---

name: speech-recognition description: "Guides SpeechRecognition package workflows for audio loading, microphone capture, speech recognizer engines, CLI/model setup, and repository maintenance." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SpeechRecognition Repo Skill

Use this repo skill when a task involves the Python SpeechRecognition package: local audio file handling, microphone capture, choosing a speech-to-text engine, setting up optional recognizer dependencies, using the sprc CLI, or maintaining the package repository.

Quick package facts

• Public distribution: SpeechRecognition

• Import package: speech_recognition

• Supported Python: >=3.10

• Console script: sprc

• Current distilled version: 3.17.0

• Minimal install:

  python -m pip install SpeechRecognition
  python - <<'PY'
  import speech_recognition as sr
  print(sr.__version__)
  print(sr.Recognizer())
  PY
  

Read references/repo-provenance.md before checking freshness against a checkout or deciding whether to refresh this skill. Read references/package-overview.md for package identity, optional extras, public entry points, and scope boundaries. Read references/troubleshooting.md for cross-cutting install/import, FLAC, optional dependency, credential, and CLI failures.

For a non-invasive runtime probe, run:

python scripts/check_install.py --json

Route by task

User task	Read
Load WAV/AIFF/FLAC files, create AudioData, convert to raw/WAV/AIFF/FLAC bytes, segment or split oversized audio	audio-data
Capture microphone input, calibrate ambient noise, use record, listen, streaming chunks, background callbacks, or threaded capture	capture-listening
Choose or call a transcription engine such as Google, PocketSphinx, Vosk, Whisper, OpenAI-compatible, Groq, Cohere, Google Cloud, or legacy APIs	recognition-engines
Install extras, check optional dependencies, use sprc, set up Vosk model files, or debug CLI/model setup	cli-model-setup
Edit or review the SpeechRecognition repository, choose focused tests, update docs/examples, or reason about CI/release/FLAC packaging	repo-development

Common operating sequence

1. Get audio. Use audio-data for existing files or capture-listening for live microphone input. Both produce speech_recognition.AudioData.
2. Prepare environment/model needs. Use cli-model-setup to install the exact extra for the chosen workflow and to check sprc/Vosk/optional imports without unsafe downloads.
3. Transcribe. Use recognition-engines to pick one engine and handle its parameters, credentials, return shape, and errors.
4. Validate or debug. Use the nearest sub-skill troubleshooting reference; use root troubleshooting only for cross-cutting install, FLAC, CLI, or optional-dependency failures.
5. If editing the repo itself, switch to repo-development and choose tests from the changed surface instead of using end-user workflows as maintainer checks.

Key boundaries

• This skill is for using and maintaining the SpeechRecognition package. It does not provide general ASR model training, diarization, TTS, or benchmark guidance outside the package's public surfaces.
• Optional engines are not base requirements. Install only the extra for the engine or workflow the user actually needs.
• Cloud/API recognizers need credentials and network access. Do not store secrets in code, skill files, or review artifacts.
• Microphone capture needs PyAudio/PortAudio plus real input hardware. Do not run interactive microphone commands in headless automation.
• sprc download vosk performs network download and writes into the installed package's model directory; ask before running it in a user environment.
• Runtime instructions here are self-contained. Do not rely on original repository examples or docs remaining available unless the task is explicitly repository maintenance in a checkout.