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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": 8, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. | |
| "eval_batch_size": 8, | |
| "r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled. | |
| "gradual_training": [[0, 7, 4], [1, 5, 2]], //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": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset. | |
| "double_decoder_consistency": true, // 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" | |
| } | |
| ] | |
| } | |