Training Glow_TTS custom data set, config questions

[Wayne-wonderai]

Hi, I am trying to train my own Glow-TTS with custom dataset. I have a couple of questions.

1. In the config file below, under normalization parameters, I set "max_norm" to 1.0 The thing cannot run.
   However, if I change it to 4.0 it can run. Why is that? what is the meaning of this "max_norm".??

2. What does the preemphasis configuration under audio processing parameters mean? what does it do? does someone have an example of how to tweak that setting?

3. In the config file, does the setting in Griffin Lim matter? From what I udnerstand, if we attach glowtts to a vocoder i.e. melgan or hifigan, do the configurations under Griffin Lim matter?

{
    "model": "glow_tts",
    "run_name": "test_glow_tts",
    "run_description": "glow-tts model training with gated conv.",

    // 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.

        // 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.

        // 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": 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": "./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
    "mixed_precision": false,
    "distributed":{
        "backend": "nccl",
        "url": "tcp:\/\/localhost:54323"
    },

    "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": 2,       // 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": 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.
    "data_dep_init_iter": 1,

    // 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": 1,               // 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.

    "hidden_channels_encoder": 192,
        "hidden_channels_decoder": 192,
        "hidden_channels_duration_predictor": 256,
        "use_encoder_prenet": true,
        "encoder_type": "rel_pos_transformer",
        "encoder_params": {
            "kernel_size":3,
            "dropout_p": 0.1,
            "num_layers": 6,
            "num_heads": 2,
            "hidden_channels_ffn": 768,
            "input_length": null
    },

    // 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"
    "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.
    "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": 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": 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
    "compute_input_seq_cache": true,
    "use_noise_augment": true,

    // PATHS
    "output_path": "./train_outputs/",

    // PHONEMES
    "phoneme_cache_path": "tests/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.

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