Feedback: speech-to-text-pre-recorded-audio-multichannel-transcription

Documentation Feedback

Original URL: https://assemblyai.com/docs/speech-to-text/pre-recorded-audio/multichannel-transcription
Category: speech-to-text
Generated: 05/08/2025, 4:24:51 pm

---

Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:24:50 pm

Technical Documentation Analysis: Multichannel Transcription

Overall Assessment

The documentation provides functional code examples but lacks crucial context and explanation that would help users understand when and how to effectively use multichannel transcription. The structure prioritizes code over conceptual understanding.

Specific Issues & Recommendations

1. Missing Critical Information

Issue: No explanation of what multichannel audio actually is or when to use it. Recommendation: Add a comprehensive introduction section:

## What is Multichannel Transcription?

Multichannel transcription processes audio files where different speakers are recorded on separate audio channels (e.g., left/right channels in stereo, or individual tracks in multi-track recordings). This is different from speaker diarization, which separates speakers from a single audio channel.

### When to Use Multichannel Transcription
- **Phone calls** recorded with each participant on separate channels
- **Interviews** with dedicated microphones per speaker
- **Podcast recordings** with individual tracks per host/guest
- **Conference calls** with channel separation

### When NOT to Use It
- Single-channel audio with multiple speakers (use speaker diarization instead)
- Audio where all speakers are mixed into one channel

Issue: No explanation of audio requirements or limitations. Recommendation: Add technical requirements section:

## Audio Requirements
- **Minimum channels**: 2
- **Maximum channels**: [specify limit]
- **Supported formats**: WAV, MP3, MP4, etc.
- **Channel configuration**: Each speaker should be primarily on one channel
- **Quality recommendations**: Minimum bitrate, sample rate guidelines

2. Response Format Documentation Gap

Issue: The documentation mentions response properties but doesn’t show what they look like. Recommendation: Add a complete response example:

## Response Format

```json
{
  "id": "transcript_id",
  "status": "completed",
  "text": "Combined transcript text...",
  "audio_channels": 2,
  "utterances": [
    {
      "speaker": 1,
      "text": "Hello, how are you?",
      "start": 1000,
      "end": 3000,
      "confidence": 0.95
    },
    {
      "speaker": 2,
      "text": "I'm doing well, thanks!",
      "start": 3500,
      "end": 5200,
      "confidence": 0.92
    }
  ],
  "words": [
    {
      "text": "Hello",
      "start": 1000,
      "end": 1400,
      "confidence": 0.98,
      "speaker": 1
    }
    // ... more words
  ]
}

### 3. Code Example Issues

**Issue**: Inconsistent terminology - some examples use "Channel" others use "Speaker" in output.
**Current**: PHP example shows "Speaker {utterance['speaker']}" while others show "Channel {utterance.speaker}"
**Recommendation**: Standardize to "Channel {utterance.speaker}" across all examples.

**Issue**: No error handling examples for multichannel-specific errors.
**Recommendation**: Add error handling section:

```python
# Handle multichannel-specific errors
if transcript.status == "error":
    if "multichannel" in transcript.error.lower():
        print("Multichannel processing failed. Check if your audio has multiple channels.")
    elif "channel" in transcript.error.lower():
        print("Channel separation issue. Verify audio channel configuration.")
    raise RuntimeError(f"Transcription failed: {transcript.error}")

4. Structure Improvements

Current structure issues:

• