refactor: Improve VAD trailing silence detection and update docs
- Replace hang time logic with consecutive silence frame counter for more precise speech end detection - Update Whisper prompt to utilize previous context for better transcription coherence - Expand README with comprehensive feature list, architecture details, debugging status, and session logging structure - Add troubleshooting section for real-world testing conditions and known issues
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README.md
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README.md
@ -4,16 +4,50 @@ Real-time Chinese to French translation system for live meetings.
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## Overview
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SecondVoice captures audio, transcribes Chinese speech using OpenAI's Whisper API, and translates it to French using Claude AI in real-time. Perfect for understanding Chinese meetings on the fly.
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SecondVoice captures audio, transcribes Chinese speech using OpenAI's Whisper API (gpt-4o-mini-transcribe), and translates it to French using Claude AI in real-time. Designed for understanding Chinese meetings, calls, and conversations on the fly.
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### Why This Project?
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Built to solve a real need: understanding Chinese meetings in real-time without constant reliance on bilingual support. Perfect for:
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- Business meetings with Chinese speakers
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- Family/administrative calls
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- Professional conferences
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- Any live Chinese conversation where real-time comprehension is needed
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**Status**: MVP complete, actively being debugged and improved based on real-world usage.
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## Quick Start
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### Windows (MinGW) - Recommended
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```batch
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# First-time setup
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.\setup_mingw.bat
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# Build
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.\build_mingw.bat
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# Run
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cd build\mingw-Release
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SecondVoice.exe
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```
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**Requirements**: `.env` file with `OPENAI_API_KEY` and `ANTHROPIC_API_KEY`, plus a working microphone.
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See full setup instructions below for other platforms.
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## Features
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- 🎤 Real-time audio capture
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- 🗣️ Chinese speech-to-text (Whisper API)
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- 🌐 Chinese to French translation (Claude API)
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- 🖥️ Clean ImGui interface
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- 💾 Full recording saved to disk
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- ⚙️ Configurable chunk sizes and settings
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- 🎤 **Real-time audio capture** with Voice Activity Detection (VAD)
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- 🔇 **Noise reduction** using RNNoise neural network
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- 🗣️ **Chinese speech-to-text** via Whisper API (gpt-4o-mini-transcribe)
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- 🧠 **Hallucination filtering** - removes known Whisper artifacts
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- 🌐 **Chinese to French translation** via Claude AI (claude-haiku-4-20250514)
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- 🖥️ **Clean ImGui interface** with adjustable VAD thresholds
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- 💾 **Full session recording** with structured logging
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- 📊 **Session archival** - audio, transcripts, translations, and metadata
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- ⚡ **Opus compression** - 46x bandwidth reduction (16kHz PCM → 24kbps Opus)
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- ⚙️ **Configurable settings** via config.json
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## Requirements
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@ -116,20 +150,138 @@ The application will:
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## Architecture
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```
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Audio Capture (PortAudio)
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Audio Input (16kHz mono)
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↓
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Whisper API (Speech-to-Text)
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Voice Activity Detection (VAD) - RMS + Peak thresholds
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↓
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Claude API (Translation)
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Noise Reduction (RNNoise) - 16→48→16 kHz resampling
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↓
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ImGui UI (Display)
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Opus Encoding (24kbps OGG) - 46x compression
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↓
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Whisper API (gpt-4o-mini-transcribe) - Chinese STT
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↓
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Hallucination Filter - Remove known artifacts
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↓
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Claude API (claude-haiku-4) - Chinese → French translation
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↓
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ImGui UI Display + Session Logging
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```
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### Threading Model
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### Threading Model (3 threads)
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- **Thread 1**: Audio capture (PortAudio callback)
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- **Thread 2**: AI processing (Whisper + Claude API calls)
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- **Thread 3**: UI rendering (ImGui + OpenGL)
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1. **Audio Thread** (`Pipeline::audioThread`)
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- PortAudio callback captures 16kHz mono audio
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- Applies VAD (Voice Activity Detection) using RMS + Peak thresholds
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- Pushes speech chunks to processing queue
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2. **Processing Thread** (`Pipeline::processingThread`)
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- Consumes audio chunks from queue
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- Applies RNNoise denoising (upsampled to 48kHz → denoised → downsampled to 16kHz)
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- Encodes to Opus/OGG for bandwidth efficiency
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- Calls Whisper API for Chinese transcription
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- Filters known hallucinations (YouTube phrases, music markers, etc.)
