TRANSCRIPT_DEBUG.md

Transcript Debugging Guide

Issue: Empty Transcripts ("No transcript available")

Complete Flow Analysis

1. Django App → API Request (slaq-version-c/diagnosis/ai_engine/detect_stuttering.py)

Location: Line 269-274

response = requests.post(
    self.api_url,
    files=files,
    data={
        "transcript": proper_transcript if proper_transcript else "",
        "language": lang_code,
    },
    timeout=self.api_timeout
)

Status: ✅ Sending transcript parameter correctly

---

2. API Receives Request (slaq-version-c-ai-enginee/app.py)

Location: Line 70-73

@app.post("/analyze")
async def analyze_audio(
    audio: UploadFile = File(...),
    transcript: str = Form("")  # ✅ Fixed: Now uses Form() for multipart
):

Status: ✅ Fixed - Now correctly receives transcript via Form()

---

3. API Calls Model (slaq-version-c-ai-enginee/app.py)

Location: Line 106

result = detector.analyze_audio(temp_file, transcript)

Status: ✅ Passing transcript correctly

---

4. Model Transcribes Audio (slaq-version-c-ai-enginee/diagnosis/ai_engine/detect_stuttering.py)

Location: Line 313-369 (_transcribe_with_timestamps)

Potential Issues:

• ❓ IndicWav2Vec decoding might not work with processor.batch_decode()
• ❓ Need to use tokenizer directly
• ❓ Model might not be producing valid predictions

Status: ⚠️ LIKELY ISSUE HERE - Decoding method may be incorrect

---

5. Model Returns Result (slaq-version-c-ai-enginee/diagnosis/ai_engine/detect_stuttering.py)

Location: Line 787-794

actual_transcript = transcript if transcript else ""
target_transcript = proper_transcript if proper_transcript else transcript if transcript else ""

return {
    'actual_transcript': actual_transcript,
    'target_transcript': target_transcript,
    ...
}

Status: ✅ Returns transcripts correctly (if transcript is not empty)

---

6. API Returns Response (slaq-version-c-ai-enginee/app.py)

Location: Line 109-113

actual = result.get('actual_transcript', '')
target = result.get('target_transcript', '')
logger.info(f"📝 Result transcripts - Actual: '{actual[:100]}' (len: {len(actual)}), Target: '{target[:100]}' (len: {len(target)})")
return result

Status: ✅ Returns JSON with transcripts

---

7. Django Receives Response (slaq-version-c/diagnosis/ai_engine/detect_stuttering.py)

Location: Line 279-410

result = response.json()
# ... formatting ...
actual_transcript = str(api_result.get('actual_transcript', '')).strip()
target_transcript = str(api_result.get('target_transcript', '')).strip()

Status: ✅ Extracts transcripts correctly

---

8. Django Saves to Database (slaq-version-c/diagnosis/tasks.py)

Location: Line 141-142

actual_transcript=actual_transcript,
target_transcript=target_transcript,

Status: ✅ Saves correctly

---

Root Cause Analysis

Most Likely Issue: Transcription Decoding

The IndicWav2Vec model (ai4bharat/indicwav2vec-hindi) may require:

1. Direct tokenizer access instead of processor.batch_decode()
2. CTC decoding with proper tokenizer
3. Special handling for Indic scripts

Fix Applied

Updated _transcribe_with_timestamps() to:

1. Try multiple decoding methods
2. Use tokenizer directly if available
3. Add comprehensive error logging
4. Log predicted IDs for debugging

---

Debugging Steps

1. Check API Logs

When processing audio, look for:

📝 Transcribed text: '...' (length: X)
📝 Final return - Actual: '...' (len: X), Target: '...' (len: Y)
📝 Result transcripts - Actual: '...' (len: X), Target: '...' (len: Y)

2. Check Django Logs

Look for:

📝 Final transcripts - Actual: X chars, Target: Y chars
📝 Saving transcripts - Actual: X chars, Target: Y chars

3. Check Database

Query the AnalysisResult table:

SELECT actual_transcript, target_transcript, LENGTH(actual_transcript) as actual_len, LENGTH(target_transcript) as target_len 
FROM diagnosis_analysisresult 
ORDER BY created_at DESC LIMIT 5;

4. Test API Directly

curl -X POST "http://localhost:7860/analyze" \
  -F "[email protected]" \
  -F "transcript=test transcript" \
  -F "language=hin"

Check the response JSON for actual_transcript and target_transcript.

---

Next Steps

1. Rebuild Docker image with latest changes
2. Check logs during audio processing
3. Verify processor structure - logs will show processor attributes
4. Test with Hindi audio - model is optimized for Hindi
5. Check if model is loaded correctly - verify HF_TOKEN is working

---

Expected Log Output (Success)

🚀 Initializing Advanced AI Engine on cpu...
✅ HF_TOKEN found - using authenticated model access
📋 Processor type: <class 'transformers.models.wav2vec2.processing_wav2vec2.Wav2Vec2Processor'>
📋 Processor attributes: ['batch_decode', 'decode', 'feature_extractor', 'tokenizer', ...]
📋 Tokenizer type: <class 'transformers.models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizer'>
📝 Transcribed text: 'नमस्ते मैं हिंदी बोल रहा हूं' (length: 25)
📝 Final return - Actual: 'नमस्ते मैं हिंदी बोल रहा हूं' (len: 25), Target: '...' (len: X)

---

If Still Empty

1. Model may not be loaded correctly - check HF_TOKEN
2. Audio format issue - ensure 16kHz mono WAV
3. Model not producing predictions - check predicted_ids in logs
4. Tokenizer mismatch - IndicWav2Vec may need special tokenizer initialization