etosworld / etos-tts Goto Github PK

ETOS TTS, aims to build a neural text-to-speech (TTS) that is able to transform text to speech in voices that are sampled in the wild. It is a PyTorch Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model.

• python 3.6 or later
• pytorch 0.4 is tested
• for ubuntu, sudo apt install libsndfile1

you can use pip to install other requirements.

pip3 install -r requirements.txt

you can use pretrained model under models/may22 and run the tts web server:

python server.py -c server_conf.json

Then go to http://127.0.0.1:8000 and enjoy.

Currently TTS provides data loaders for

• LJ Speech

To run your own training, you need to define a config.json file (simple template below) and call with the command.

train.py --config_path config.json

If you like to use specific set of GPUs.

CUDA_VISIBLE_DEVICES="0,1,4" train.py --config_path config.json

Each run creates an experiment folder with the corresponfing date and time, under the folder you set in config.json. And if there is no checkpoint yet under that folder, it is going to be removed when you press Ctrl+C.

You can also enjoy Tensorboard with couple of good training logs, if you point --logdir the experiment folder.

Example config.json:

{
  "num_mels": 80,
  "num_freq": 1025,
  "sample_rate": 22050,
  "frame_length_ms": 50,
  "frame_shift_ms": 12.5,
  "preemphasis": 0.97,
  "min_level_db": -100,
  "ref_level_db": 20,
  "embedding_size": 256,
  "text_cleaner": "english_cleaners",

  "epochs": 200,
  "lr": 0.002,
  "warmup_steps": 4000,
  "batch_size": 32,
  "eval_batch_size":32,
  "r": 5,
  "mk": 0.0,  // guidede attention loss weight. if 0 no use
  "priority_freq": true,  // freq range emphasis

  "griffin_lim_iters": 60,
  "power": 1.2,

  "dataset": "TWEB",
  "meta_file_train": "transcript_train.txt",
  "meta_file_val": "transcript_val.txt",
  "data_path": "/data/shared/BibleSpeech/",
  "min_seq_len": 0,
  "num_loader_workers": 8,

  "checkpoint": true,  // if save checkpoint per save_step
  "save_step": 200,
  "output_path": "/path/to/my_experiment",
}

• wavenet vocoder for better quality
• IAF or NAF for real time performance

• Efficient Neural Audio Synthesis
• Attention-Based models for speech recognition
• Generating Sequences With Recurrent Neural Networks
• Char2Wav: End-to-End Speech Synthesis
• VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
• WaveRNN
• Faster WaveNet
• Parallel WaveNet
• NAF

• https://github.com/keithito/tacotron
• https://github.com/r9y9/tacotron_pytorch
• https://github.com/mozilla/TTS