"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":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,
// 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
"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.
"test_delay_epochs":-1,//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":1.0,// upper limit for gradients for clipping.
"epochs":1,// total number of epochs to train.
"lr":0.002,// Initial learning rate. If Noam decay is active, maximum learning rate.
"warmup_steps":4000,// Noam decay steps to increase the learning rate from 0 to "lr"
// 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":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.:set n
"mixed_precision":false,
// DATA LOADING
"text_cleaner":"english_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_val_loader_workers":0,// number of evaluation data loader processes.
"batch_group_size":0,//Number of batches to shuffle after bucketing.
"min_seq_len":2,// DATASET-RELATED: minimum text length to use in training
"max_seq_len":300,// DATASET-RELATED: maximum text length
"compute_f0":false,// compute f0 values in data-loader
"compute_input_seq_cache":false,// if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage.
// 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 pronoun[ciation.
"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.
"use_external_speaker_embedding_file":false,// if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
"external_speaker_embedding_file":"/home/erogol/Data/libritts/speakers.json",// if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
// DATASETS
"datasets":// List of datasets. They all merged and they get different speaker_ids.
