pywhisper
[openai/whisper](https://github.com/openai/whisper) + extra features
extra features
- no need for ffmpeg cli installation, pip install is enough
- srt export
- progress bar for `transcribe`
- continious integration and package testing via github actions
setup
bash
pip install pywhisper
You may need [`rust`](http://rust-lang.org) installed as well, in case [tokenizers](https://pypi.org/project/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](https://www.rust-lang.org/learn/get-started) to install Rust development environment.
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](pywhisper/tokenizer.py) for the list of all available languages.
python usage
Transcription can also be performed within Python:
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.
python
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)
What's Changed
* initial commit by fcakyon in https://github.com/fcakyon/pywhisper/pull/1
* add srt export, add cli test, improve test speed by fcakyon in https://github.com/fcakyon/pywhisper/pull/2
* add progress bar with tqdm by fcakyon in https://github.com/fcakyon/pywhisper/pull/3
New Contributors
* fcakyon made their first contribution in https://github.com/fcakyon/pywhisper/pull/1
**Full Changelog**: https://github.com/fcakyon/pywhisper/commits/1.0.0