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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
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.
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)
FAQs
openai/whisper speech to text model + extra features
We found that pywhisper demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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