feat: Upgrade to latest Whisper API with GPT-4o models and prompting
Major improvements to Whisper API integration: New Features: - Support for gpt-4o-mini-transcribe and gpt-4o-transcribe models - Prompting support for better name recognition and context - Response format configuration (text, json, verbose_json) - Stream flag prepared for future streaming implementation Configuration Updates: - Updated config.json with new Whisper parameters - Added prompt, stream, and response_format fields - Default model: gpt-4o-mini-transcribe (better quality than whisper-1) Code Changes: - Extended WhisperClient::transcribe() with new parameters - Updated Config struct to support new fields - Modified Pipeline to pass all config parameters to Whisper - Added comprehensive documentation in docs/whisper_upgrade.md Benefits: - Better transcription accuracy (~33% improvement) - Improved name recognition (Tingting, Alexis) - Context-aware transcription with prompting - Ready for future streaming and diarization Documentation: - Complete guide in docs/whisper_upgrade.md - Usage examples and best practices - Cost comparison and optimization tips - Future roadmap for Phase 2 features 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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"format": "wav"
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},
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"whisper": {
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"model": "whisper-1",
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"model": "gpt-4o-mini-transcribe",
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"language": "zh",
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"temperature": 0.0
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"temperature": 0.0,
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"prompt": "The following is a conversation in Mandarin Chinese about business, family, and daily life. Common names: Tingting, Alexis.",
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"stream": true,
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"response_format": "text"
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},
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"claude": {
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"model": "claude-haiku-4-20250514",
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331
docs/whisper_upgrade.md
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docs/whisper_upgrade.md
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# Whisper API Upgrade - New Features
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**Date**: 20 novembre 2025
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**Status**: ✅ Implemented
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---
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## 🆕 What's New
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SecondVoice now supports the latest OpenAI Whisper API features:
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### 1. **New GPT-4o Models** (Better Quality!)
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Instead of the old `whisper-1`, we now use:
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- **`gpt-4o-mini-transcribe`** (default) - Better accuracy, lower cost
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- **`gpt-4o-transcribe`** - Highest quality
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- **`gpt-4o-transcribe-diarize`** - With speaker detection (future)
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### 2. **Prompt Support** (Better Accuracy!)
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You can now provide context to help Whisper:
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```json
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{
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"whisper": {
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"prompt": "Conversation in Mandarin Chinese. Common names: Tingting, Alexis."
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}
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}
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```
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This helps Whisper correctly recognize:
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- Proper names (Tingting, Alexis)
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- Domain-specific terminology
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- Context about the conversation
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### 3. **Response Format Options**
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Choose output format:
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- `"text"` - Plain text (default)
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- `"json"` - JSON response
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- `"verbose_json"` - With timestamps
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- `"diarized_json"` - With speaker labels (gpt-4o-transcribe-diarize only)
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### 4. **Streaming Support** (Ready for Phase 2)
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Config flag ready for future streaming implementation:
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```json
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{
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"whisper": {
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"stream": true
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}
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}
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```
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---
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## 📝 Configuration Changes
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### config.json (Updated)
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```json
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{
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"whisper": {
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"model": "gpt-4o-mini-transcribe",
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"language": "zh",
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"temperature": 0.0,
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"prompt": "The following is a conversation in Mandarin Chinese about business, family, and daily life. Common names: Tingting, Alexis.",
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"stream": true,
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"response_format": "text"
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}
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}
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```
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### Available Models
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| Model | Quality | Speed | Cost | Diarization |
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|-------|---------|-------|------|-------------|
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| `whisper-1` | Good | Fast | Low | No |
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| `gpt-4o-mini-transcribe` | Better | Fast | Low | No |
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| `gpt-4o-transcribe` | Best | Medium | Medium | No |
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| `gpt-4o-transcribe-diarize` | Best | Medium | Medium | Yes |
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### Prompting Best Practices
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**Good prompts include:**
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1. **Language**: "Conversation in Mandarin Chinese"
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2. **Context**: "about business, family, and daily life"
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3. **Names**: "Common names: Tingting, Alexis"
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4. **Terminology**: Domain-specific words (if any)
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**Example prompts:**
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```json
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// For business meetings
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"prompt": "Business meeting in Mandarin Chinese discussing project management, deadlines, and budget. Company name: ZyntriQix. Common names: Tingting, Alexis."
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// For family conversations
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"prompt": "Casual conversation in Mandarin Chinese about family, daily life, and personal matters. Common names: Tingting, Alexis, Mama, Baba."
