pywhisper 1.0.6

openai/whisper speech to text model + extra features

pywhisper

openai/whisper + extra features

extra features

• easy installation from pypi
• no need for ffmpeg cli installation, pip install is enough
• continious integration and package testing via github actions

setup

pip install pywhisper

You may need rust installed as well, in case tokenizers does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH". If the installation fails with No module named 'setuptools_rust', you need to install setuptools_rust, e.g. by running:

pip install setuptools-rust

command-line usage

The following command will transcribe speech in audio files, using the medium model:

pywhisper audio.flac audio.mp3 audio.wav --model medium

The default setting (which selects the small model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the --language option:

pywhisper japanese.wav --language Japanese

Adding --task translate will translate the speech into English:

pywhisper japanese.wav --language Japanese --task translate

Run the following to view all available options:

pywhisper --help

See tokenizer.py for the list of all available languages.

python usage

Transcription can also be performed within Python:

import pywhisper

model = pywhisper.load_model("base")
result = model.transcribe("audio.mp3")
print(result["text"])

Internally, the transcribe() method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.

Below is an example usage of pywhisper.detect_language() and pywhisper.decode() which provide lower-level access to the model.

import pywhisper

model = pywhisper.load_model("base")

# load audio and pad/trim it to fit 30 seconds
audio = pywhisper.load_audio("audio.mp3")
audio = pywhisper.pad_or_trim(audio)

# make log-Mel spectrogram and move to the same device as the model
mel = pywhisper.log_mel_spectrogram(audio).to(model.device)

# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")

# decode the audio
options = pywhisper.DecodingOptions()
result = pywhisper.decode(model, mel, options)

# print the recognized text
print(result.text)