🎤 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:

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

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

Supported Audio Formats

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

Token Savings Examples

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.