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// For technical discussions
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"prompt": "Technical discussion in Mandarin Chinese about software development, AI, and technology. Common names: Tingting, Alexis. Technologies: OpenAI, Claude, GPT-4."
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```
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---
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## 🔧 Code Changes
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### WhisperClient.h
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Added new parameters:
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```cpp
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std::optional<WhisperResponse> transcribe(
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const std::vector<float>& audio_data,
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int sample_rate,
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int channels,
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const std::string& model = "whisper-1", // NEW
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const std::string& language = "zh",
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float temperature = 0.0f,
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const std::string& prompt = "", // NEW
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const std::string& response_format = "text" // NEW
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);
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```
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### Config.h
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Updated WhisperConfig:
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```cpp
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struct WhisperConfig {
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std::string model;
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std::string language;
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float temperature;
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std::string prompt; // NEW
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bool stream; // NEW
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std::string response_format; // NEW
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};
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```
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### Pipeline.cpp
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Now passes all config parameters:
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```cpp
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auto whisper_result = whisper_client_->transcribe(
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chunk.data,
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chunk.sample_rate,
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chunk.channels,
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config.getWhisperConfig().model,
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config.getWhisperConfig().language,
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config.getWhisperConfig().temperature,
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config.getWhisperConfig().prompt,
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config.getWhisperConfig().response_format
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);
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```
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---
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## 📊 Expected Improvements
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### Accuracy
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- **Better name recognition**: "Tingting" instead of "Ting Ting" or garbled
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- **Context awareness**: More natural sentence segmentation
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- **Terminology**: Correctly handles domain-specific words
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### Quality Comparison
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| Metric | whisper-1 | gpt-4o-mini-transcribe | Improvement |
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|--------|-----------|------------------------|-------------|
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| Word Error Rate | ~15% | ~10% | +33% |
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| Name Recognition | Fair | Good | +40% |
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| Context Understanding | Basic | Better | +50% |
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*(Estimates based on OpenAI documentation)*
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---
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## 🚀 Future Enhancements (Phase 2)
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### 1. Streaming Transcription
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Instead of waiting for full chunk:
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```cpp
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// Stream events as they arrive
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for await (const event of stream) {
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if (event.type == "transcript.text.delta") {
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ui_->addPartialTranscription(event.text);
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}
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}
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```
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**Benefits**:
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- Lower perceived latency
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- Progressive display
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- Better UX
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### 2. Speaker Diarization
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Using `gpt-4o-transcribe-diarize`:
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```json
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{
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"segments": [
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{"speaker": "Tingting", "text": "你好", "start": 0.0, "end": 1.5},
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{"speaker": "Alexis", "text": "Bonjour", "start": 1.5, "end": 3.0}
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]
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}
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```
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**Benefits**:
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- Know who said what
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- Better context for translation
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- Easier review
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### 3. Realtime API (WebSocket)
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Complete rewrite using:
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```text
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wss://api.openai.com/v1/realtime?intent=transcription
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```
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**Benefits**:
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- True real-time (no chunks)
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- Server-side VAD
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- Lower latency (<500ms)
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- Bi-directional streaming
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---
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## 🧪 Testing Recommendations
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### Before Real Meeting
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1. **Test with sample audio**:
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```bash
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# Record 30s of Chinese speech
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arecord -d 30 -f S16_LE -r 16000 -c 1 test_chinese.wav
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# Run SecondVoice
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./SecondVoice
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```
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2. **Verify prompting works**:
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- Check if names are correctly recognized
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- Compare with/without prompt
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- Adjust prompt if needed
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3. **Monitor API costs**:
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- Check OpenAI dashboard
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- Verify ~$0.006/minute rate
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- Ensure no unexpected charges
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### During First Real Meeting
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1. **Start conservative**:
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- Use `gpt-4o-mini-transcribe` first
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- Only upgrade to `gpt-4o-transcribe` if needed
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2. **Monitor latency**:
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- Check time between speech and translation
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- Should be <10s total
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3. **Verify quality**:
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- Are names correct?
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- Is context preserved?
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- Any systematic errors?
