{
    "model": "Tacotron2",          // one of the model in models/  
    "run_name": "ljspeech-gravesv2",
    "run_description": "tacotron2 wuth graves attention",

    // AUDIO PARAMETERS
    "audio":{
        // 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,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
        "frame_length_ms": 50.0,  // stft window length in ms.
        "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
        "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.
    },

    // DISTRIBUTED TRAINING
    "distributed":{
        "backend": "nccl",
        "url": "tcp:\/\/localhost:54321"
    },

    "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": 2,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
    "eval_batch_size":16,   
    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.  
    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
    "grad_accum": 2,        // if N > 1, enable gradient accumulation for N iterations. It is useful for low memory GPUs. 

    // 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.
    "grad_clip": 1.0,                // 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.
    "wd": 0.000001,         // Weight decay weight.
    "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
    "seq_len_norm": false,	   // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
    
    // TACOTRON PRENET
    "memory_size": -1,              // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame. 
    "prenet_type": "original",     // "original" or "bn".
    "prenet_dropout": true,        // enable/disable dropout at prenet. 

    // ATTENTION
    "attention_type": "graves",  // 'original' or 'graves'
    "attention_heads": 4,          // number of attention heads (only for 'graves')
    "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
    "windowing": false,            // Enables attention windowing. Used only in eval mode.
    "use_forward_attn": false,      // if it uses forward attention. In general, it aligns faster.
    "forward_attn_mask": false,    // Additional masking forcing monotonicity only in eval mode.
    "transition_agent": false,     // enable/disable transition agent of forward attention.
    "location_attn": false,        // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
    "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.

    // STOPNET
    "stopnet": true,               // Train stopnet predicting the end of synthesis. 
    "separate_stopnet": true,     // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.

    // TENSORBOARD and LOGGING
    "print_step": 25,       // Number of steps to log traning on console.
    "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
    "text_cleaner": "phoneme_cleaners",
    "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
    "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.
    "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
    "min_seq_len": 6,       // DATASET-RELATED: minimum text length to use in training
    "max_seq_len": 150,     // DATASET-RELATED: maximum text length

    // PATHS
    // "output_path": "/data5/rw/pit/keep/",      // DATASET-RELATED: output path for all training outputs.
    "output_path": "/home/erogol/Models/LJSpeech/",
 
    // PHONEMES
    "phoneme_cache_path": "mozilla_us_phonemes",  // phoneme computation is slow, therefore, it caches results in the given folder.
    "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
    "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages

    // MULTI-SPEAKER and GST
    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
    "style_wav_for_test": null,          // path to style wav file to be used in TacotronGST inference.
    "use_gst": false,       // TACOTRON ONLY: use global style tokens

    // DATASETS
    "datasets":   // List of datasets. They all merged and they get different speaker_ids.
        [
            {
                "name": "ljspeech",
                "path": "/home/erogol/Data/LJSpeech-1.1/",
                // "path": "/home/erogol/Data/LJSpeech-1.1",
                "meta_file_train": "metadata.csv",
                "meta_file_val": null
            }
        ]

}