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{
"run_name": "wavernn_test",
"run_description": "wavernn_test training",
// AUDIO PARAMETERS
"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": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
"spec_gain": 20.0, // 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
},
// Generating / Synthesizing
"batched": true,
"target_samples": 11000, // target number of samples to be generated in each batch entry
"overlap_samples": 550, // number of samples for crossfading between batches
// DISTRIBUTED TRAINING
// "distributed":{
// "backend": "nccl",
// "url": "tcp:\/\/localhost:54321"
// },
// MODEL PARAMETERS
"use_aux_net": true,
"use_upsample_net": true,
"upsample_factors": [4, 8, 8], // this needs to correctly factorise hop_length
"seq_len": 1280, // has to be devideable by hop_length
"mode": "mold", // mold [string], gauss [string], bits [int]
"mulaw": false, // apply mulaw if mode is bits
"padding": 2, // pad the input for resnet to see wider input length
// GENERATOR - for backward compatibility
"generator_model": "Wavernn",
// DATASET
//"use_gta": true, // use computed gta features from the tts model
"data_path": "tests/data/ljspeech/wavs/", // path containing training wav files
"feature_path": null, // path containing computed features from wav files if null compute them
// MODEL PARAMETERS
"wavernn_model_params": {
"rnn_dims": 512,
"fc_dims": 512,
"compute_dims": 128,
"res_out_dims": 128,
"num_res_blocks": 10,
"use_aux_net": true,
"use_upsample_net": true,
"upsample_factors": [4, 8, 8] // this needs to correctly factorise hop_length
},
"mixed_precision": false,
// TRAINING
"batch_size": 4, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"epochs": 1, // total number of epochs to train.
// VALIDATION
"run_eval": true,
"test_every_epochs": 10, // Test after set number of epochs (Test every 20 epochs for example)
// OPTIMIZER
"grad_clip": 4, // 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": [200000, 400000, 600000]
},
"lr": 1e-4, // initial learning rate
// TENSORBOARD and LOGGING
"print_step": 25, // 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": 25000, // 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": false, // 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": 10, // number of samples for testing
// PATHS
"output_path": "tests/train_outputs/"
}
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