{
    "model_name": "TTS-dev-tweb",
    "model_description": "Higher dropout rate for stopnet and disabled custom initialization, pull current mel prediction to stopnet.",

    "audio":{
        "audio_processor": "audio",     // to use dictate different audio processors, if available.
        // Audio processing parameters
        "num_mels": 80,         // size of the mel spec frame. 
        "num_freq": 1025,       // number of stft frequency levels. Size of the linear spectogram frame.
        "sample_rate": 22050,   // wav sample-rate. If different than the original data, it is resampled.
        "frame_length_ms": 50,  // stft window length in ms.
        "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
        "preemphasis": 0.97,    // 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": false, // move normalization to range [-1, 1]
        "max_norm": 1,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
        "clip_norm": true,      // clip normalized values into the range.
        "mel_fmin": null,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
        "mel_fmax": null,        // maximum freq level for mel-spec. Tune for dataset!!
        "do_trim_silence": false  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
    },

    "embedding_size": 256,    
    "text_cleaner": "english_cleaners",
    "epochs": 1000,
    
    "lr": 0.001,
    "lr_decay": false,
    "warmup_steps": 4000,

    "batch_size": 20,
    "eval_batch_size":32,
    "r": 5,
    "wd": 0.000001,
    "checkpoint": true,
    "save_step": 5000,
    "print_step": 10,
    "tb_model_param_stats": true,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 

    "run_eval": true,
    "data_path": "../../Data/LJSpeech-1.1/",  // can overwritten from command argument
    "meta_file_train": "transcript.txt",      // metafile for training dataloader.
    "meta_file_val": "",    // metafile for evaluation dataloader.
    "dataset": "tweb",      // one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
    "min_seq_len": 0,       // minimum text length to use in training
    "max_seq_len": 300,     // maximum text length
    "output_path": "../keep/",      // output path for all training outputs.
    "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.
}