- no need for ffmpeg cli installation, pip install is enough
- srt export
- progress bar for
transcribe - continious integration and package testing via github actions
pip install pywhisperYou 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.
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)