Synthesizing Speech

First, you need to install TTS. We recommend using PyPi. You need to call the command below:

$ pip install TTS

After the installation, 2 terminal commands are available.

  1. TTS Command Line Interface (CLI). - tts

  2. Local Demo Server. - tts-server

On the Commandline - tts

cli.gif

After the installation, 🐸TTS provides a CLI interface for synthesizing speech using pre-trained models. You can either use your own model or the release models under 🐸TTS.

Listing released 🐸TTS models.

tts --list_models

Run a TTS model, from the release models list, with its default vocoder. (Simply copy and paste the full model names from the list as arguments for the command below.)

tts --text "Text for TTS" \
    --model_name "<type>/<language>/<dataset>/<model_name>" \
    --out_path folder/to/save/output.wav

Run a tts and a vocoder model from the released model list. Note that not every vocoder is compatible with every TTS model.

tts --text "Text for TTS" \
    --model_name "<type>/<language>/<dataset>/<model_name>" \
    --vocoder_name "<type>/<language>/<dataset>/<model_name>" \
    --out_path folder/to/save/output.wav

Run your own TTS model (Using Griffin-Lim Vocoder)

tts --text "Text for TTS" \
    --model_path path/to/model.pth.tar \
    --config_path path/to/config.json \
    --out_path folder/to/save/output.wav

Run your own TTS and Vocoder models

tts --text "Text for TTS" \
    --config_path path/to/config.json \
    --model_path path/to/model.pth.tar \
    --out_path folder/to/save/output.wav \
    --vocoder_path path/to/vocoder.pth.tar \
    --vocoder_config_path path/to/vocoder_config.json

Run a multi-speaker TTS model from the released models list.

tts --model_name "<type>/<language>/<dataset>/<model_name>"  --list_speaker_idxs  # list the possible speaker IDs.
tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>"  --speaker_idx "<speaker_id>"

Note: You can use ./TTS/bin/synthesize.py if you prefer running tts from the TTS project folder.

On the Demo Server - tts-server

server.gif

You can boot up a demo 🐸TTS server to run an inference with your models. Note that the server is not optimized for performance but gives you an easy way to interact with the models.

The demo server provides pretty much the same interface as the CLI command.

tts-server -h # see the help
tts-server --list_models  # list the available models.

Run a TTS model, from the release models list, with its default vocoder. If the model you choose is a multi-speaker TTS model, you can select different speakers on the Web interface and synthesize speech.

tts-server --model_name "<type>/<language>/<dataset>/<model_name>"

Run a TTS and a vocoder model from the released model list. Note that not every vocoder is compatible with every TTS model.

tts-server --model_name "<type>/<language>/<dataset>/<model_name>" \
           --vocoder_name "<type>/<language>/<dataset>/<model_name>"

TorchHub

You can also use this simple colab notebook using TorchHub to synthesize speech.