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-pythonTo 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.binNavigate to http://localhost:8001/docs to see the OpenAPI documentation.
whisper-cpp-python is released under the MIT License. See LICENSE for details.