NEXUS Voice Transcriber

Setup#

On first use, read references/whisper-models.md and references/troubleshooting.md.

Ensure dependencies: ffmpeg, python3, and required Python packages ( openai-whisper, deepgram-sdk optional).

When to Use#

• User sends a voice note / audio file / video file that needs transcription.
• Need to archive both the original audio and the text transcript.
• Want speaker detection (if using Deepgram with diarization).
• Quick local transcription without external APIs (Whisper).

Architecture#

Memory lives in ~/voice-transcriber/. See below for structure.

text

~/voice-transcriber/
├── memory.md          # Provider preferences, defaults, history
├── transcripts/       # Saved transcripts (txt, json, srt)
├── audio/             # Saved original audio files
└── temp/              # Processing workspace (auto-cleaned)

Quick Reference#

TopicFileWhisper model guidereferences/whisper-models.mdTroubleshootingreferences/troubleshooting.mdMain scriptscripts/transcribe.py

Core Rules#

1. Detect Input Type#

Before transcription:

• Local file path → verify exists, check format (mp3, wav, m4a, mp4, etc.)
• URL → download to temp/, then process
• Voice memo → usually single speaker, short
• Meeting / interview → likely multiple speakers, consider diarization

2. Choose Provider Based on Context#

ScenarioBest ProviderWhyPrivacy, no API keysLocal WhisperRuns on-device, freeHigh accuracy, speedDeepgram Nova‑3Low latency, good accuracySpeaker identificationDeepgram (with diarization)Native speaker labelsNo internetLocal WhisperOffline capable

3. Handle Long Audio#

Files >25 MB or >2 hours:

1. Split into chunks with ffmpeg (see scripts/transcribe.py --split)
2. Process each chunk
3. Merge transcripts with proper timestamps

4. Save Artifacts#

After successful transcription:

• Save transcript to ~/voice-transcriber/transcripts/ with a meaningful name
• Save original audio to ~/voice-transcriber/audio/ if user wants archival
• Update memory.md with date, file, provider, duration

5. Output Formats#

Default to plain text ( .txt). Offer alternatives:

• .txt — clean text, no timestamps
• .srt / .vtt — subtitles with timing
• .json — structured with word‑level timing (Deepgram) or segment timing (Whisper)

Common Traps#

• Assuming one provider fits all → Whisper lacks diarization; Deepgram needs API key.
• Uploading huge files directly → Timeouts. Split first.
• Ignoring audio quality → Noisy audio may need preprocessing ( ffmpeg noise reduction).
• Not checking language → Whisper auto‑detects but can fail on mixed‑language content.
• Forgetting to save audio → User may want the original file archived.

Requirements#

Required:

• ffmpeg (audio conversion, splitting)
• python3 + pip
• Python packages: openai-whisper (local), requests (for Deepgram if used)

Optional API keys (only if using Deepgram):

• DEEPGRAM_API_KEY — for Deepgram Nova‑3 (speaker diarization available)

Local Whisper works without any API keys.

Provider Quick Reference#

Local Whisper (No API Key)#

bash

# Install
pip install openai-whisper

# Basic transcription (via script)
python3 scripts/transcribe.py --file audio.wav --provider whisper --model base

# Output formats: txt (default), srt, vtt, json
python3 scripts/transcribe.py --file audio.wav --provider whisper --model medium --format srt

Models: tiny (fastest) → base → small → medium → large (most accurate).

Deepgram Nova‑3 (API Key Required)#

bash

# Set environment variable
export DEEPGRAM_API_KEY="your_key_here"

# Transcribe with speaker diarization
python3 scripts/transcribe.py --file audio.wav --provider deepgram --diarize

# Output JSON with speaker labels
python3 scripts/transcribe.py --file audio.wav --provider deepgram --format json

Audio Preprocessing#

Extract Audio from Video#

bash

ffmpeg -i video.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 audio.wav

Reduce Noise#

bash

ffmpeg -i noisy.wav -af "afftdn=nf=-25" clean.wav

Split Long Audio (10‑minute chunks)#

bash

ffmpeg -i long.mp3 -f segment -segment_time 600 -c copy temp/chunk_%03d.mp3

Security & Privacy#

Data that stays local:

• Transcripts in ~/voice-transcriber/transcripts/
• Original audio in ~/voice-transcriber/audio/
• Local Whisper processes entirely on‑device

Data that leaves your machine (if using Deepgram):

• Audio file sent to Deepgram API ( api.deepgram.com)
• Transcript returned and stored locally

This skill does NOT:

• Store API keys in plain text (use environment variables)
• Auto‑upload without confirmation
• Retain files on external servers after processing

External Endpoints#

EndpointData SentPurposeapi.deepgram.com/v1/listenAudio fileDeepgram transcription

Only called when user explicitly chooses Deepgram provider. Local Whisper sends nothing.

Memory Template#

Create ~/voice-transcriber/memory.md with this structure:

markdown

# Voice Transcriber Memory

## Status
status: ongoing
version: 1.0.0
last: YYYY‑MM‑DD
integration: pending

## Context
<!-- Observations about transcription needs, preferred providers, languages, etc. -->

## Notes
<!-- Provider preferences, format preferences, diarization needs -->

---
*Updated: YYYY‑MM‑DD*

Related Skills#

Install with clawhub install <slug> if user confirms:

• speech-to-text-transcription — broader audio/video transcription with more providers
• ffmpeg — advanced audio/video processing
• audio — general audio manipulation

Feedback#

• If useful: clawhub star voice-transcriber
• Stay updated: clawhub sync