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name: python-asr-pipelines description: "Default Python ASR route for FunASR: AutoModel, funasr CLI, batch transcription, subtitle generation, hotword correction, audio-byte decoding, timestamps, punctuation-aware ASR, speaker verification, and common non-LLM model selection." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

python-asr-pipelines

Use this sub-skill for everyday local FunASR work: transcribe a file or folder, generate subtitles, choose a first model, inspect AutoModel inputs and outputs, handle hotwords, or interpret timestamps and speaker labels.

Start here

• Python API and return shapes: references/api-reference.md
• funasr CLI flags and outputs: references/cli-reference.md
• Practical workflows and model choice: references/workflows.md
• Recovery notes for common failures: references/troubleshooting.md
• Batch transcription helper: scripts/batch_transcribe.py
• Subtitle helper: scripts/generate_subtitles.py

This sub-skill owns

• AutoModel for offline Python ASR
• funasr CLI usage
• batch transcription and subtitle generation
• audio bytes and raw PCM decoding
• timestamps, sentence segmentation, and speaker labels
• hotword correction on final text
• non-LLM model selection among common checkpoints such as SenseVoice and Paraformer

Route elsewhere when needed

• Punctuation cleanup or full ITN/TN: text-normalization
• OpenAI API, realtime WS, MCP, browser/client, runtime SDK: serving-and-runtime
• Fun-ASR-Nano / GLM-ASR / Qwen3-ASR / AutoModelVLLM: llm-asr-and-vllm
• Training, manifests, export, and local inference after export: training-data-and-export

Helpful starting points

• CPU-friendly first try: SenseVoiceSmall
• Mandarin production with hotwords or timestamps: Paraformer
• Speaker-aware meeting transcript: AutoModel with vad_model and optional spk_model
• Import smoke: import funasr should work before AutoModel is touched, but AutoModel itself requires PyTorch