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How to add offline English speech to a Python app with Paradee

Install Paradee from GitHub and generate a WAV file from text on a CPU.

Short answer: Run pip install git+https://github.com/sahilmahendrakar/paradee to install the package; then run python -m paradee "Paradee is a small voice that runs anywhere." -o hello.wav to generate a test file; in your Python code, import Paradee and SAMPLE_RATE from Paradee, create tts = Paradee(), and call tts("your text") to get the audio array. All steps.

Who it is forA Python developer building a script, tool or prototype that needs spoken English output without a cloud TTS account or a GPU.

The problemCloud speech APIs need keys and network calls, and larger open TTS models are heavy to run on a laptop.

The outcomeA WAV file of a sentence, generated locally by the Paradee model in a few seconds per sentence.

What you need first

  • Python with pip installed
  • Internet access for the first run, to download the 8.5 MB model and misaki's English dictionary data
  • Audio output support (soundfile is used in the README example)

Steps

  1. Run pip install git+https://github.com/sahilmahendrakar/paradee to install the package.
  2. Run python -m paradee "Paradee is a small voice that runs anywhere." -o hello.wav to generate a test file.
  3. In your Python code, import Paradee and SAMPLE_RATE from Paradee, create tts = Paradee(), and call tts("your text") to get the audio array. (inferred, check the README)
  4. Write the array to a file with soundfile using sf.write("out.wav", audio, SAMPLE_RATE). (inferred, check the README)

Prompt for your agent

Install the GitHub repo sahilmahendrakar/paradee with pip, then generate speech for the text <your sentence> and save it to <output.wav>. Expect a WAV file at 24 kHz that plays back as the spoken sentence. Verify the file exists and is longer than zero seconds before reporting success.

Caveat. Paradee speaks only one voice, Kokoro's af_heart, and the GitHub description's 18x speed figure is for version 1.0 while the README gives 62x for version 1.1.

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