{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"pywhispercpp provides Python bindings for whisper.cpp, enabling speech-to-text transcription with support for multiple hardware accelerators and output formats.","skillfed_tags":["speech-recognition","offline-inference","audio-processing"],"use_cases":["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"],"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.\n\nYou 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.","worth_installing":"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."},"id":"pywhispercpp","links":{"html":"https://skillfed.io/packages/pywhispercpp","md":"https://skillfed.io/packages/pywhispercpp.md","pypi":"https://pypi.org/project/pywhispercpp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-30","license_spdx":null,"license_treatment":"permissive","name":"pywhispercpp","python_support":"supports_current","summary":"Python bindings for whisper.cpp"},"popularity":{"monthly_downloads":232045,"position":9076,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.0"}