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ljspeech--hifigan_v2_config.json

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+{
+    "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/"
+}
+
+

ljspeech--hifigan_v2_model_file.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4047e93886faa1aba11948efa71f59dcb0ec9117e286660e59b91892ef98d129
+size 3794153