$ show Uberi/speech_recognition
This repository is a well-established Python library (`SpeechRecognition`) that provides a unified interface for multiple speech recognition engines, including Google Speech, CMU Sphinx (PocketSphinx), Vosk, OpenAI Whisper (both local and API), Groq, Cohere, and several others. The architecture follows a facade pattern via a central `Recognizer` class, which handles audio capture (microphone, files) and delegates recognition to pluggable backends – each implemented as a standalone function in the `recognizers/` subpackage and attached to the `Recognizer` instance at import time. The library bundles FLAC encoder binaries for audio conversion, offers a CLI tool for model downloads, and includes comprehensive test suites with audio fixtures in multiple formats. While the code is well-structured and features clean abstractions (e.g., `AudioData` for format conversion, `OpenAICompatibleRecognizer` base class), it exhibits some technical drift: bundled FLAC binaries are outdated (v1.3.2 from 2014), it depends on `audioop` (deprecated in Python 3.13+), and several backend APIs are at risk of deprecation. The library is production-grade (v3.17.0) and widely used, but its reliance on hardcoded API keys and legacy workarounds for old Python versions suggests maintenance is ongoing but with moderate lag. Overall, it is a solid, reusable codebase with several components that could be extracted independently.
The LLM identified 3 files worth highlighting for reuse:
The `AudioData` class provides a unified interface for raw audio data with methods to convert between PCM, WAV, AIFF, and FLAC formats via a portable temporary file workaround (`PortableNamedTemporaryFile`). It also supports splitting audio into chunks (by duration or silence). This component is self-contained (only depends on standard library and `audioop`/`audioop-lts`) and could be reused in any project requiring audio format handling, independent of the speech recognition logic.
The `OpenAICompatibleRecognizer` abstract base class defines a consistent interface for interacting with any OpenAI-compatible speech-to-text API (e.g., OpenAI Whisper API, Groq). It handles API key configuration, endpoint URL building, and response parsing, leaving only the specific request construction to subclasses. This pattern is reusable for wrapping any REST-based speech recognition service and demonstrates clean separation of concerns.
The `sprc` CLI provides a simple command to download Vosk language models. It uses `argparse` and handles exceptions gracefully, printing progress via `tqdm`. This pattern shows how to add a minimal, useful command-line interface to a library for model or resource management, and could be adapted for other model download use cases.