Spaces:
Runtime error
Runtime error
| { | |
| "model": "speaker_encoder", | |
| "run_name": "test_speaker_encoder", | |
| "run_description": "test speaker encoder.", | |
| "audio":{ | |
| // Audio processing parameters | |
| "num_mels": 40, // size of the mel spec frame. | |
| "fft_size": 400, // number of stft frequency levels. Size of the linear spectogram frame. | |
| "sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled. | |
| "win_length": 400, // stft window length in ms. | |
| "hop_length": 160, // 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. | |
| "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. | |
| "min_level_db": -100, // normalization range | |
| "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air. | |
| "power": 1.5, // value to sharpen wav signals after GL algorithm. | |
| "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation. | |
| // Normalization parameters | |
| "signal_norm": true, // normalize the spec values in range [0, 1] | |
| "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. | |
| "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!! | |
| "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. | |
| }, | |
| "reinit_layers": [], | |
| "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA) | |
| "grad_clip": 3.0, // upper limit for gradients for clipping. | |
| "epochs": 1000, // total number of epochs to train. | |
| "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate. | |
| "lr_decay": false, // if true, Noam learning rate decaying is applied through training. | |
| "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" | |
| "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. | |
| "steps_plot_stats": 10, // number of steps to plot embeddings. | |
| "num_classes_in_batch": 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. | |
| "num_utter_per_class": 10, // | |
| "num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values. | |
| "wd": 0.000001, // Weight decay weight. | |
| "checkpoint": true, // If true, it saves checkpoints per "save_step" | |
| "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints. | |
| "print_step": 20, // Number of steps to log traning on console. | |
| "batch_size": 32, | |
| "output_path": "", // DATASET-RELATED: output path for all training outputs. | |
| "model_params": { | |
| "model_name": "lstm", | |
| "input_dim": 40, | |
| "proj_dim": 256, | |
| "lstm_dim": 768, | |
| "num_lstm_layers": 3, | |
| "use_lstm_with_projection": true | |
| }, | |
| "storage": { | |
| "sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage | |
| "storage_size": 15 // the size of the in-memory storage with respect to a single batch | |
| }, | |
| "datasets":null | |
| } |