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BytesAgain โ€บ Skills โ€บ SenseVoice Transcribe
๐Ÿฆ€ ClawHub

SenseVoice Transcribe

by @ylongw

Transcribe audio files (WAV/MP3/M4A/FLAC) to timestamped text using SenseVoice-Small + FSMN-VAD. Supports single-file and batch mode with VAD-anchored per-se...

Versionv1.0.0
Downloads729
#clawhub
TERMINAL
clawhub install sensevoice-transcribe

๐Ÿ“– About This Skill

name: sensevoice-transcribe description: Transcribe audio files (WAV/MP3/M4A/FLAC) to timestamped text using SenseVoice-Small + FSMN-VAD. Supports single-file and batch mode with VAD-anchored per-segment timestamps (~15s granularity). Use when the user wants to transcribe speech/audio, run batch transcription on daylog recordings, or re-transcribe specific dates. Replaces the old whisper-transcribe skill.

SenseVoice Transcribe

Transcribe audio to timestamped text using FunASR's iic/SenseVoiceSmall model with fsmn-vad for timestamp anchoring.

Pipeline

1. FSMN-VAD segments audio into speech regions (~258 segments for 30min file) 2. SenseVoice-Small transcribes full audio with merge_vad=True 3. Raw text is split by <|zh|> tags โ†’ cleaned via rich_transcription_postprocess() 4. Text segments are proportionally mapped to VAD timestamps 5. Output: [HH:MM:SS] text per line, ~15s granularity

Environment

Venv: ~/.openclaw/venvs/sensevoice/
Python: 3.12
Key packages: funasr==1.3.1, modelscope, onnxruntime
Model cache: ~/.cache/modelscope/hub/models/iic/SenseVoiceSmall
VAD cache: ~/.cache/modelscope/hub/models/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch

First-time Setup

python3 -m venv ~/.openclaw/venvs/sensevoice
source ~/.openclaw/venvs/sensevoice/bin/activate
pip install funasr modelscope onnxruntime

Models auto-download on first run (~234MB SenseVoice + ~4MB VAD)

Usage

Single File

source ~/.openclaw/venvs/sensevoice/bin/activate
python3 -c "
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
from datetime import datetime, timedelta
import re
wav = ''

Parse start time from filename: TX01_MIC015_20260308_124130_orig.wav

m = re.search(r'(\d{8})_(\d{6})', wav)
start_dt = datetime.strptime(m.group(1)+m.group(2), '%Y%m%d%H%M%S') if m else None
vad_model = AutoModel(model='fsmn-vad', disable_update=True)
model = AutoModel(model='iic/SenseVoiceSmall', vad_model='fsmn-vad',
                  vad_kwargs={'max_single_segment_time': 30000}, device='cpu')
vad_segs = vad_model.generate(input=wav)[0].get('value', [])
res = model.generate(input=wav, cache={}, language='zh', use_itn=True,
                     batch_size_s=60, merge_vad=True)
texts = [rich_transcription_postprocess(s).strip()
         for s in re.split(r'<\|zh\|>', res[0]['text']) if s.strip()]
texts = [s for s in texts if len(s) > 1]
ratio = len(vad_segs) / len(texts) if texts else 1
for i, t in enumerate(texts):
    vi = min(int(i * ratio), len(vad_segs)-1)
    ts = (start_dt + timedelta(milliseconds=vad_segs[vi][0])).strftime('%H:%M:%S') if start_dt else f'{vad_segs[vi][0]//1000:.0f}s'
    print(f'[{ts}] {t}')
"

Batch Mode (daylog)

The bundled scripts/batch_transcribe.py handles the full daylog pipeline:

source ~/.openclaw/venvs/sensevoice/bin/activate
cd ~/Documents/dec/daylog
Dry run โ€” see what would be transcribed

python3 scripts/batch_transcribe.py --dry-run
Transcribe all new files

python3 scripts/batch_transcribe.py
Re-transcribe specific dates (deletes existing, then re-runs)

python3 scripts/batch_transcribe.py --force-dates 2026-03-07,2026-03-08
With progress file + Discord webhook

python3 scripts/batch_transcribe.py \
  --progress-file /tmp/daylog-progress.json \
  --discord-webhook https://discord.com/api/webhooks/...

Flags: | Flag | Description | |------|-------------| | --dry-run | Preview without writing | | --engine sensevoice\|whisper | Engine (default: sensevoice) | | --force-dates YYYY-MM-DD,... | Delete & re-transcribe these dates | | --progress-file PATH | Write JSON progress for monitoring | | --discord-webhook URL | Post start/milestone/finish to Discord |

Directory layout: 

daylog/
โ”œโ”€โ”€ raw/                          # WAV input (DJI MIC 3, 48kHz/32bit, ~247MB/30min)
โ”‚   โ”œโ”€โ”€ TX01_MIC009_20260308_094129_orig.wav
โ”‚   โ””โ”€โ”€ ...
โ”œโ”€โ”€ transcripts/                  # Output, grouped by date
โ”‚   โ””โ”€โ”€ 2026-03-08/
โ”‚       โ”œโ”€โ”€ 000_TX01_MIC009_20260308_094129_orig.txt
โ”‚       โ””โ”€โ”€ ...
โ””โ”€โ”€ notes/                        # Compiled daily notes (separate step)
    โ””โ”€โ”€ 2026-03-08.md

Behavior:

  • Groups WAV files by date extracted from filename (YYYYMMDD)
  • Sorts by timestamp within each date for correct chronological order
  • Skips already-transcribed files unless --force-dates
  • Indexed output filenames (000_, 001_, ...) for sort order
  • Discord milestones every 25% progress

    • Output Format

    [ๅฝ•้Ÿณๅผ€ๅง‹: 09:41:29]
[09:41:35] ๅˆฐไบ†๏ผŒๆˆ‘ไปฌไธ‹่ฝฆๅงใ€‚
[09:41:48] ๆญฆๅบทๅคงๆฅผ๏ผŒไบบๅฅฝๅคšๅ•Šใ€‚
[09:42:04] ไฝ ๅธฎๆˆ‘ๅœจ่ฟ™้‡Œๆ‹ไธ€ๅผ ใ€‚
...

