| { | |
| "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/" | |
| } | |