tts2jk / tests /inputs /test_speedy_speech.json
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{
"model": "speedy_speech",
"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": 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
},
// VOCABULARY PARAMETERS
// if custom character set is not defined,
// default set in symbols.py is used
// "characters":{
// "pad": "_",
// "eos": "&",
// "bos": "*",
// "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
// "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
"positional_encoding": true,
"hidden_channels": 128,
"encoder_type": "residual_conv_bn",
"encoder_type": "residual_conv_bn",
"encoder_params":{
"kernel_size": 4,
"dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1],
"num_conv_blocks": 2,
"num_res_blocks": 13
},
"decoder_type": "residual_conv_bn",
"decoder_params":{
"kernel_size": 4,
"dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1],
"num_conv_blocks": 2,
"num_res_blocks": 17
},
// TRAINING
"batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
"eval_batch_size":32,
"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.
// LOSS PARAMETERS
"ssim_alpha": 1,
"l1_alpha": 1,
"huber_alpha": 1,
// VALIDATION
"run_eval": true,
"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_eval_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_d_vector_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
"d_vector_file": "/home/erogol/Data/libritts/speakers.json", // if not null and use_d_vector_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.
[
{
"formatter": "ljspeech",
"path": "tests/data/ljspeech/",
"meta_file_train": "metadata.csv",
"meta_file_val": "metadata.csv",
"meta_file_attn_mask": "tests/data/ljspeech/metadata_attn_mask.txt"
}
]
}