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- Calls Claude API for French translation
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- Logs to session files
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3. **UI Thread** (main)
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- GLFW/ImGui rendering loop (must run on main thread)
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- Displays real-time transcription and translation
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- Allows runtime VAD threshold adjustment
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- Handles user controls (stop recording, etc.)
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### Core Components
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**Audio Processing**:
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- `AudioCapture.cpp` - PortAudio wrapper with VAD-based segmentation
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- `AudioBuffer.cpp` - Accumulates samples, exports WAV/Opus
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- `NoiseReducer.cpp` - RNNoise denoising with resampling
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**API Clients**:
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- `WhisperClient.cpp` - OpenAI Whisper API (multipart/form-data)
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- `ClaudeClient.cpp` - Anthropic Claude API (JSON)
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- `WinHttpClient.cpp` - Native Windows HTTP client (replaced libcurl)
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**Core Logic**:
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- `Pipeline.cpp` - Orchestrates audio → transcription → translation flow
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- `TranslationUI.cpp` - ImGui interface with VAD controls
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**Utilities**:
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- `Config.cpp` - Loads config.json + .env
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- `ThreadSafeQueue.h` - Lock-free queue for audio chunks
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## Known Issues & Active Debugging
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**Status**: Real-world testing has identified issues with degraded audio conditions (see `PLAN_DEBUG.md` for details).
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### Current Problems
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Based on transcript analysis from actual meetings (November 2025):
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1. **VAD cutting speech too early**
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- Voice Activity Detection triggers end-of-segment prematurely
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- Results in fragmented phrases ("我很。" → "Je suis.")
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- **Hypothesis**: Silence threshold too aggressive for multi-speaker scenarios
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2. **Segments too short for context**
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- Whisper receives insufficient audio context for accurate Chinese transcription
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- Single-word or two-word segments lack conversational context
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- **Impact**: Lower accuracy, especially with homonyms
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3. **Ambient noise interpreted as speech**
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- Background sounds trigger false VAD positives
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- Test transcript shows "太多声音了" (too much noise) being captured
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- **Mitigation**: RNNoise helps but not sufficient for very noisy environments
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4. **Loss of inter-segment context**
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- Each audio chunk processed independently
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- Whisper cannot use previous context for better transcription
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- **Potential solution**: Pass previous 2-3 transcriptions in prompt
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### Test Conditions
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Testing has been performed under **deliberately degraded conditions** to ensure robustness:
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- Multiple simultaneous speakers
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- Variable microphone distance
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- Variable volume levels
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- Fast-paced conversations
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- Low-quality microphone
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These conditions are intentionally harsh to validate real-world meeting scenarios.
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### Debug Plan
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See `PLAN_DEBUG.md` for:
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- Detailed session logging implementation (JSON per segment + metadata)
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- Improved Whisper prompt engineering
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- VAD threshold tuning recommendations
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- Context propagation strategies
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## Session Logging
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### Structure
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```
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sessions/
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└── YYYY-MM-DD_HHMMSS/
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├── session.json # Session metadata
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├── segments/
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│ ├── 001.json # Segment: Chinese + French + metadata
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│ ├── 002.json
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│ └── ...
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└── transcript.txt # Final export
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```
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### Segment Format
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```json
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{
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"id": 1,
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"chinese": "两个老鼠求我",
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"french": "Deux souris me supplient"
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}
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```
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**Future enhancements**: Audio duration, RMS levels, timestamps, Whisper/Claude latencies per segment.
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## Configuration
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@ -143,8 +295,9 @@ ImGui UI (Display)
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"chunk_duration_seconds": 10
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},
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"whisper": {
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"model": "whisper-1",
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"language": "zh"
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"model": "gpt-4o-mini-transcribe",
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"language": "zh",
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"prompt": "Transcription d'une réunion en chinois mandarin. Plusieurs interlocuteurs. Ne transcris PAS : musique, silence, bruits de fond. Si l'audio est inaudible, renvoie une chaîne vide. Noms possibles: Tingting, Alexis."
