{
        "model": "glow_tts",
        "run_name": "glow-tts-tdsep-conv",
        "run_description": "glow-tts model training with time-depth separable conv encoder.",

        // 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": 0,     // reference level db, theoretically 20db is the sound of air.

            // Griffin-Lim
            "power": 1.1,           // 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.

            // 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.

            // MelSpectrogram parameters
            "num_mels": 80,         // size of the mel spec frame.
            "mel_fmin": 50.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
            "mel_fmax": 7600.0,     // 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": 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": 1.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
            "clip_norm": true,      // clip normalized values into the range.
            "stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy"    // 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
        },

        // VOCABULARY PARAMETERS
        // if custom character set is not defined,
        // default set in symbols.py is used
        // "characters":{
        //     "pad": "_",
        //     "eos": "~",
        //     "bos": "^",
        //     "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
        //     "punctuations":"!'(),-.:;? ",
        //     "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
        // },

        "add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.

        // 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.

        // MODEL PARAMETERS
        "use_mas": false,       // use Monotonic Alignment Search if true. Otherwise use pre-computed attention alignments.

        // TRAINING
        "batch_size": 32,       // 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": 1,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
        "loss_masking": true,   // enable / disable loss masking against the sequence padding.

        // VALIDATION
        "run_eval": true,
        "test_delay_epochs": 0,       //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": true,         // use noam warmup and lr schedule.
        "grad_clip": 5.0,              // upper limit for gradients for clipping.
        "epochs": 10000,               // total number of epochs to train.
        "lr": 1e-3,                    // 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.

        "encoder_type": "time-depth-separable",

        // TENSORBOARD and LOGGING
        "print_step": 25,       // Number of steps to log training on console.
        "tb_plot_step": 100,    // Number of steps to plot TB training figures.
        "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
        "save_step": 5000,      // 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.
        "apex_amp_level": null,

        // 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": 3,       // DATASET-RELATED: minimum text length to use in training
        "max_seq_len": 500,     // DATASET-RELATED: maximum text length
        "compute_f0": false,     // compute f0 values in data-loader

        // PATHS
        "output_path": "/home/erogol/Models/LJSpeech/",

        // PHONEMES
        "phoneme_cache_path": "/home/erogol/Models/phoneme_cache/",  // 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/",
                    "meta_file_train": "metadata.csv",
                    "meta_file_val": null
                    // "path_for_attn": "/home/erogol/Data/LJSpeech-1.1/alignments/"
                }
            ]
    }


