Transcribing Meeting Recordings

Transcribing Meeting Recordings (with Speaker Identification)

Overview

End-to-end pipeline: video/audio file → cleaned SRT transcript with timestamps and real participant names. Uses whisperx (faster-whisper + alignment + pyannote diarization) for ASR + speaker clustering, plus a Microsoft Teams / Zoom video-frame trick to map anonymized SPEAKER_XX clusters onto real names.

When to Use

• User provides a meeting recording (.mp4 / .mov / .mkv / .wav / .m4a / .mp3) and wants a transcript with timestamps.
• User asks for SRT/VTT subtitles, captions, 字幕, or "transcript with speaker labels".
• Multilingual content (especially Chinese with English technical terms mixed in — whisper large-v3 handles this well with the right prompt).
• User wants to know who said what in a meeting, not just the words.

Do NOT use for:

• Live / real-time / streaming transcription — whisperx is batch-only.
• Summarization without producing a transcript artifact.
• Audio-only files where speaker identification doesn't matter — then skip diarization and the Teams-frame step.

Quick Reference

Prerequisites (one-time setup)

# 1. ffmpeg installed system-wide
which ffmpeg || sudo apt install ffmpeg

# 2. whisperx — REQUIRES Python 3.11
#    (Python 3.14 ships a torchaudio that removed list_audio_backends → pyannote crashes)
uv tool install whisperx --python 3.11

# 3. Accept ALL THREE pyannote gated repos on huggingface.co (just click "Agree"):
#      https://huggingface.co/pyannote/speaker-diarization-3.1
#      https://huggingface.co/pyannote/segmentation-3.0
#      https://huggingface.co/pyannote/speaker-diarization-community-1
#    Then ensure HF_TOKEN is in ~/.cache/huggingface/token:
huggingface-cli login   # only needed once

Implementation

Step 1 — Extract 16 kHz mono WAV

Whisper is trained on 16 kHz mono; matching that format avoids resampling cost.

ffmpeg -y -i meeting.mp4 -vn -ac 1 -ar 16000 -c:a pcm_s16le audio.wav

Step 2 — Run whisperx (transcribe + align + diarize)

Tune for your GPU. For 8 GB VRAM (e.g., RTX 4060) use --compute_type int8 --batch_size 4 — at the default batch_size 8 large-v3 + diarization OOMs around 70 % progress:

whisperx audio.wav \
  --model large-v3 \
  --language zh \
  --device cuda \
  --compute_type int8 \
  --batch_size 4 \
  --diarize \
  --diarize_model pyannote/speaker-diarization-3.1 \
  --min_speakers 2 --max_speakers 8 \
  --output_format srt --output_format json \
  --output_dir . \
  --condition_on_previous_text False \
  --initial_prompt "domain terminology hint, e.g. 物流系统, AB Declaration, shipment, MAWB"

This produces:

• audio.json — word-level timestamps + speaker field on every segment.
• audio.srt — basic SRT (may be missing if writing fails; we regenerate it in step 3 anyway).

The --initial_prompt gives whisper context for technical/domain words it would otherwise mis-transcribe. Always pass one for technical meetings.

If diarization fails with 403 Client Error: Forbidden, you haven't accepted all three pyannote licenses — see Prerequisites.

Step 3 — Clean + (optionally) map names

The bundled scripts/make_srt.py reads ./audio.json from the current directory and writes ./transcript.srt. It:

1. Splits long whisper segments at sentence-final punctuation and at speaker-change boundaries from pyannote.
2. Strips known Whisper YouTube-training hallucinations (请不吝点赞订阅..., 请勿模仿, etc.).
3. Inserts a space between CJK and ASCII characters (shipment加A → shipment 加 A) for readability.
4. Smooths diarization noise — cues shorter than 2.5 s + 10 chars sandwiched between two same-speaker neighbours get snapped to the surrounding speaker.
5. Merges consecutive same-speaker cues that are close in time and don't cross sentence boundaries.
6. Substitutes SPEAKER_XX with real names read from a sibling speakers.md table (see step 4).

cd <meeting-folder-with-audio.json>
cp <skill>/templates/speakers.template.md ./speakers.md  # first time only
python3 <skill>/scripts/make_srt.py
# → wrote transcript.srt (NN cues)
# → speaker durations printed to stdout

Step 4 — Speaker identification via Teams/Zoom video frames (novel technique)

Microsoft Teams and Zoom both show the active speaker's name in a bottom-left banner and highlight their gallery tile with a coloured ring / brighter border. This makes label → name mapping a simple visual check.

For every SPEAKER_XX produced by step 2:

1. From the SRT, pick a timestamp 3–5 seconds into a clean monologue (avoid speaker-change boundaries — the Teams banner has ~1-2 s lag).

2. Extract the frame:

   ffmpeg -ss <seconds> -i meeting.mp4 -frames:v 1 -q:v 2 spk_<label>_<mmss>.jpg
   

3. Open the JPG and identify which avatar is highlighted (the bottom-left name banner is the surest signal).

4. Fill in the Mapping table in speakers.md:

   | Label      | Name        | Role  | Notes                                |
   | ---------- | ----------- | ----- | ------------------------------------ |
   | SPEAKER_03 | Junwei Nie  | Dev   | confirmed at 07:25 — JN tile lit     |
   

5. Re-run python3 scripts/make_srt.py — the SRT now carries real names.

Critical tip — pyannote over-segmentation. It is common for one physical speaker to be split across 2+ SPEAKER_XX clusters (different mic proximity, varying intonation between explanations vs. questions, etc.). Verify by frame-checking each label; map all of them to the same canonical name in speakers.md and make_srt.py will merge their cues automatically.

Cross-meeting tracking. Keep a roster at the parent folder of all meeting recordings (use templates/roster.template.md). Each new meeting's per-meeting speakers.md gets summarised into one row per person on the parent roster, with their alias list (including Whisper homophones — e.g., 训维 for Junwei).

Common Mistakes

Bundled Files

transcribing-meeting-recordings/
├── SKILL.md                         ← this file
├── scripts/
│   └── make_srt.py                  ← cleaner + name-substituter (CWD-driven)
└── templates/
    ├── speakers.template.md         ← per-meeting label→name table
    └── roster.template.md           ← cross-meeting participant roster

Real-World Impact

A 34-minute Chinese requirements-walkthrough meeting (10 Teams participants) was processed end-to-end in:

• ~2 min — audio extraction + whisperx transcription + alignment + diarization on RTX 4060 8 GB (int8 / batch 4).
• ~5 min — visual identification of 4 actual speakers from 6 pyannote clusters via 10 Teams screenshots.
• Output: 202-cue SRT carrying real names (Zhujun Hu, Junwei Nie, Denny Huang, Macklin Feng).

Every failure mode in the Common Mistakes table above was actually hit and resolved during that exercise — Python 3.14 incompatibility, two rounds of HF license acceptance, OOM at default batch size, YouTube hallucination, English-word concatenation, and pyannote splitting two of the four participants into two clusters each.