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},
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"claude": {
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"model": "claude-haiku-4-20250514",
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@ -166,23 +319,33 @@ ANTHROPIC_API_KEY=sk-ant-...
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- **Claude Haiku**: ~$0.03-0.05/hour
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- **Total**: ~$0.40/hour of recording
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## Project Structure
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## Advanced Features
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```
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secondvoice/
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├── src/
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│ ├── main.cpp # Entry point
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│ ├── audio/ # Audio capture & buffer
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│ ├── api/ # Whisper & Claude clients
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│ ├── ui/ # ImGui interface
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│ ├── utils/ # Config & thread-safe queue
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│ └── core/ # Pipeline orchestration
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├── docs/ # Documentation
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├── recordings/ # Output recordings
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├── config.json # Runtime configuration
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├── .env # API keys (not committed)
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└── CMakeLists.txt # Build configuration
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```
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### GPU Forcing (Hybrid Graphics Systems)
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`main.cpp` exports symbols to force dedicated GPU on Optimus/PowerXpress systems:
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- `NvOptimusEnablement` - Forces NVIDIA GPU
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- `AmdPowerXpressRequestHighPerformance` - Forces AMD GPU
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Critical for laptops with both integrated and dedicated GPUs.
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### Hallucination Filtering
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`Pipeline.cpp` maintains an extensive list (~65 patterns) of known Whisper hallucinations:
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- YouTube phrases: "Thank you for watching", "Subscribe", "Like and comment"
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- Chinese video endings: "谢谢观看", "再见", "订阅我的频道"
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- Music symbols: "♪♪", "🎵"
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- Silence markers: "...", "silence", "inaudible"
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These are automatically filtered before translation to avoid wasting API calls.
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### Console-Only Build
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A `SecondVoice_Console` target exists for headless testing:
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- Uses `main_console.cpp`
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- No ImGui/GLFW dependencies
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- Outputs transcriptions to stdout
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- Useful for debugging and automated testing
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## Development
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- Check all system dependencies are installed
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- Try `cmake --build build --clean-first`
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## Project Structure
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```
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secondvoice/
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├── src/
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│ ├── main.cpp # Entry point, forces NVIDIA GPU
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│ ├── core/
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│ │ └── Pipeline.cpp # Audio→Transcription→Translation orchestration
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│ ├── audio/
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│ │ ├── AudioCapture.cpp # PortAudio + VAD segmentation
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│ │ ├── AudioBuffer.cpp # Sample accumulation, WAV/Opus export
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│ │ └── NoiseReducer.cpp # RNNoise (16→48→16 kHz)
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│ ├── api/
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│ │ ├── WhisperClient.cpp # OpenAI Whisper (multipart/form-data)
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│ │ ├── ClaudeClient.cpp # Anthropic Claude (JSON)
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│ │ └── WinHttpClient.cpp # Native Windows HTTP
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│ ├── ui/
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│ │ └── TranslationUI.cpp # ImGui interface + VAD controls
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│ └── utils/
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│ ├── Config.cpp # config.json + .env loader
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│ └── ThreadSafeQueue.h # Lock-free audio queue
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├── docs/ # Build guides
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├── sessions/ # Session recordings + logs
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├── recordings/ # Legacy recordings directory
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├── denoised/ # Denoised audio outputs
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├── config.json # Runtime configuration
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├── .env # API keys (not committed)
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├── CLAUDE.md # Development guide for Claude Code
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├── PLAN_DEBUG.md # Active debugging plan
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└── CMakeLists.txt # Build configuration
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```
