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Merge branch 'dev' of https://github.com/mozilla/TTS into dev

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erogol 2020-06-01 03:03:17 +02:00
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vocoder/configs/multiband-melgan_and_rwd_config.json Normal file
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{
"run_name": "multiband-melgan-rwd",
"run_description": "multibadn melgan with random window discriminator",
// AUDIO PARAMETERS
"audio":{
// stft parameters
"num_freq": 513, // 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.
// 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.
// Griffin-Lim
"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.
// 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!!
// 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
// "distributed":{
// "backend": "nccl",
// "url": "tcp:\/\/localhost:54321"
// },
// MODEL PARAMETERS
"use_pqmf": true,
// LOSS PARAMETERS
"use_stft_loss": true,
"use_mse_gan_loss": true,
"use_hinge_gan_loss": false,
"use_feat_match_loss": false, // use only with melgan discriminators
"stft_loss_weight": 1,
"mse_gan_loss_weight": 2.5,
"hinge_gan_loss_weight": 1,
"feat_match_loss_weight": 10.0,
"stft_loss_params": {
"n_ffts": [1024, 2048, 512],
"hop_lengths": [120, 240, 50],
"win_lengths": [600, 1200, 240]
},
"target_loss": "avg_G_loss", // loss value to pick the best model
// DISCRIMINATOR
// "discriminator_model": "melgan_multiscale_discriminator",
// "discriminator_model_params":{
// "base_channels": 16,
// "max_channels":1024,
// "downsample_factors":[4, 4, 4, 4]
// },
"steps_to_start_discriminator": 100000, // steps required to start GAN trainining.1
"discriminator_model": "random_window_discriminator",
"discriminator_model_params":{
"uncond_disc_donwsample_factors": [8, 4],
"cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
"cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
"window_sizes": [512, 1024, 2048, 4096, 8192]
},
// GENERATOR
"generator_model": "multiband_melgan_generator",
"generator_model_params": {
"upsample_factors":[2 ,2, 4, 4],
"num_res_blocks": 4
},
// DATASET
"data_path": "/root/LJSpeech-1.1/wavs/",
"seq_len": 16384,
"pad_short": 2000,
"conv_pad": 0,
"use_noise_augment": true,
"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": 64, // 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
"noam_schedule": false, // use noam warmup and lr schedule.
"warmup_steps_gen": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"warmup_steps_disc": 4000,
"epochs": 1000, // total number of epochs to train.
"wd": 0.000001, // Weight decay weight.
"lr_gen": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_disc": 0.0001,
"gen_clip_grad": 10.0,
"disc_clip_grad": 10.0,
// TENSORBOARD and LOGGING
"print_step": 25, // Number of steps to log traning on console.
"print_eval": false, // If True, it prints intermediate loss values in evalulation.
"save_step": 10000, // Number of training steps expected to save traninpg stats and 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": 4, // 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/Models/LJSpeech/"
}