whisper-cpp-pybind 0.1.2

A Python wrapper for whisper.cpp

whisper-cpp-pybind: python bindings for whisper.cpp

whisper-cpp-pybind provides an interface for calling whisper.cpp in Python. And whisper.cpp provides accelerated inference for whisper models. This project provides both high-level and low-level API. The high-level API almost implement all the features of the main example of whisper.cpp

Installation

Install form PyPI

pip intall whisper-cpp-pybind

Install Locally

git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git
cd llama-cpp-python

# Install with pip
pip install -e .

Install with Hardware Acceleration

whisper.cpp supports multiple hardware for faster processing, so you can also install with the following command:

OpenBLAS

Make sure you have installed openblas: https://www.openblas.net/

# Install with with haradware acceleration (OpenBLAS)
pip install --config-settings="--build-option=--accelerate=openblas" .

cuBLAS

Make sure you have installed cuda for Nvidia cards: https://developer.nvidia.com/cuda-downloads

# Install with with haradware acceleration (cuBLAS)
pip install --config-settings="--build-option=--accelerate=cublas" .

CLBlast

For cards and integrated GPUs that support OpenCL, whisper.cpp can be largely offloaded to the GPU through CLBlast. This is especially useful for users with AMD APUs or low end devices for up to ~2x speedup.

Make sure you have installed CLBlast for your OS: https://github.com/CNugteren/CLBlast

# Install with with haradware acceleration (CLBlast)
pip install --config-settings="--build-option=--accelerate=clblast" .

CoreML

On Apple Silicon devices, whisper.cpp can be executed on the Apple Neural Engine (ANE) via Core ML.

Make sure you have installed CoreML environment for your OS: https://github.com/apple/coremltools

# Install with with haradware acceleration (CoreML)
pip install --config-settings="--build-option=--accelerate=coreml" .

OpenVINO

On Intel devices which have x86 CPUs and Intel GPUs (integrated & discrete) whisper.cpp can be accelerated using OpenVINO.

Make sure you have installed OpenVINO environment for your OS: https://github.com/openvinotoolkit/openvino

# Install with with haradware acceleration (OpenVINO)
pip install --config-settings="--build-option=--accelerate=openvino" .

Usage

High-level API

The high-level API provides two main interface through the Wisper class.

Below is a simple example demonstrating how to use the high-level API to transcribe a wav file:

from whisper_cpp import Whisper

whisper = Whisper("/../models/ggml-large.bin")

whisper.transcribe("samples.wav", diarize=True)

whisper.output(output_csv=True, output_jsn=True, output_lrc=True, output_srt=True, output_txt=True, output_vtt=True, log_score=True)

Low-level API

All functions provided by whisper.h are translated to python interfaces.

Below is an example to use low-level api to transcribe.

from ctypes import (
    c_float,
)
from whisper_cpp.whisper_cpp import (
    whisper_init_from_file,
    whisper_full_default_params,
    whisper_full_parallel,
    whisper_full_n_segments,
    whisper_full_get_segment_text,
    WHISPER_SAMPLING_GREEDY,
)
from whisper_cpp.utils import read_wav

pcmf32 = []
pcmf32s = []

if not read_wav("samples/samples.wav", pcmf32, pcmf32s, False):
    raise RuntimeError("Failed to read WAV file!")

whisper_ctx = whisper_init_from_file("../models/ggml-large.bin".encode("utf-8"))

wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY)

pcmf32_array = (c_float * len(pcmf32))(*(i for i in pcmf32))

whisper_full_parallel(
    ctx=whisper_ctx,
    params=wparams,
    samples=pcmf32_array,
    n_samples=len(pcmf32),
    n_processors=1
)

result = ""
n_segments = whisper_full_n_segments(whisper_ctx)
for i in range(n_segments):
    text = whisper_full_get_segment_text(whisper_ctx, i)
    result = result + text.decode('utf-8')

print(result)

Development

This package is under active development and any contributions will be welcomed.

License

whisper-cpp-pybind is released under the MIT License.