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### External Dependencies
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**Fetched via CMake FetchContent**:
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- ImGui v1.90.1 - UI framework
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- Opus v1.5.2 - Audio encoding
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- Ogg v1.3.6 - Container format
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- RNNoise v0.1.1 - Neural network noise reduction
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**vcpkg Dependencies** (x64-mingw-static triplet):
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- portaudio - Cross-platform audio I/O
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- nlohmann_json - JSON parsing
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- glfw3 - Windowing/input
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- glad - OpenGL loader
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## Roadmap
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### Phase 1 - MVP (Current)
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- ✅ Audio capture
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- ✅ Whisper integration
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- ✅ Claude integration
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- ✅ ImGui UI
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- ✅ Stop button
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### Phase 1 - MVP ✅ (Complete)
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- ✅ Audio capture with VAD
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- ✅ Noise reduction (RNNoise)
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- ✅ Whisper API integration
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- ✅ Claude API integration
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- ✅ ImGui UI with runtime VAD adjustment
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- ✅ Opus compression
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- ✅ Hallucination filtering
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- ✅ Session recording
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### Phase 2 - Enhancement
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- ⬜ Auto-summary post-meeting
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- ⬜ Export transcripts
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- ⬜ Search functionality
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### Phase 2 - Debugging 🔄 (Current)
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- 🔄 Session logging (JSON per segment)
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- 🔄 Improved Whisper prompt engineering
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- 🔄 VAD threshold optimization
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- 🔄 Context propagation between segments
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- ⬜ Automated testing with sample audio
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### Phase 3 - Enhancement
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- ⬜ Auto-summary post-meeting (Claude analysis)
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- ⬜ Full-text search (SQLite FTS5)
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- ⬜ Semantic search (embeddings)
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- ⬜ Speaker diarization
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- ⬜ Replay mode
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- ⬜ Replay mode with synced transcripts
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- ⬜ Multi-language support extension
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## Development Documentation
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- **CLAUDE.md** - Development guide for Claude Code AI assistant
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- **PLAN_DEBUG.md** - Active debugging plan with identified issues and solutions
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- **WINDOWS_BUILD.md** - Detailed Windows build instructions
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- **WINDOWS_MINGW.md** - MinGW-specific build guide
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- **WINDOWS_QUICK_START.md** - Quick start for Windows users
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## Contributing
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This is a personal project built to solve a real need. Bug reports and suggestions welcome:
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**Known issues**: See `PLAN_DEBUG.md` for current debugging efforts
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**Architecture**: See `CLAUDE.md` for detailed system design
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## License
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See LICENSE file.
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## Contributing
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## Acknowledgments
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This is a personal project, but suggestions and bug reports are welcome via issues.
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## Contact
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See docs/SecondVoice.md for project context and motivation.
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- OpenAI Whisper for excellent Chinese transcription
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- Anthropic Claude for context-aware translation
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- RNNoise for neural network-based noise reduction
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- ImGui for clean, immediate-mode UI
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@ -10,7 +10,7 @@
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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": "Transcription en direct d'une conversation en chinois mandarin. Plusieurs interlocuteurs parlent, parfois en même temps. RÈGLES STRICTES: (1) Ne transcris QUE les paroles audibles en chinois. (2) Si l'audio est inaudible, du bruit, ou du silence, renvoie une chaîne vide. (3) NE GÉNÈRE JAMAIS ces phrases: 谢谢观看, 感谢收看, 订阅, 请订阅, 下期再见, Thank you, Subscribe, 字幕. (4) Ignore: musique, applaudissements, rires, bruits de fond, respirations.",
