"dblock_out_channels": [128, 128, 256, 512],
"upsample_factors": [4, 4, 4, 2, 2],
"upsample_dilations": [
[1, 2, 1, 2],
[1, 2, 1, 2],
[1, 2, 4, 8],
[1, 2, 4, 8],
[1, 2, 4, 8]],
"use_weight_norm": true
},
// DATASET
"data_path": "tests/data/ljspeech/wavs/", // root data path. It finds all wav files recursively from there.
"feature_path": null, // if you use precomputed features
"seq_len": 6144, // 24 * hop_length
"pad_short": 0, // additional padding for short wavs
"conv_pad": 0, // additional padding against convolutions applied to spectrograms
"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.
"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
// TRAINING
"batch_size": 1, // Batch size for training.
"train_noise_schedule":{
"min_val": 1e-6,
"max_val": 1e-2,
"num_steps": 1000
},
"test_noise_schedule":{
"min_val": 1e-6,
"max_val": 1e-2,
"num_steps": 2
},
// VALIDATION
"run_eval": true, // enable/disable evaluation run
// OPTIMIZER
"epochs": 1, // total number of epochs to train.
"grad_clip": 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_params": {
"gamma": 0.5,
"milestones": [100000, 200000, 300000, 400000, 500000, 600000]
},
"lr": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
// TENSORBOARD and LOGGING
"print_step": 250, // Number of steps to log traning on console.
"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.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"keep_all_best": true, // If true, keeps all best_models after keep_after steps
"keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
"tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
// DATA LOADING
"num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_eval_loader_workers": 0, // number of evaluation data loader processes.
"eval_split_size": 4,
// PATHS
"output_path": "tests/train_outputs/"
}
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