My settings for Mozilla/TTS Docker

README.md

All dashes are subdirectory's, make sure they are where they belong.

Branch Loaded is: https://github.com/mozilla/TTS/wiki/Released-Models -> Tacotron2-iter-260K https://github.com/mozilla/TTS/tree/Tacotron2-iter-260K-824c091

Model is from the following link tree: https://github.com/mozilla/TTS/wiki/Released-Models -> Tacotron2-iter-260K https://drive.google.com/drive/folders/1FJRjGDAqWIyZRX4CsppaIPEW8UWXCWzF -> checkpoint_261000.pth.tar https://drive.google.com/file/d/1otOqpixEsHf7SbOZIcttv3O7pG0EadDx/view

Model needs to be in the server directory. (and untouched.)

TTS-config.json

{
    "run_name": "ljspeech",
    "run_description": "finetune 4241 for align with architectural changes",

    "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": 22050,   // 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": [],  //set which layers to be reinitialized in finetunning. Only used if --restore_model is provided.

    "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": "softmax",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
    "prenet_type": "bn",    // ONLY TACOTRON2 - "original" or "bn".
    "use_forward_attn": true,    // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
    "transition_agent": true,    // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
    "loss_masking": false,       // enable / disable loss masking against the sequence padding.
    "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.


    "batch_size": 16,       // 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": 100,      // Number of steps to log traning on console.
    "tb_model_param_stats": true,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
    "batch_group_size": 8,  // Number of batches to shuffle after bucketing.

    "run_eval": true,
    "test_delay_epochs": 2,  //Until attention is aligned, testing only wastes computation time.
    "data_path": "/home/erogol/Data/LJSpeech-1.1",  // DATASET-RELATED: can overwritten from command argument
    "meta_file_train": "metadata_train.csv",      // DATASET-RELATED: metafile for training dataloader.
    "meta_file_val": "metadata_val.csv",    // DATASET-RELATED: metafile for evaluation dataloader.
    "dataset": "ljspeech",      // 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": 0,       // DATASET-RELATED: minimum text length to use in training
    "max_seq_len": 150,     // DATASET-RELATED: maximum text length
    "output_path": "/media/erogol/data_ssd/Data/models/ljspeech_models/",      // DATASET-RELATED: output path for all training outputs.
    "num_loader_workers": 8,        // 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": "ljspeech_phonemes",  // phoneme computation is slow, therefore, it caches results in the given folder.
    "use_phonemes": true,           // 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"
}

TTS-Dockerfile

FROM nvidia/cuda:9.0-base-ubuntu16.04 as base

WORKDIR /srv/app

RUN apt-get update && \
	apt-get install -y git software-properties-common wget vim build-essential libsndfile1 espeak && \
	add-apt-repository ppa:deadsnakes/ppa && \
	apt-get update && \
	apt-get install -y python3.6 python3.6-dev python3.6-tk && \
	# Install pip manually
	wget https://bootstrap.pypa.io/get-pip.py && \
	python3.6 get-pip.py && \
	rm get-pip.py && \
	# Used by the server in server/synthesizer.py
	pip install soundfile

ADD . /srv/app

# Setup for development
RUN python3.6 setup.py develop

# http://bugs.python.org/issue19846
# > At the moment, setting "LANG=C" on a Linux system *fundamentally breaks Python 3*, and that's not OK.
ENV LANG C.UTF-8

CMD python3.6 server/server.py -c server/conf.json

TTS-server-conf.json

{
    "model_path":"./server",
    "model_name":"checkpoint_261000.pth.tar",
    "model_config":"config.json",
    "port": 5002,
    "use_cuda": true
}

TTS-server-config.json

{
"github_branch":"dev-tacotron2",
"restore_path":"/media/erogol/data_ssd/Data/models/ljspeech_models/4241_best_t2_model/best_model.pth.tar",
    "run_name": "ljspeech",
    "run_description": "finetune 4241 for align with architectural changes",

    "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": 22050,   // 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": [],  //set which layers to be reinitialized in finetunning. Only used if --restore_model is provided.

    "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": "softmax",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
    "prenet_type": "bn",    // ONLY TACOTRON2 - "original" or "bn".
    "use_forward_attn": true,    // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
    "transition_agent": false,    // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
    "loss_masking": false,       // enable / disable loss masking against the sequence padding.
    "enable_eos_bos_chars": true, // enable/disable beginning of sentence and end of sentence chars.

    "batch_size": 16,       // 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": 100,      // Number of steps to log traning on console.
    "tb_model_param_stats": true,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
    "batch_group_size": 8,  // Number of batches to shuffle after bucketing.

    "run_eval": true,
    "test_delay_epochs": 2,  //Until attention is aligned, testing only wastes computation time.
    "data_path": "/home/erogol/Data/LJSpeech-1.1",  // DATASET-RELATED: can overwritten from command argument
    "meta_file_train": "metadata_train.csv",      // DATASET-RELATED: metafile for training dataloader.
    "meta_file_val": "metadata_val.csv",    // DATASET-RELATED: metafile for evaluation dataloader.
    "dataset": "ljspeech",      // 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": 0,       // DATASET-RELATED: minimum text length to use in training
    "max_seq_len": 150,     // DATASET-RELATED: maximum text length
    "output_path": "/media/erogol/data_ssd/Data/models/ljspeech_models/",      // DATASET-RELATED: output path for all training outputs.
    "num_loader_workers": 8,        // 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": "ljspeech_phonemes",  // phoneme computation is slow, therefore, it caches results in the given folder.
    "use_phonemes": true,           // 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"
}