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"prompt": "Transcription en direct d'une conversation en chinois mandarin. Plusieurs interlocuteurs parlent, parfois en même temps. Si un contexte de phrases précédentes est fourni, utilise-le pour maintenir la cohérence (noms propres, sujets, terminologie). RÈGLES STRICTES: (1) Ne transcris QUE les paroles audibles en chinois. (2) Si l'audio est inaudible, du bruit, ou du silence, renvoie une chaîne vide. (3) NE GÉNÈRE JAMAIS ces phrases: 谢谢观看, 感谢收看, 订阅, 请订阅, 下期再见, Thank you, Subscribe, 字幕. (4) Ignore: musique, applaudissements, rires, bruits de fond, respirations.",
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"stream": false,
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"response_format": "text"
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},
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@ -135,16 +135,12 @@ int AudioCapture::audioCallback(const void* input, void* output,
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// Speech = energy OK AND (ZCR OK or very high energy)
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bool frame_has_speech = energy_ok && (zcr_ok || denoised_rms > adaptive_rms_thresh * 3.0f);
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// Hang time logic: don't immediately cut on silence
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// Reset trailing silence counter when speech detected
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if (frame_has_speech) {
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self->hang_frames_ = self->hang_frames_threshold_; // Reset hang counter
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} else if (self->hang_frames_ > 0) {
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self->hang_frames_--;
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frame_has_speech = true; // Keep "speaking" during hang time
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self->consecutive_silence_frames_ = 0;
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}
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// Calculate durations in samples
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int silence_samples_threshold = (self->silence_duration_ms_ * self->sample_rate_ * self->channels_) / 1000;
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int min_speech_samples = (self->min_speech_duration_ms_ * self->sample_rate_ * self->channels_) / 1000;
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int max_speech_samples = (self->max_speech_duration_ms_ * self->sample_rate_ * self->channels_) / 1000;
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@ -183,16 +179,17 @@ int AudioCapture::audioCallback(const void* input, void* output,
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}
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self->speech_buffer_.clear();
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self->speech_samples_count_ = 0;
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self->consecutive_silence_frames_ = 0; // Reset after forced flush
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// Reset stream for next segment
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if (self->noise_reducer_) {
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self->noise_reducer_->resetStream();
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}
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}
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} else {
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// True silence (after hang time expired)
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// Silence detected
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self->silence_samples_count_ += sample_count;
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// If we were speaking and now have enough silence, flush
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// If we were speaking and now have silence, track consecutive silence frames
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if (self->speech_buffer_.size() > 0) {
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// Add trailing silence (denoised)
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if (!denoised_samples.empty()) {
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@ -204,7 +201,16 @@ int AudioCapture::audioCallback(const void* input, void* output,
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}
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}
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if (self->silence_samples_count_ >= silence_samples_threshold) {
|
||||
// Increment consecutive silence frame counter
|
||||
self->consecutive_silence_frames_++;
|
||||
|
||||
// Calculate threshold in frames (callbacks)
|
||||
// frames_per_buffer = frame_count from callback
|
||||
int frames_per_buffer = static_cast<int>(frame_count);
|
||||
int silence_threshold_frames = (self->silence_duration_ms_ * self->sample_rate_) / (1000 * frames_per_buffer);
|
||||
|
||||
// Flush when consecutive silence exceeds threshold
|
||||
if (self->consecutive_silence_frames_ >= silence_threshold_frames) {
|
||||
self->is_speech_active_.store(false, std::memory_order_relaxed);
|
||||
|
||||
// Flush if we have enough speech
|
||||
@ -220,7 +226,9 @@ int AudioCapture::audioCallback(const void* input, void* output,
|
||||
|
||||
float duration = static_cast<float>(self->speech_buffer_.size()) /
|
||||
(self->sample_rate_ * self->channels_);
|
||||
std::cout << "[VAD] Speech ended (noise_floor=" << self->noise_floor_
|
||||
std::cout << "[VAD] Speech ended (trailing silence detected, "
|
||||
<< self->consecutive_silence_frames_ << " frames, "
|
||||
<< "noise_floor=" << self->noise_floor_
|
||||
<< "), flushing " << duration << "s (denoised)" << std::endl;
|
||||
|
||||
if (self->callback_) {
|
||||
@ -233,6 +241,7 @@ int AudioCapture::audioCallback(const void* input, void* output,
|
||||
|
||||
self->speech_buffer_.clear();
|
||||
self->speech_samples_count_ = 0;
|
||||
self->consecutive_silence_frames_ = 0; // Reset after flush
|
||||
// Reset stream for next segment
|
||||
if (self->noise_reducer_) {
|
||||
self->noise_reducer_->resetStream();
|
||||
|
||||
@ -77,9 +77,8 @@ private:
|
||||
float noise_floor_ = 0.005f; // Estimated background noise level
|
||||
float noise_floor_alpha_ = 0.001f; // Slower adaptation
|
||||
|
||||
// Hang time - wait before cutting to avoid mid-sentence cuts
|
||||
int hang_frames_ = 0;
|
||||
int hang_frames_threshold_ = 35; // ~350ms tolerance for pauses (was 20)
|
||||
// Trailing silence detection - count consecutive silence frames after speech
|
||||
int consecutive_silence_frames_ = 0;
|
||||
|
||||
// Zero-crossing rate for speech vs noise discrimination
|
||||
float last_zcr_ = 0.0f;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user