Feedback: guides-automatic-language-detection-workflow

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/automatic-language-detection-workflow
Category: guides
Generated: 05/08/2025, 4:43:15 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:43:14 pm

Technical Documentation Analysis: Automatic Language Detection Workflow

Overall Assessment

This documentation provides a clear technical workflow but has several areas for improvement in completeness, error handling, and user experience.

Critical Missing Information

1. Error Handling & Troubleshooting

Issue: No guidance on handling failures or edge cases.

Recommendations:

def detect_language(audio_url):
    try:
        config = aai.TranscriptionConfig(
            audio_end_at=60000,
            language_detection=True,
            speech_model=aai.SpeechModel.nano,
        )
        transcript = transcriber.transcribe(audio_url, config=config)

        # Check if transcription was successful
        if transcript.status == aai.TranscriptStatus.error:
            raise Exception(f"Language detection failed: {transcript.error}")

        language_code = transcript.json_response.get("language_code")
        if not language_code:
            raise Exception("No language code returned")

        return language_code
    except Exception as e:
        print(f"Error detecting language for {audio_url}: {e}")
        return "en"  # Default fallback

2. Cost Calculation Details

Issue: Mentions $0.002 cost but lacks complete pricing breakdown.

Add section:

## Cost Breakdown
- Nano ALD (60 seconds): $0.002 per detection
- Universal transcription: $0.62 per hour
- Nano transcription: $0.10 per hour
- Total workflow cost = $0.002 + (audio_duration_hours × model_rate)

3. API Key Security

Issue: Shows hardcoded API key without security guidance.

Replace with:

import os
import assemblyai as aai

# Secure API key handling
aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
if not aai.settings.api_key:
    raise ValueError("Please set ASSEMBLYAI_API_KEY environment variable")

Structure Improvements

1. Add Prerequisites Section

## Prerequisites
- Python 3.7 or higher
- AssemblyAI API key (get one [here](https://www.assemblyai.com/dashboard/signup))
- Audio files in supported formats (MP3, WAV, M4A, etc.)
- Basic understanding of Python async programming (optional for advanced usage)

2. Reorganize Content Flow

Current flow jumps directly into code. Suggest:

1. Overview (what this workflow does)
2. When to use this workflow vs alternatives
3. Prerequisites
4. Step-by-step implementation
5. Advanced configuration
6. Troubleshooting

Enhanced Code Examples

1. Production-Ready Implementation

class LanguageDetectionWorkflow:
    def __init__(self, api_key=None):
        self.transcriber = aai.Transcriber()
        if api_key:
            aai.settings.api_key = api_key

    def detect_language(self, audio_url, detection_duration=60000):
        """
        Detect language from audio file.

        Args:
            audio_url (str): URL or path to audio file
            detection_duration (int): Duration in ms for detection (default: 60000)

        Returns:
            str: Detected language code or 'en' as fallback
        """
        # Implementation with error handling

    def transcribe_with_detection(self, audio_url, enable_audio_intelligence=False):
        """Complete workflow with language detection and transcription."""
        # Implementation

2. Batch Processing Example

def process_multiple_files(audio_urls, max_concurrent=3):
    """Process multiple files with rate limiting."""
    import asyncio
    from concurrent.futures import ThreadPoolExecutor

    def process_single_file(url):
        language_code = detect_language(url)
        return transcribe_file(url, language_code)

    with ThreadPoolExecutor(max_workers=max_concurrent) as executor:
        results = list(executor.map(process_single_file, audio_urls))

    return results

Missing Critical Information

1. Supported Audio Formats

Add section explaining:

• Supported file formats
• File size limits
• Duration limits
• Quality recommendations

2. Language Detection Accuracy

## Language Detection Accuracy
- Confidence scores and how to interpret them
- When detection might fail (very short audio, poor quality, mixed languages)
- Fallback strategies for low-confidence detection

3. Model Selection Logic

Expand explanation:

## Model Selection Decision Tree
1. **Language detected and supported by Universal** → Use Universal
2. **Language detected but not supported by Universal** → Use Nano
3. **Language detection fails** → Default to English + Universal
4. **Low confidence detection** → Prompt user or use fallback logic

User Experience Improvements

1. Add Validation Functions

def validate_audio_url(url):
    """Validate audio URL/file exists and is accessible."""
    # Implementation

def estimate_cost(audio_duration_minutes, detected_language):
    """Estimate total cost for the workflow."""
    # Implementation

2. Progress Indicators

def transcribe_with_progress(audio_url):
    """Transcribe with progress updates."""
    print("🔍 Detecting language...")
    language_code = detect_language(audio_url)
    print(f"✅ Language detected: {language_code}")

    print("📝 Starting transcription...")
    transcript = transcribe_file(audio_url, language_code)
    print("✅ Transcription complete!")

    return transcript

3. Configuration Options

# Add configuration class
class WorkflowConfig:
    def __init__(
        self,
        detection_duration=60000,
        enable_audio_intelligence=False,
        fallback_language="en",
        confidence_threshold=0.8
    ):
        self.detection_duration = detection_duration
        self.enable_audio_intelligence = enable_audio_intelligence
        self.fallback_language = fallback_language
        self.confidence_threshold = confidence_threshold

Additional Sections Needed

1. FAQ Section

• What happens if language detection is wrong?
• Can I override the detected language?
• How accurate is the 60-second detection?
• What if my audio contains multiple languages?

2. Performance Considerations

• Expected processing times
• Rate limits
• Optimization tips for large files

3. Integration Examples

• Flask/FastAPI web service
• Background job processing
• Real-time audio processing

This enhanced documentation would significantly improve user success rates and reduce support requests.

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