asr-skill

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name: asr description: This skill should be used when the user asks to "transcribe audio", "transcribe video", "convert speech to text", "generate subtitles", "create captions", "identify speakers in audio", or mentions audio/video transcription needs. Provides local ASR transcription with speaker diarization using FunASR.

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ASR Transcription Skill

Provide local audio/video transcription with speaker diarization, multiple output formats, and progress indication.

Purpose

Enable users to transcribe audio and video files to text with automatic speaker identification, supporting multiple subtitle formats while preserving privacy through local processing.

When to Use

This skill triggers when the user:

Wants to transcribe an audio file (MP3, WAV, M4A, FLAC)

Wants to transcribe a video file (MP4, AVI, MKV)

Needs subtitles or captions generated from media

Wants to identify different speakers in audio

Needs timestamped transcription output

Quick Start

Basic Transcription

# Transcribe audio file (outputs TXT by default)
python3 skills/asr/scripts/transcribe.py path/to/audio.mp3
Transcribe video file
python3 skills/asr/scripts/transcribe.py path/to/video.mp4

Output Formats

python3 skills/asr/scripts/transcribe.py audio.mp3 -f json   # Structured JSON with metadata
python3 skills/asr/scripts/transcribe.py audio.mp3 -f srt    # SubRip subtitles
python3 skills/asr/scripts/transcribe.py audio.mp3 -f ass    # ASS/SSA subtitles with speaker styling
python3 skills/asr/scripts/transcribe.py audio.mp3 -f md     # Markdown with speaker sections

Python API

from asr_skill import transcribe
result = transcribe("meeting.mp4", format="srt")
print(f"Output: {result['output_path']}")
print(f"Speakers: {result.get('speakers', [])}")

Asynchronous Execution (Recommended for Long Files)

Avoid timeouts by running transcription in the background:

# Start async task
python3 skills/asr/scripts/transcribe.py long_video.mp4 --async
Output: {"task_id": "a1b2c3d4", "status": "queued", ...}
Check status
python3 skills/asr/scripts/transcribe.py --status a1b2c3d4
Output: {"task_id": "a1b2c3d4", "status": "processing", "progress": 45, ...}
List recent tasks
python3 skills/asr/scripts/transcribe.py --list

Core Features

Speaker Diarization

Automatically identifies and labels different speakers:

Speaker A, Speaker B, Speaker C, etc.

Per-segment timestamps

Overlap detection marked with [OVERLAP]

Hardware Auto-Detection

Detects and uses the best available hardware:

CUDA GPU (NVIDIA)

Apple MPS (Apple Silicon)

CPU fallback with notification

Long Audio Support

Handles audio files longer than 1 hour:

VAD-based intelligent segmentation

Memory-efficient processing

Progress indication during transcription

Multiple Output Formats

| Format | Extension | Use Case | |--------|-----------|----------| | txt | .txt | Plain text with timestamps | | json | .json | Structured data with word-level info | | srt | .srt | Video subtitles | | ass | .ass | Styled subtitles | | md | .md | Documentation with speaker sections |

Implementation Details

Processing Pipeline

1. Input validation - Check file exists and format supported 2. Hardware detection - Auto-detect GPU/MPS/CPU 3. Video extraction - Extract audio from video files via FFmpeg 4. Audio preprocessing - Resample to 16kHz mono 5. Model loading - Load FunASR models (cached locally) 6. Transcription - Run ASR with speaker diarization 7. Formatting - Output in requested format 8. Cleanup - Remove temporary files

Model Components

ASR Model: Paraformer-large (Chinese optimized)

VAD Model: FSMN-VAD (voice activity detection)

Punctuation: CT-Transformer

Speaker: CAM++ (speaker diarization)

File Locations

Models cached in: ./models/

Output defaults to: same directory as input

Temp files: auto-cleaned after processing

Troubleshooting

Common Issues

"FFmpeg not found"

FFmpeg auto-installed via imageio-ffmpeg

Check internet connection for first run

"CUDA out of memory"

System falls back to CPU automatically

Try shorter audio segments

"No speakers detected"

Speaker diarization requires multi-speaker audio

Single speaker audio shows "Speaker A" only

Additional Resources

Reference Files

For detailed format specifications:

references/output-formats.md - Complete format documentation

Scripts

Utility scripts for batch processing:

scripts/transcribe.py - Batch transcription script

Examples

Working examples:

examples/basic_usage.py - Python API examples

examples/cli_examples.sh - CLI usage examples

Requirements

Python >= 3.10

FunASR (auto-installed)

FFmpeg (auto-installed via imageio-ffmpeg for video)

Notes

First run downloads models (~1GB total)

All processing happens locally for privacy

Chinese language optimized for v1

Working examples:

examples/basic_usage.py - Python API examples

examples/cli_examples.sh - CLI usage examples

First run downloads models (~1GB total)

All processing happens locally for privacy

Chinese language optimized for v1