https://github.com/carloscdias/whisper-cpp-python
whisper.cpp bindings for python
https://github.com/carloscdias/whisper-cpp-python
python python3 whisper whisper-api whisper-cpp
Last synced: 2 months ago
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whisper.cpp bindings for python
- Host: GitHub
- URL: https://github.com/carloscdias/whisper-cpp-python
- Owner: carloscdias
- License: mit
- Created: 2023-05-07T17:42:43.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-24T15:14:37.000Z (about 2 years ago)
- Last Synced: 2025-03-11T22:34:25.850Z (7 months ago)
- Topics: python, python3, whisper, whisper-api, whisper-cpp
- Language: Python
- Homepage:
- Size: 79.1 KB
- Stars: 89
- Watchers: 6
- Forks: 23
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# whisper-cpp-python
whisper-cpp-python is a Python module inspired by [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) that provides a Python interface to the [whisper.cpp](https://github.com/ggerganov/whisper.cpp) model.
This module automatically parses the C++ header file of the project during building time, generating the corresponding Python bindings.
## Installation
To install the module, you can use pip:
```bash
pip install whisper-cpp-python
```
## Usage
To use the module, you need to create an instance of the `Whisper` class, passing the path to the model file as a parameter. Then, you can call the `transcribe` or `translate` method to transcribe or translate a given audio file.
### High-level API
The high-level API provides a simple managed interface through the `Wisper` class.
Below is a short example demonstrating how to use the high-level API to transcribe an mp3:
```python
>>> from whisper_cpp_python import Whisper
>>> whisper = Whisper(model_path="./models/ggml-tiny.bin")
>>> output = whisper.transcribe(open('samples/jfk.mp3'))
>>> print(output)
{'text': 'And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.'}
>>> output = whisper.transcribe(open('samples/jfk.mp3'), response_format='verbose_json')
>>> print(output)
{
'task': 'transcribe',
'language': 'en',
'duration': 11.0,
'text': 'And so, my fellow Americans ask not what your country can do for you, ask what you can do for your country.',
'segments': [{
'id': 0,
'seek': 0.0,
'start': 0.0,
'end': 10.98,
'text': ' And so, my fellow Americans ask not what your country can do for you, ask what you can do for your country.',
'tokens': [50364,
400,
370,
11,
452,
7177,
6280,
1029,
406,
437,
428,
1941,
393,
360,
337,
291,
11,
1029,
437,
291,
393,
360,
337,
428,
1941,
13,
50913],
'temperature': 0.800000011920929,
'avg_logprob': -0.3063158459133572,
'compression_ratio': 2.4000000953674316,
'no_speech_prob': 0.0,
'transient': False
}]
}
```
### Low-level API
All interfaces provided by `whisper.h` are available in python. The following example
show how to pass a custom `progress_callback` function to the model.
```python
from whisper_cpp_python import Whisper
from whisper_cpp_python.whisper_cpp import whisper_progress_callback
def callback(ctx, state, i, p):
print(i)
model = Whisper('../quantized_models/whisper/models/ggml-tiny.bin')
model.params.progress_callback = whisper_progress_callback(callback)
print(model.transcribe('vendor/whisper.cpp/samples/jfk.wav'))
```
## Web Server
`whisper-cpp-python` offers a web server which aims to act as a drop-in replacement for the OpenAI API.
This allows you to use whisper.cpp compatible models with any OpenAI compatible client (language libraries, services, etc).
To install the server package and get started:
```bash
pip install whisper-cpp-python[server]
python3 -m whisper_cpp_python.server --model models/ggml-tiny.bin
```
Navigate to [http://localhost:8001/docs](http://localhost:8001/docs) to see the OpenAPI documentation.
## License
whisper-cpp-python is released under the MIT License. See [LICENSE](LICENSE) for details.