    Performance (Apple M4, 10-core CPU)

    | Metric | Value | |--------|-------| | RTF | ~0.04 (25x realtime) | | CPU | ~1.2 cores (12%) | | RAM | ~1.5GB | | 30min WAV | ~73s transcription + ~4s VAD | | Accuracy | 92% keyword accuracy (vs Whisper-medium 23%, turbo 38%) | | Hallucinations | 0 (vs Whisper hundreds per session) | | Model size | 234MB (vs Whisper-large-v3-turbo 1.5GB) |

    vs Old Whisper Skill

    | | Whisper (old) | SenseVoice (new) | |---|---|---| | Model | mlx-whisper-medium | SenseVoice-Small (234MB) | | Accuracy | 23-38% | 92% | | Hallucinations | Hundreds per session | 0 | | Timestamp | Per-word (~2-4s) | VAD-anchored (~15s) | | Duplicate lines | ~23% | <0.2% | | Chinese support | Weak | Native (Mandarin-optimized) |

    Emoji Note

    SenseVoice appends emotion tags (๐Ÿ˜Š๐Ÿ˜”๐Ÿ˜ก๐Ÿ˜ฎ) to segments. These are model artifacts reflecting detected speech emotion, not literal emoji in the audio. Downstream consumers (note compilation) should ignore or strip them.

    ๐Ÿ’ก Examples

    Single File

    source ~/.openclaw/venvs/sensevoice/bin/activate
python3 -c "
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
from datetime import datetime, timedelta
import re
    wav = ''

    Parse start time from filename: TX01_MIC015_20260308_124130_orig.wav
    
m = re.search(r'(\d{8})_(\d{6})', wav)
start_dt = datetime.strptime(m.group(1)+m.group(2), '%Y%m%d%H%M%S') if m else None
    vad_model = AutoModel(model='fsmn-vad', disable_update=True)
model = AutoModel(model='iic/SenseVoiceSmall', vad_model='fsmn-vad',
                  vad_kwargs={'max_single_segment_time': 30000}, device='cpu')
    vad_segs = vad_model.generate(input=wav)[0].get('value', [])
res = model.generate(input=wav, cache={}, language='zh', use_itn=True,
                     batch_size_s=60, merge_vad=True)
    texts = [rich_transcription_postprocess(s).strip()
         for s in re.split(r'<\|zh\|>', res[0]['text']) if s.strip()]
texts = [s for s in texts if len(s) > 1]
    ratio = len(vad_segs) / len(texts) if texts else 1
for i, t in enumerate(texts):
    vi = min(int(i * ratio), len(vad_segs)-1)
    ts = (start_dt + timedelta(milliseconds=vad_segs[vi][0])).strftime('%H:%M:%S') if start_dt else f'{vad_segs[vi][0]//1000:.0f}s'
    print(f'[{ts}] {t}')
"

    Batch Mode (daylog)

    The bundled scripts/batch_transcribe.py handles the full daylog pipeline:

    source ~/.openclaw/venvs/sensevoice/bin/activate
cd ~/Documents/dec/daylog
    Dry run โ€” see what would be transcribed
    
python3 scripts/batch_transcribe.py --dry-run
    Transcribe all new files
    
python3 scripts/batch_transcribe.py
    Re-transcribe specific dates (deletes existing, then re-runs)
    
python3 scripts/batch_transcribe.py --force-dates 2026-03-07,2026-03-08
    With progress file + Discord webhook
    
python3 scripts/batch_transcribe.py \
  --progress-file /tmp/daylog-progress.json \
  --discord-webhook https://discord.com/api/webhooks/...

    Flags: | Flag | Description | |------|-------------| | --dry-run | Preview without writing | | --engine sensevoice\|whisper | Engine (default: sensevoice) | | --force-dates YYYY-MM-DD,... | Delete & re-transcribe these dates | | --progress-file PATH | Write JSON progress for monitoring | | --discord-webhook URL | Post start/milestone/finish to Discord |

    Directory layout: 

    daylog/
โ”œโ”€โ”€ raw/                          # WAV input (DJI MIC 3, 48kHz/32bit, ~247MB/30min)
โ”‚   โ”œโ”€โ”€ TX01_MIC009_20260308_094129_orig.wav
โ”‚   โ””โ”€โ”€ ...
โ”œโ”€โ”€ transcripts/                  # Output, grouped by date
โ”‚   โ””โ”€โ”€ 2026-03-08/
โ”‚       โ”œโ”€โ”€ 000_TX01_MIC009_20260308_094129_orig.txt
โ”‚       โ””โ”€โ”€ ...
โ””โ”€โ”€ notes/                        # Compiled daily notes (separate step)
    โ””โ”€โ”€ 2026-03-08.md

    Behavior:

  • Groups WAV files by date extracted from filename (YYYYMMDD)
  • Sorts by timestamp within each date for correct chronological order
  • Skips already-transcribed files unless --force-dates
  • Indexed output filenames (000_, 001_, ...) for sort order
  • Discord milestones every 25% progress