{ "model": "align_tts", "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": 256, "hidden_channels_dp": 256, "encoder_type": "fftransformer", "encoder_params":{ "hidden_channels_ffn": 1024 , "num_heads": 2, "num_layers": 6, "dropout_p": 0.1 }, "decoder_type": "fftransformer", "decoder_params":{ "hidden_channels_ffn": 1024 , "num_heads": 2, "num_layers": 6, "dropout_p": 0.1 }, // 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. "phase_start_steps": null, // LOSS PARAMETERS "ssim_alpha": 1, "spec_loss_alpha": 1, "dur_loss_alpha": 1, "mdn_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": null } ] }