assemblyai-transcription

name	AssemblyAI Transcription
description	Use when transcribing audio files with speaker diarization. Triggers on TRANSCRIBE keyword.
pattern	\b(TRANSCRIBE)\b[.,;:!?]?

AssemblyAI Audio Transcription with Speaker Diarization

Default Behavior

When the user says "TRANSCRIBE" without specifying a file, automatically find the latest audio file in ~/Downloads/:

/bin/ls -lt ~/Downloads/ | grep -iE '\.(m4a|mp3|mp4|wav|flac|ogg|webm|mov|avi|mkv)$' | head -1

Then transcribe that file. Always confirm which file you found before proceeding.

Environment

• Python venv: /Users/wz/Desktop/.venv (assemblyai is installed here)
• API key: Set via ASSEMBLYAI_API_KEY environment variable (see ~/.zshrc or ~/.zprofile)

Required Configuration (CRITICAL)

The API requires speech_models parameter. Without it, transcription will fail with:

"speech_models" must be a non-empty list containing one or more of: "universal-3-pro", "universal-2"

Always use this config:

config=aai.TranscriptionConfig(
    speaker_labels=True,
    speech_models=['universal-3-pro', 'universal-2'],
    language_detection=True
)

Workflow: Transcribe and Save

Always pipe output directly to file to avoid large terminal output.

Step 1: Transcribe to temp file

First transcribe to a temp file next to the audio (using the original audio filename):

cd /Users/wz/Desktop && source .venv/bin/activate && python3 -c "
import assemblyai as aai
import os
aai.settings.api_key = os.environ['ASSEMBLYAI_API_KEY']

transcript = aai.Transcriber().transcribe(
    '/path/to/audio.m4a',
    config=aai.TranscriptionConfig(
        speaker_labels=True,
        speech_models=['universal-3-pro', 'universal-2'],
        language_detection=True
    )
)

if transcript.status == aai.TranscriptStatus.error:
    print(f'ERROR: {transcript.error}')
else:
    for u in transcript.utterances:
        print(f'Speaker {u.speaker}: {u.text}')
        print()
" > '/path/to/AudioFileName - transcript.md' 2>&1

Important: Use 2>&1 to capture errors to the file too, and check the file for errors after.

Timeout: Set bash timeout to 300000ms (5 min) since transcription can take a while for long audio.

Step 2: Content-based rename

After transcription, read the transcript and rename the file based on its content:

1. Read the transcript to understand what it's about
2. Generate a descriptive filename: YYYY-MM-DD - <Topic Summary>.md
   • Use today's date (or recording date if known from filename)
   • Topic summary should be 3-6 words, Title Case, describing the main subject
   • Examples:
     • 2026-02-05 - Product Permissions Architecture Discussion.md
     • 2026-01-28 - Client Onboarding Call.md
     • 2026-02-03 - Weekly Team Standup.md
3. Rename the temp transcript file to the content-based name (in same directory)

Step 3: Archive to ~/.transcripts/

Always copy the final transcript to ~/.transcripts/ with intelligent grouping by subdirectory:

Subdirectory	When to use
work/poly/	Poly/Baoyuan property management business calls
work/meetings/	General work meetings, standups
work/interviews/	Job interviews, candidate screens
personal/	Personal calls, conversations
academic/	Lectures, office hours, study groups
misc/	Anything that doesn't fit above

mkdir -p ~/.transcripts/<subdirectory>
cp '/path/to/YYYY-MM-DD - Topic Summary.md' ~/.transcripts/<subdirectory>/

Use your best judgment to categorize. When unsure, use misc/.

Step 4: Contextual copy (if applicable)

If there's an obvious project-specific location where the transcript belongs, also copy it there. Use judgment:

• If discussing a specific codebase project and you're in that repo → ./claude_files/ or a relevant docs folder
• If it's a client/contact call → check if a contacts/ directory exists for that client
• If no obvious project context → skip this step (the ~/.transcripts/ archive is sufficient)

Pricing

Feature	Cost
Core transcription	$0.37/hour ($0.00617/min)
Speaker diarization	+$0.36/hour ($0.006/min)
Total with diarization	$0.73/hour (~$0.012/min)

Supported Formats

Audio: mp3, mp4, wav, flac, ogg, webm, m4a Video: mp4, mov, avi, mkv (extracts audio) Max file size: 5GB

Common Options

config = aai.TranscriptionConfig(
    speaker_labels=True,                    # Enable diarization (always use)
    speech_models=['universal-3-pro', 'universal-2'],  # REQUIRED
    language_detection=True,                # Auto-detect language
    speakers_expected=2,                    # Hint for expected speakers (optional)
    punctuate=True,                         # Add punctuation
    format_text=True,                       # Format numbers, dates, etc.
    word_boost=["specific", "terms"],       # Boost recognition of specific words
)

Speaker Identification

After transcription, identify speakers by name if obvious from context:

• If the user provides context about who the speakers are, label them accordingly (e.g., "Warren:", "Jenny:")
• If identity is obvious from the conversation content (e.g., someone says their name, references their role, or the context makes it clear), label them
• If identity is not obvious, leave as generic "Speaker A:", "Speaker B:" etc.—do not guess. Only ask the user if they volunteer the info or if it's needed for the task

When renaming speakers, do a find-and-replace across the entire transcript.

Post-Transcription Summary

After all copies are done, provide a brief summary:

• Speakers: Number detected, with identified names if known
• Language: Detected language
• Topics: Key subjects discussed
• Action items: Any commitments or next steps mentioned
• Filed to: List all locations the transcript was saved/copied to