pywhisper 1.0.6
openai/whisper speech to text model + extra features
License: MIT
Requires Python: >=3.7
Keywords: machine-learning,deep-learning,ml,pytorch,speech-to-text,speech,text,nlp,natural-language-processing,openai,whisper

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<h1>
  pywhisper
</h1>

<h4>
  <a href="https://github.com/openai/whisper/tree/9f70a352f9f8630ab3aa0d06af5cb9532bd8c21d">openai/whisper</a> + extra features
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    <a href="https://badge.fury.io/py/pywhisper"><img src="https://badge.fury.io/py/pywhisper.svg" alt="pypi version"></a>
    <a href="https://pepy.tech/project/pywhisper"><img src="https://pepy.tech/badge/pywhisper" alt="downloads"></a>
    <a href="https://github.com/fcakyon/pywhisper/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/yolov5-pip/actions/workflows/ci.yml/badge.svg" alt="ci testing"></a>
    <a href="https://github.com/fcakyon/pywhisper/actions/workflows/package_testing.yml"><img src="https://github.com/fcakyon/yolov5-pip/actions/workflows/package_testing.yml/badge.svg" alt="package testing"></a>
    <a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/twitter/follow/fcakyon?color=blue&logo=twitter&style=flat" alt="fcakyon twitter"></a>
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    <br>
    
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## 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

```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. 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:

```bash
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](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)
```