Metadata-Version: 2.1
Name: pywhisper
Version: 1.0.1
Summary: openai/whisper speech to text model + extra features
Author: fcakyon
Keywords: machine-learning,deep-learning,ml,pytorch,speech-to-text,speech,text,nlp,natural-language-processing,openai,whisper
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# 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)
```
