ospeech 1.4.0

Minimum dependency inference library for OptiSpeech TTS models

ospeech

Minimum dependency inference library for OptiSpeech TTS model.

About OptiSpeech

OptiSpeech is ment to be an efficient, lightweight and fast text-to-speech model for on-device text-to-speech.

Install

This package can be installed using pip:

$ pip install ospeech

If you want to run the ospeech command from anywhere, try:

$ pipx install ospeech

Most models are trained with IPA phonemized text. To use these models, install ospeech with the espeak feature, which pulls-in piper-phonemize:

pip install ospeech[espeak]

If you want a gradio interface, install with the gradio feature:

pip install ospeech[gradio]

Usage

Obtaining models

$ ospeech-models --help
usage: ospeech-models [-h] {ls,dl} ...

List and download ospeech models from HuggingFace.

positional arguments:
  {ls,dl}
    ls        List available models
    dl        Download ospeech models from HuggingFace

options:
  -h, --help  show this help message and exit

To list available models:

$ ospeech-models ls
Lang  | Speaker                 | ID
---------------------------------------------------------------------
en-us | lightspeech-hfc-female  | en-us-lightspeech-hfc-female
en-us | convnext-tts-hfc-female | en-us-convnext-tts-hfc-female
---------------------------------------------------------------------

Using the model ID, use the following command to download a model:

$ ospeech-models dl en-us-lightspeech-hfc-female .
Downloading `en-us-lightspeech-hfc-female.onnx`
Downloading:   100%|                                                                           | 38/38 [00:02<?, ?MB/s]

Command line usage

$ ospeech --help
usage: ospeech [-h] [--d-factor D_FACTOR] [--p-factor P_FACTOR] [--e-factor E_FACTOR] [--no-split] [--cuda]
               onnx_path text output_dir

ONNX inference of OptiSpeech

positional arguments:
  onnx_path            Path to the exported OptiSpeech ONNX model
  text                 Text to speak
  output_dir           Directory to write generated audio to.

options:
  -h, --help           show this help message and exit
  --d-factor D_FACTOR  Scale to control speech rate.
  --p-factor P_FACTOR  Scale to control pitch.
  --e-factor E_FACTOR  Scale to control energy.
  --no-split           Don't split input text into sentences.
  --cuda               Use GPU for inference

If you want to run with the gradio interface:

$ ospeech-gradio --help
usage: ospeech-gradio [-h] [-s] [--host HOST] [--port PORT] [--char-limit CHAR_LIMIT] onnx_file_path

positional arguments:
  onnx_file_path        Path to model ONNX file

options:
  -h, --help            show this help message and exit
  -s, --share           Generate gradio share link
  --host HOST           Host to serve the app on.
  --port PORT           Port to serve the app on.
  --char-limit CHAR_LIMIT
                        Input text character limit.

Python API

import soundfile as sf
from ospeech import OptiSpeechONNXModel


model_path = "./optispeech-en-us-lightspeech.onnx"
sentence = "OptiSpeech is awesome!"

model = OptiSpeechONNXModel.from_onnx_file_path(model_path)
model_inputs= model.prepare_input(sentence)
outputs = model.synthesise(model_inputs)

for (idx, wav) in enumerate(outputs):
    # Wav is a float array
    sf.write(f"output-{idx}.wav", wav, model.sample_rate)

Licence

Copyright (c) Musharraf Omer. MIT Licence. See LICENSE for more details.