---
id: pywhispercpp
version: "1.5.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# pywhispercpp — Python bindings for whisper.cpp

License: permissive · Maintenance: active · Downloads: 232.0K/mo

## What it is and what it does

pywhispercpp wraps the C++ implementation of OpenAI's Whisper speech recognition model, exposing it through a Python API. It downloads and caches models automatically, then transcribes audio files to text, returning segments with timing information. The package supports multiple backends (CUDA, CoreML, Vulkan, OpenBLAS, OpenVINO) via environment variables at install time, allowing optimization for different hardware. It depends on numpy, requests, tqdm, and platformdirs for core functionality.

You use it by instantiating a Model with a model name (e.g., 'base.en'), then calling transcribe() on an audio file path. The transcription runs locally without external API calls. Optional ffmpeg installation extends support beyond WAV to MP3, FLAC, and other formats. A CLI tool (pwcpp) and callback-based API enable both batch and streaming workflows.

Use it for:

- Transcribe audio files offline without sending data to cloud APIs, for privacy-sensitive applications
- Build a local speech-to-text service with GPU acceleration on supported hardware platforms
- Batch-process media files to generate subtitles (SRT, VTT) or transcripts in multiple output formats
- Integrate real-time transcription into Python applications via segment callbacks during inference
- Translate non-English audio to English text using the built-in translation capability

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

pywhispercpp provides Python bindings for whisper.cpp, enabling speech-to-text transcription with support for multiple hardware accelerators and output formats.

Yes, if you need local, offline speech-to-text transcription. The package is actively maintained, has no known vulnerabilities, and offers broad hardware acceleration options. Install friction is moderate due to C++ compilation, but pre-built wheels ease setup for common platforms. The MIT license imposes no restrictions. Verify that pre-built wheels include the accelerator support you need, or be prepared to build from source with environment flags.

## Install

pip install pywhispercpp
uv add pywhispercpp
poetry add pywhispercpp

## Installing pywhispercpp

Before you install: Medium install friction due to compiled C++ bindings; pre-built wheels cover multiple Python versions on Linux, macOS, and Windows, but source builds require a C++ toolchain. Optional ffmpeg dependency for non-WAV formats. Active maintenance with recent release.

License in practice: MIT license is permissive; you may use, modify, and distribute pywhispercpp freely in commercial and private projects with minimal restrictions.

Quickstart:

pip install pywhispercpp

from pywhispercpp.model import Model

model = Model('base.en')
segments = model.transcribe('file.wav')
for segment in segments:
    print(segment.text)

ffmpeg must be installed on the system to transcribe non-WAV formats (MP3, FLAC, etc.); WAV files work without it. For GPU acceleration, CUDA, CoreML, or other backends must be installed before building from source.

Verify before relying:

- Whether pre-built wheels include CUDA, CoreML, Vulkan, or other accelerator support, or if source builds are required for GPU/specialized hardware
- Actual transcription speed and accuracy compared to other Whisper implementations
- Memory footprint and latency characteristics for different model sizes

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 232.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

speech to text transcription, whisper.cpp python bindings, audio transcription with gpu, offline speech recognition, multilingual audio transcription, whisper model inference, audio to text conversion, speech-recognition, offline-inference, audio-processing

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