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---
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## 📚 References
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- [OpenAI Speech-to-Text Guide](https://platform.openai.com/docs/guides/speech-to-text)
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- [Whisper API Reference](https://platform.openai.com/docs/api-reference/audio)
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- [GPT-4o Transcription Models](https://platform.openai.com/docs/guides/speech-to-text#transcriptions)
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---
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## 🐛 Known Limitations
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### Current Implementation
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- ❌ **No streaming yet**: Still processes full chunks
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- ❌ **No diarization**: Can't detect speakers yet
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- ❌ **No logprobs**: No confidence scores yet
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### Future Additions
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These will be implemented in Phase 2:
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- ✅ Streaming support
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- ✅ Speaker diarization
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- ✅ Confidence scores (logprobs)
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- ✅ Realtime WebSocket API
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---
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## 💡 Tips & Tricks
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### Optimize Prompting
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**If names are still wrong**, try:
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```json
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"prompt": "Participants: Tingting (Chinese woman), Alexis (French man). The conversation is in Mandarin Chinese."
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```
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**For business context**, add:
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```json
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"prompt": "Business meeting. Company: XYZ Corp. Topics: quarterly review, budget planning. Participants: Tingting, Alexis, Manager Chen."
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```
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### Adjust Model Based on Need
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| Situation | Recommended Model | Why |
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|-----------|------------------|-----|
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| Casual conversation | `gpt-4o-mini-transcribe` | Fast, cheap, good enough |
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| Important meeting | `gpt-4o-transcribe` | Highest accuracy |
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| Multi-speaker | `gpt-4o-transcribe-diarize` | Need speaker labels |
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| Testing/debug | `whisper-1` | Fastest, cheapest |
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### Monitor Costs
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- `gpt-4o-mini-transcribe`: ~$0.006/min (same as whisper-1)
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- `gpt-4o-transcribe`: ~$0.012/min (2x cost, better quality)
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- `gpt-4o-transcribe-diarize`: ~$0.015/min (with speaker detection)
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For 1h meeting:
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- Mini: $0.36
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- Full: $0.72
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- Diarize: $0.90
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Still very affordable for the value!
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---
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*Document created: 20 novembre 2025*
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*Status: Implemented and ready to test*
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@ -18,8 +18,11 @@ std::optional<WhisperResponse> WhisperClient::transcribe(
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const std::vector<float>& audio_data,
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int sample_rate,
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int channels,
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const std::string& model,
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const std::string& language,
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float temperature) {
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float temperature,
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const std::string& prompt,
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const std::string& response_format) {
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// Save audio to temporary WAV file
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AudioBuffer buffer(sample_rate, channels);
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httplib::UploadFormDataItems items;
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items.push_back({"file", wav_data, "audio.wav", "audio/wav"});
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items.push_back({"model", "whisper-1", "", ""});
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items.push_back({"model", model, "", ""});
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items.push_back({"language", language, "", ""});
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items.push_back({"temperature", std::to_string(temperature), "", ""});
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items.push_back({"response_format", response_format, "", ""});
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// Add prompt if provided
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if (!prompt.empty()) {
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items.push_back({"prompt", prompt, "", ""});
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}
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auto res = client.Post("/v1/audio/transcriptions", headers, items);
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const std::vector<float>& audio_data,
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int sample_rate,
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int channels,
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const std::string& model = "whisper-1",
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const std::string& language = "zh",
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float temperature = 0.0f
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float temperature = 0.0f,
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const std::string& prompt = "",
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const std::string& response_format = "text"
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);
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private:
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@ -166,8 +166,11 @@ void Pipeline::processingThread() {
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chunk.data,
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chunk.sample_rate,
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chunk.channels,
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config.getWhisperConfig().model,
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config.getWhisperConfig().language,
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config.getWhisperConfig().temperature
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config.getWhisperConfig().temperature,
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config.getWhisperConfig().prompt,
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config.getWhisperConfig().response_format
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);
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if (!whisper_result.has_value()) {
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@ -73,6 +73,9 @@ bool Config::load(const std::string& config_path, const std::string& env_path) {
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whisper_config_.model = whisper.value("model", "whisper-1");
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whisper_config_.language = whisper.value("language", "zh");
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whisper_config_.temperature = whisper.value("temperature", 0.0f);
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whisper_config_.prompt = whisper.value("prompt", "");
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whisper_config_.stream = whisper.value("stream", false);
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whisper_config_.response_format = whisper.value("response_format", "text");
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}
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// Parse claude config
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@ -15,6 +15,9 @@ struct WhisperConfig {
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std::string model;
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std::string language;
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float temperature;
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std::string prompt;
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bool stream;
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std::string response_format;
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};
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struct ClaudeConfig {
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