whisper-cpp-python is a Python module inspired by llama-cpp-python that provides a Python interface to the whisper.cpp model. This module automatically parses the C++ header file of the project during building time, generating the corresponding Python bindings.
To install the module, you can use pip:
pip install whisper-cpp-python
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.
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:
>>> 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 }] }
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.
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'))
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:
pip install whisper-cpp-python[server] python3 -m whisper_cpp_python.server --model models/ggml-tiny.bin
Navigate to http://localhost:8001/docs to see the OpenAPI documentation.
whisper-cpp-python is released under the MIT License. See LICENSE for details.