TTS/config_tacotron_de.json

{
                "run_name": "german-all-tacotrongst",
                "run_description": "train with all the german dataset using gst",
            
                "audio":{
                    // Audio processing parameters
                    "num_mels": 80,         // size of the mel spec frame. 
                    "num_freq": 1025,       // number of stft frequency levels. Size of the linear spectogram frame.
                    "sample_rate": 16000,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
                    "frame_length_ms": 50,  // stft window length in ms.
                    "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
                    "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": "TacotronGST",          // one of the model in models/    
                "grad_clip": 1,                // upper limit for gradients for clipping.
                "epochs": 10000,                // 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": false,      // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
                "transition_agent": false,     // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
                "forward_attn_mask": false,      
                "location_attn": true,        // 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. 
                
                "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
                "eval_batch_size":32,   
                "r": 5,                 // 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": false,
                "test_sentences_file": "de_sentences.txt",  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
                "test_delay_epochs": 5,  //Until attention is aligned, testing only wastes computation time.
                "data_path": "/home/erogol/Data/m-ai-labs/de_DE/by_book/" ,  // DATASET-RELATED: can overwritten from command argument
                "meta_file_train": null,      // DATASET-RELATED: metafile for training dataloader.
                "meta_file_val": null,    // DATASET-RELATED: metafile for evaluation dataloader.
                "dataset": "mailabs",      // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
                "min_seq_len": 15,       // DATASET-RELATED: minimum text length to use in training
                "max_seq_len": 200,     // DATASET-RELATED: maximum text length
                "output_path": "/home/erogol/Models/mozilla_models/",      // 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": "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": "de",     // 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
            }