| { | |
| "run_name": "hifigan", | |
| "run_description": "universal hifigan trained on LibriTTS with no spectrogram normalization and using log() for scaling instead of log10()", | |
| // 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.log", | |
| // 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": 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": 1.0, // scaler value appplied after log transform of spectrogram. | |
| // Normalization parameters | |
| "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": 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 | |
| "distributed":{ | |
| "backend": "nccl", | |
| "url": "tcp:\/\/localhost:54324" | |
| }, | |
| // MODEL PARAMETERS | |
| "use_pqmf": false, | |
| // LOSS PARAMETERS | |
| "use_stft_loss": false, | |
| "use_subband_stft_loss": false, | |
| "use_mse_gan_loss": true, | |
| "use_hinge_gan_loss": false, | |
| "use_feat_match_loss": true, // use only with melgan discriminators | |
| "use_l1_spec_loss": true, | |
| // loss weights | |
| "stft_loss_weight": 0, | |
| "subband_stft_loss_weight": 0, | |
| "mse_G_loss_weight": 1, | |
| "hinge_G_loss_weight": 0, | |
| "feat_match_loss_weight": 10, | |
| "l1_spec_loss_weight": 45, | |
| // multiscale stft loss parameters | |
| // "stft_loss_params": { | |
| // "n_ffts": [1024, 2048, 512], | |
| // "hop_lengths": [120, 240, 50], | |
| // "win_lengths": [600, 1200, 240] | |
| // }, | |
| "l1_spec_loss_params": { | |
| "use_mel": true, | |
| "sample_rate": 16000, | |
| "n_fft": 1024, | |
| "hop_length": 256, | |
| "win_length": 1024, | |
| "n_mels": 80, | |
| "mel_fmin": 0.0, | |
| "mel_fmax": null | |
| }, | |
| "target_loss": "avg_G_loss", // loss value to pick the best model to save after each epoch | |
| // DISCRIMINATOR | |
| "discriminator_model": "hifigan_discriminator", | |
| //"discriminator_model_params":{ | |
| // "peroids": [2, 3, 5, 7, 11], | |
| // "base_channels": 16, | |
| // "max_channels":512, | |
| // "downsample_factors":[4, 4, 4] | |
| //}, | |
| "steps_to_start_discriminator": 0, // steps required to start GAN trainining.1 | |
| // GENERATOR | |
| "generator_model": "hifigan_generator", | |
| "generator_model_params": { | |
| "resblock_type": "1", | |
| "upsample_factors": [8,8,2,2], | |
| "upsample_kernel_sizes": [16,16,4,4], | |
| "upsample_initial_channel": 128, | |
| "resblock_kernel_sizes": [3,7,11], | |
| "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]] | |
| }, | |
| // DATASET | |
| "data_path": "/home/erogol/gdrive/Datasets/non-binary-voice-files/vo_voice_quality_transformation/", | |
| "feature_path": null, | |
| // "feature_path": "/home/erogol/gdrive/Datasets/non-binary-voice-files/tacotron-DCA/", | |
| "seq_len": 8192, | |
| "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": 16, // 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": 10000, // 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. | |
| // "lr_scheduler_gen": "ExponentialLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate | |
| // "lr_scheduler_gen_params": { | |
| // "gamma": 0.999, | |
| // "last_epoch": -1 | |
| // }, | |
| // "lr_scheduler_disc": "ExponentialLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate | |
| // "lr_scheduler_disc_params": { | |
| // "gamma": 0.999, | |
| // "last_epoch": -1 | |
| // }, | |
| "lr_gen": 0.00001, // Initial learning rate. If Noam decay is active, maximum learning rate. | |
| "lr_disc": 0.00001, | |
| // 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" | |
| "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": 8, // 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. | |
| "eval_split_size": 10, | |
| // PATHS | |
| "output_path": "/home/erogol/gdrive/Trainings/sam/" | |
| } | |