simpler-whisper 0.2.4.post2

A simple Python wrapper for whisper.cpp

Simpler Whisper

A zero-dependency simple Python wrapper for whisper.cpp, providing an easy-to-use interface for speech recognition using the Whisper model.

Why is it better than faster-whisper and pywhispercpp:

• Zero-dependency: Everything is shipped with the built wheel, no Python dependency (on av or ctranslate2 etc.) except for numpy.
• Dead simple API: call .transcribe() and get a result
• Acceleration enabled: supports whatever whisper.cpp supports
• Updated: using precompiled whisper.cpp from https://github.com/locaal-ai/occ-ai-dep-whispercpp
• Build time: builds in 2 minutes because it's using a precompiled binary

Installation

To install simpler-whisper, you need:

• A C++ compiler (e.g., GCC, Clang, or MSVC)
• CMake (version 3.12 or higher)
• NumPy

Then you can install using pip:

pip install simpler-whisper

Usage

import simpler_whisper.whisper
import numpy as np

# Load the model file.
# It's on you to download one from https://huggingface.co/ggerganov/whisper.cpp
model = simpler_whisper.whisper.load_model("path/to/model.bin")

# Load your 16kHz mono audio samples as a numpy array of float32
# It's on you if you need to convert (use av) or resample (use resampy)
audio = np.frombuffer(open("path/to/audio.raw", "rb").read(), dtype=np.float32)

# Transcribe
transcription = model.transcribe(audio)

print(transcription)

Platform-specific notes

• On Windows, the package uses a DLL (whisper.dll), which is included in the package.
• On Mac and Linux, the package uses static libraries that are linked into the extension.

Building from source

If you're building from source:

1. Clone the repository:

   git clone https://github.com/locaal-ai/simpler-whisper.git
   cd simpler-whisper
   

2. Install the package in editable mode:

   pip install -e .
   

This will run the CMake build process and compile the extension.

Build Configuration

Simpler Whisper supports various build configurations to optimize for different hardware and acceleration methods. You can specify the build configuration using environment variables:

• SIMPLER_WHISPER_ACCELERATION: Specifies the acceleration method. Options are:
  • cpu (default)
  • cuda (for NVIDIA GPUs)
  • hipblas (for AMD GPUs)
  • vulkan (for cross-platform GPU acceleration)

Example: Building for Windows with CUDA acceleration

$env:SIMPLER_WHISPER_ACCELERATION="cuda"
pip install .

Example: Building for macOS ARM64

pip install .

License

This project is licensed under the MIT License - see the LICENSE file for details.