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video-dubbing
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{
    "model": "Tacotron",
    "run_name": "test_sample_dataset_run",
    "run_description": "sample dataset test run",

    // AUDIO PARAMETERS
    "audio":{
        // stft parameters
        "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.
        "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 (true), 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!!
        "spec_gain": 20.0,

        // 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
    },

    // 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"
    // },

    // 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": 1,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
    "eval_batch_size":1,
    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
    "gradual_training": [[0, 7, 4]], //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.
    "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
    "mixed_precision": false,

    // 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.

    // LOSS SETTINGS
    "loss_masking": true,       // enable / disable loss masking against the sequence padding.
    "decoder_loss_alpha": 0.5,  // original decoder loss weight. If > 0, it is enabled
    "postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled
    "postnet_diff_spec_alpha": 0.25,     // differential spectral loss weight. If > 0, it is enabled
    "decoder_diff_spec_alpha": 0.25,     // differential spectral loss weight. If > 0, it is enabled
    "decoder_ssim_alpha": 0.5,     // decoder ssim loss weight. If > 0, it is enabled
    "postnet_ssim_alpha": 0.25,     // postnet ssim loss weight. If > 0, it is enabled
    "ga_alpha": 5.0,           // weight for guided attention loss. If > 0, guided attention is enabled.
    "stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples.

    // OPTIMIZER
    "noam_schedule": false,        // use noam warmup and lr schedule.
    "grad_clip": 1.0,              // upper limit for gradients for clipping.
    "epochs": 1,                // 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": "bn",            // "original" or "bn".
    "prenet_dropout": false,        // enable/disable dropout at prenet.

    // TACOTRON ATTENTION
    "attention_type": "original",  // 'original' , 'graves', 'dynamic_convolution'
    "attention_heads": 4,          // number of attention heads (only for 'graves')
    "attention_norm": "sigmoid",   // softmax or sigmoid.
    "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": true,         // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
    "bidirectional_decoder": true,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
    "double_decoder_consistency": false,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
    "ddc_r": 7,                           // reduction rate for coarse decoder.

    // 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": 1,       // 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": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
    "keep_all_best": true,  // If true, keeps all best_models after keep_after steps
    "keep_after": 10000,    // Global step after which to keep best models if keep_all_best is true
    "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": 0,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
    "num_eval_loader_workers": 0,    // 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": 153,     // DATASET-RELATED: maximum text length
    "compute_input_seq_cache": true,

    // PATHS
    "output_path": "tests/train_outputs/",

    // PHONEMES
    "phoneme_cache_path": "tests/train_outputs/phoneme_cache/",  // phoneme computation is slow, therefore, it caches results in the given folder.
    "use_phonemes": false,           // 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_d_vector_file": false,
    "d_vector_file": null,
    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
    "use_gst": true,       			    // use global style tokens
    "gst":	{			                // gst parameter if gst is enabled
        "gst_style_input": null,        // Condition the style input either on a
                                        // -> wave file [path to wave] or
                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
                                        // with the dictionary being len(dict) == len(gst_style_tokens).
        "gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
        "gst_embedding_dim": 512,
        "gst_num_heads": 4,
        "gst_style_tokens": 10
    },

    // DATASETS
    "train_portion": 0.1,  // dataset portion used for training. It is mainly for internal experiments.
    "eval_portion": 0.1,   // dataset portion used for training. It is mainly for internal experiments.
    "datasets":   // List of datasets. They all merged and they get different speaker_ids.
        [
            {
                "formatter": "ljspeech",
                "path": "tests/data/ljspeech/",
                "meta_file_train": "metadata.csv",
                "meta_file_val": "metadata.csv"
            }
        ]

}