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Simpler Whisper

Build and Test

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

There are three ways to use simpler-whisper:

1. Basic Usage

from simpler_whisper.whisper import WhisperModel

# Load the model (models can be downloaded from https://huggingface.co/ggerganov/whisper.cpp)
model = WhisperModel("path/to/model.bin", use_gpu=True)

# Load and prepare your audio
# You can use av, librosa, or any method that gives you 16kHz mono float32 samples
import av
container = av.open("audio.mp3")
audio_stream = container.streams.audio[0]
samples = np.concatenate([
    frame.to_ndarray().mean(axis=0) if frame.format.channels == 2 else frame.to_ndarray()
    for frame in container.decode(audio_stream)
])

# Transcribe
transcription = model.transcribe(samples)
for segment in transcription:
    print(f"{segment.text} ({segment.t0:.2f}s - {segment.t1:.2f}s)")

2. Async Processing

This will create a thread in the backend (not locked by the GIL) to allow for asynchronous transcription.

from simpler_whisper.whisper import AsyncWhisperModel

def handle_result(chunk_id: int, segments: List[WhisperSegment], is_partial: bool):
    text = " ".join([seg.text for seg in segments])
    print(f"Chunk {chunk_id}: {text}")

# Create and start async model
model = AsyncWhisperModel("path/to/model.bin", callback=handle_result, use_gpu=True)
model.start()

# Queue audio chunks for processing
chunk_id = model.transcribe(audio_samples)

# When done
model.stop()

3. Real-time Threaded Processing

This method creates a background thread for real-time transcription that will continuously process the input in e.g. 10 seconds chunks and report on both final or partial results.

from simpler_whisper.whisper import ThreadedWhisperModel

def handle_result(chunk_id: int, segments: List[WhisperSegment], is_partial: bool):
    text = " ".join([seg.text for seg in segments])
    print(f"Chunk {chunk_id}: {text}")

# Create and start threaded model with 10-second chunks
model = ThreadedWhisperModel(
    "path/to/model.bin",
    callback=handle_result,
    use_gpu=True,
    max_duration_sec=10.0
)
model.start()

# Queue audio chunks as they arrive
chunk_id = model.queue_audio(audio_samples)

# When done
model.stop()

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.