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Zero
| { | |
| "run_name": "mozilla-no-loc-fattn-stopnet-sigmoid-loss_masking", | |
| "run_description": "using forward attention, with original prenet, loss masking,separate stopnet, sigmoid. Compare this with 4817. Pytorch DPP", | |
| "audio":{ | |
| // Audio processing parameters | |
| "num_mels": 80, // size of the mel spec frame. | |
| "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame. | |
| "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled. | |
| "hop_length": 256, | |
| "win_length": 1024, | |
| "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. | |
| "min_level_db": -100, // normalization range | |
| "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air. | |
| "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. | |
| // Normalization parameters | |
| "signal_norm": true, // normalize the spec values in range [0, 1] | |
| "symmetric_norm": false, // move normalization to range [-1, 1] | |
| "max_norm": 1, // scale normalization to range [-max_norm, max_norm] or [0, max_norm] | |
| "clip_norm": true, // clip normalized values into the range. | |
| "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!! | |
| "do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true) | |
| }, | |
| "distributed":{ | |
| "backend": "nccl", | |
| "url": "tcp:\/\/localhost:54321" | |
| }, | |
| "reinit_layers": [], | |
| "model": "Tacotron2", // one of the model in models/ | |
| "grad_clip": 1, // upper limit for gradients for clipping. | |
| "epochs": 1000, // total number of epochs to train. | |
| "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate. | |
| "lr_decay": false, // if true, Noam learning rate decaying is applied through training. | |
| "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" | |
| "windowing": false, // Enables attention windowing. Used only in eval mode. | |
| "memory_size": 5, // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5. | |
| "attention_norm": "sigmoid", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron. | |
| "prenet_type": "original", // ONLY TACOTRON2 - "original" or "bn". | |
| "prenet_dropout": true, // ONLY TACOTRON2 - enable/disable dropout at prenet. | |
| "use_forward_attn": true, // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster. | |
| "forward_attn_mask": false, | |
| "attention_type": "original", | |
| "attention_heads": 5, | |
| "bidirectional_decoder": false, | |
| "transition_agent": false, // ONLY TACOTRON2 - enable/disable transition agent of forward attention. | |
| "location_attn": false, // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default. | |
| "loss_masking": true, // enable / disable loss masking against the sequence padding. | |
| "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars. | |
| "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. | |
| "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. | |
| "use_gst": false, | |
| "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. | |
| "batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. | |
| "eval_batch_size":16, | |
| "r": 1, // Number of frames to predict for step. | |
| "wd": 0.000001, // Weight decay weight. | |
| "checkpoint": true, // If true, it saves checkpoints per "save_step" | |
| "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints. | |
| "print_step": 10, // Number of steps to log traning on console. | |
| "batch_group_size": 0, //Number of batches to shuffle after bucketing. | |
| "run_eval": true, | |
| "test_delay_epochs": 5, //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. | |
| "data_path": "/media/erogol/data_ssd/Data/Mozilla/", // DATASET-RELATED: can overwritten from command argument | |
| "meta_file_train": "metadata_train.txt", // DATASET-RELATED: metafile for training dataloader. | |
| "meta_file_val": "metadata_val.txt", // DATASET-RELATED: metafile for evaluation dataloader. | |
| "dataset": "mozilla", // DATASET-RELATED: one of mozilla_voice_tts.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py | |
| "min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training | |
| "max_seq_len": 150, // DATASET-RELATED: maximum text length | |
| "output_path": "../keep/", // DATASET-RELATED: output path for all training outputs. | |
| "num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values. | |
| "num_val_loader_workers": 4, // number of evaluation data loader processes. | |
| "phoneme_cache_path": "mozilla_us_phonemes", // 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 | |
| "text_cleaner": "phoneme_cleaners", | |
| "use_speaker_embedding": false, // whether to use additional embeddings for separate speakers | |
| // MULTI-SPEAKER and GST | |
| "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning. | |
| "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 | |
| } | |
| } | |