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test_vocoder_multiband_melgan_config.json
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test_vocoder_multiband_melgan_config.json
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{
"run_name": "multiband-melgan",
"run_description": "multiband melgan mean-var scaling",
// 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": 20, // reference level db, theoretically 20db is the sound of air.
"log_func": "np.log10",
"do_sound_norm": true,
// Silence trimming
"do_trim_silence": false,// 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
},
// DISTRIBUTED TRAINING
// "distributed":{
// "backend": "nccl",
// "url": "tcp:\/\/localhost:54321"
// },
// MODEL PARAMETERS
"use_pqmf": true,
// LOSS PARAMETERS
"use_stft_loss": true,
"use_subband_stft_loss": true,
"use_mse_gan_loss": true,
"use_hinge_gan_loss": false,
"use_feat_match_loss": false, // use only with melgan discriminators
"use_l1_spec_loss": true,
// loss weights
"stft_loss_weight": 0.5,
"subband_stft_loss_weight": 0.5,
"mse_G_loss_weight": 2.5,
"hinge_G_loss_weight": 2.5,
"feat_match_loss_weight": 25,
"l1_spec_loss_weight": 2.5,
// multiscale stft loss parameters
"stft_loss_params": {
"n_ffts": [1024, 2048, 512],
"hop_lengths": [120, 240, 50],
"win_lengths": [600, 1200, 240]
},
// subband multiscale stft loss parameters
"subband_stft_loss_params":{
"n_ffts": [384, 683, 171],
"hop_lengths": [30, 60, 10],
"win_lengths": [150, 300, 60]
},
"l1_spec_loss_params": {
"use_mel": true,
"sample_rate": 22050,
"n_fft": 1024,
"hop_length": 256,
"win_length": 1024,
"n_mels": 80,
"mel_fmin": 0.0,
"mel_fmax": null
},
"target_loss": "G_avg_loss", // loss value to pick the best model to save after each epoch
// DISCRIMINATOR
"discriminator_model": "melgan_multiscale_discriminator",
"discriminator_model_params":{
"base_channels": 16,
"max_channels":512,
"downsample_factors":[4, 4, 4]
},
"steps_to_start_discriminator": 200000, // steps required to start GAN trainining.1
// GENERATOR
"generator_model": "multiband_melgan_generator",
"generator_model_params": {
"upsample_factors":[8, 4, 2],
"num_res_blocks": 4
},
// DATASET
"data_path": "tests/data/ljspeech/wavs/",
"feature_path": null,
"seq_len": 16384,
"pad_short": 2000,
"conv_pad": 0,
"use_noise_augment": false,
"use_cache": true,
"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": 4, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
// VALIDATION
"run_eval": true,
"test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time.
"test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
// OPTIMIZER
"epochs": 1, // total number of epochs to train.
"wd": 0.0, // Weight decay weight.
"gen_clip_grad": -1, // Generator gradient clipping threshold. Apply gradient clipping if > 0
"disc_clip_grad": -1, // Discriminator gradient clipping threshold.
"optimizer": "AdamW",
"optimizer_params":{
"betas": [0.8, 0.99],
"weight_decay": 0.0
},
"lr_scheduler_gen": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"lr_scheduler_gen_params": {
"gamma": 0.5,
"milestones": [100000, 200000, 300000, 400000, 500000, 600000]
},
"lr_scheduler_disc": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"lr_scheduler_disc_params": {
"gamma": 0.5,
"milestones": [100000, 200000, 300000, 400000, 500000, 600000]
},
"lr_gen": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_disc": 1e-4,
// TENSORBOARD and LOGGING
"print_step": 1, // 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,
// PATHS
"output_path": "tests/train_outputs/"
}