# Fixes Applied for Speech Recognition Issues

**Date:** November 5, 2024
**Issues Fixed:** Language code errors and training data collator problems

## Problems Identified

### 1. Language Code Issue
- **Error:** `Unsupported language: tw`
- **Root Cause:** Whisper doesn't officially support "tw" as a language code
- **Impact:** Server returning 500 errors, transcription failing

### 2. Data Collator Issue
- **Error:** `'list' object has no attribute 'keys'`
- **Root Cause:** Feature extractor expected dictionary format but received list
- **Impact:** Training pipeline failing during data loading

## Fixes Applied

### 1. Language Code Fixes

#### A. Updated Configuration (`config/config.py`)
```python
# Before:
"language": "tw",  # Twi language code for fine-tuned model

# After:
"language": None,  # Auto-detect language (Whisper doesn't officially support 'tw')
```

#### B. Updated Speech Recognizer (`src/speech_recognizer.py`)
- Changed default language parameter from `"tw"` to `None`
- Updated transcription methods to use auto-detection
- Modified both custom and pre-trained model paths

#### C. Updated API Server (`src/api_server.py`)
- Changed default language from `"tw"` to `None`
- Updated API endpoint documentation
- Modified request models to use auto-detection

#### D. Updated Training Script (`train_whisper_twi.py`)
- Changed language configuration to use auto-detection
- Ensures training doesn't fail on unsupported language codes

### 2. Data Collator Fix

#### Fixed in `train_whisper_twi.py`
```python
# Before (line 234):
batch = self.processor.feature_extractor.pad(
    input_features, return_tensors="pt", padding=True
)

# After:
batch = self.processor.feature_extractor.pad(
    {"input_features": input_features}, return_tensors="pt", padding=True
)
```

**Explanation:** The feature extractor's `pad` method expects a dictionary with an "input_features" key, not a raw list of features.

## Files Modified

1. `config/config.py` - Updated language configuration
2. `src/speech_recognizer.py` - Fixed language defaults and transcription methods
3. `src/api_server.py` - Updated API endpoints and request models
4. `train_whisper_twi.py` - Fixed data collator and language configuration
5. `test_fixes.py` - Created test script to verify fixes

## Testing

A comprehensive test script (`test_fixes.py`) has been created to verify:
- Configuration loads correctly with new language settings
- Data collator works with proper dictionary format
- API server initializes without language errors
- Speech recognizer handles auto-detection properly

## How Whisper Will Handle Twi

Since Whisper doesn't officially support "tw" language code:

1. **Auto-Detection:** Whisper will attempt to detect the language automatically
2. **Similar Languages:** May detect as related languages (e.g., "ak" for Akan family)
3. **Fine-Tuned Model:** Once trained on Twi data, the model will better handle Twi regardless of language code
4. **Fallback:** If detection fails, Whisper defaults to English but transcription may still work

## Next Steps

### 1. Test the Fixes
```bash
cd optimized_engine
python test_fixes.py
```

### 2. Run Quick Training
```bash
python run_training.py --quick
```

### 3. Start Server (if training succeeds)
```bash
python main.py server
```

### 4. Test Endpoints
```bash
# Health check
curl http://localhost:8000/health

# Test transcription (replace with actual audio file)
curl -X POST "http://localhost:8000/test-intent" \
  -F "file=@path/to/audio.wav"
```

## Expected Behavior After Fixes

1. **Server Startup:** Should start without language errors
2. **Transcription:** Will use auto-detection instead of failing on "tw"
3. **Training:** Data collator will properly format input features
4. **API Responses:** Will include detected language instead of hardcoded "tw"

## Monitoring Language Detection

The system will now:
- Log detected languages for monitoring
- Allow manual language specification if needed
- Fall back gracefully when detection is uncertain
- Provide confidence scores for transcriptions

## Additional Recommendations

1. **Data Collection:** Monitor what languages Whisper detects for your Twi audio
2. **Fine-Tuning:** The custom model training will improve Twi recognition regardless of language codes
3. **Validation:** Test with various Twi dialects to ensure broad coverage
4. **Fallbacks:** Consider post-processing if Whisper's auto-detection needs correction

## Troubleshooting

If issues persist:

1. **Check Logs:** Look for language-related warnings in server logs
2. **Audio Quality:** Ensure audio files are clear and properly formatted
3. **Model Loading:** Verify Whisper models download correctly
4. **Dependencies:** Ensure all packages are compatible versions

The fixes address the immediate blocking issues and should allow the training pipeline and server to run successfully.