Voice Recognition

Intelligent speech-to-text using local OpenAI Whisper (no API key needed, fully private). Use when you need to transcribe audio files, convert voice messages to text, recognize spoken content, or process speech input in any of 99+ languages. Key differentiator: smart auto-model selection analyzes audio length and complexity to choose the optimal Whisper model — short clean clips use the fast base model, long or mixed-language clips automatically upgrade to small/medium for accuracy.

🎤 Voice Recognition — Smart Auto-Model Selection

Transcribe audio to text using local OpenAI Whisper. No API keys, no internet required, 100% private.

Smart auto-selection dynamically picks the best model based on your audio characteristics — you never have to think about which model to use.

Quick Start

# Auto mode — analyzes audio, picks best model automatically
scripts/transcribe.py voice.ogg

# Force a specific model
scripts/transcribe.py voice.ogg --model small

# Specify language (auto-detect if omitted)
scripts/transcribe.py voice.ogg --language zh   # Chinese (Mandarin)
scripts/transcribe.py voice.ogg --language en   # English
scripts/transcribe.py voice.ogg --language yue  # Cantonese

# Show segment timestamps
scripts/transcribe.py voice.ogg --segments

# Save transcript to file
scripts/transcribe.py voice.ogg -o transcript.txt

Smart Auto-Selection

The script analyzes audio duration + complexity and selects the optimal model automatically:

Audio Characteristic	Model Used	Why
Short (<10s), clean speech	base	Fast (2-3s). Accurate enough for simple content.
Short (<10s), mixed languages	small	Better multilingual handling for code-switching.
Medium (10-60s), clean	base	Balanced speed and accuracy.
Medium (10-60s), mixed	small	Handles accents and language transitions.
Long (1-2min)	small	Maintains context, still fast enough.
Very long (2min+)	medium	Maximum accuracy for extended recordings.

You don't need to think about models. Just send audio.

Installation

Prerequisites

• Python 3.10+
• pip (Python package manager)

Via bundled installer

python3 scripts/install.py

Manual

pip install openai-whisper soundfile numpy
pip install torch --index-url https://download.pytorch.org/whl/cpu

Using requirements.txt

pip install -r requirements.txt
pip install torch --index-url https://download.pytorch.org/whl/cpu

Note: First run downloads the Whisper model (~139MB for base, ~461MB for small). Subsequent runs use the cached model (~/.cache/whisper/) and load instantly.

Model Reference

Model	Size	Speed	Accuracy	Best For
tiny	72MB	⚡⚡⚡	⭐⭐	Real-time preview, very short clips
base	139MB	⚡⚡	⭐⭐⭐	General use (auto-select default for short audio)
small	461MB	⚡	⭐⭐⭐⭐	Mixed languages, accents (auto-select for long/complex)
medium	1.5GB	🐢	⭐⭐⭐⭐⭐	Maximum accuracy, long recordings
large	2.9GB	🐢	⭐⭐⭐⭐⭐	Research-grade transcription

Language Support

Whisper supports 99 languages including:

• 🇨🇳 Chinese (Mandarin, Cantonese)
• 🇺🇸 English
• 🇪🇸 Spanish
• 🇯🇵 Japanese
• 🇰🇷 Korean
• 🇫🇷 French
• 🇩🇪 German

Auto-detects language by default. Use --language to provide a hint for better accuracy.

Features

Feature	Description
🔒 100% Private	Everything runs locally. No data leaves your machine.
🆓 No API Costs	Free unlimited transcription. No quotas, no keys.
🌐 99 Languages	Supports virtually all major world languages.
🧠 Smart Auto-Model	Analyzes audio → picks optimal model automatically.
⚡ Fast by Default	Short clips → base model (2-3s). Long clips → small/medium.
🎯 Accurate When Needed	Complex/mixed audio automatically upgrades the model.
📊 Segment Timestamps	Sentence-level timing for long recordings.
📁 Multiple Formats	OGG, WAV, MP3, M4A, FLAC, OPUS and more.

Supported Audio Formats

Format	Extension	Notes
OGG Opus	.ogg	Common voice message format ✅
WAV	.wav	Uncompressed, high quality
MP3	.mp3	Compressed audio
M4A	.m4a	Apple/MPEG-4 audio
FLAC	.flac	Lossless compressed
OPUS	.opus	Pure Opus stream

Usage Examples

Quick transcription (auto model)

$ scripts/transcribe.py meeting.ogg
📂 Loading audio: meeting.ogg
⏱  Duration: 32.0s | Sample rate: 16000Hz
🧠 Auto-selected model: BASE
✓ Model loaded (1.0s)
🎯 Transcribing...
✅ Done (4.1s total)
Meeting notes: Today we discuss three topics. First, project progress...

Transcription in context

# Chinese
scripts/transcribe.py voice.ogg --language zh

# English lecture with timestamps
scripts/transcribe.py lecture.m4a --language en --segments

# Mixed Chinese-English interview (auto complexity detection)
scripts/transcribe.py interview.ogg

# Save to file
scripts/transcribe.py podcast.mp3 -o transcript.txt

# Force high accuracy
scripts/transcribe.py important.wav --model medium

Output with segments

$ scripts/transcribe.py message.ogg --segments
📂 Loading audio: message.ogg
⏱  Duration: 7.5s | Sample rate: 16000Hz
🧠 Auto-selected model: BASE
✓ Model loaded (1.0s)
🎯 Transcribing...
✅ Done (2.4s total)
Now I'm sending this voice message to XiaoA, can you recognize what I said?

📝 Segments:
   [0.0s - 3.6s] Now I'm sending this voice message
   [3.6s - 7.4s] to XiaoA, can you recognize what I said?

Troubleshooting

Problem	Solution
No module error	Use the venv Python: python3 scripts/transcribe.py or run scripts/install.py
Slow transcription	First download caches the model (~139-461MB). Normal for first run.
Wrong language detected	Pass --language en or --language zh for a hint
Background noise	Use --model small or --model medium for noisy environments

Token Savings Examples

Scenario	Cloud API Cost	This Skill	Savings
10 short voice messages/day	~$0.60/day (Whisper API)	$0	∞
1 hour meeting transcription	~$2.88 (Deepgram)	$0	∞
1000 files for a project	~$50-200	$0	∞
Agent processing voice inputs	LLM tokens + API fees	0 tokens	Full token budget saved

Privacy & Security

• 100% offline — no data leaves your machine.
• No API keys — no third-party services, no accounts.
• No telemetry — zero tracking.
• No cloud — everything runs locally.
• Zero token consumption — frees your LLM budget for reasoning.

Your audio is yours. Always.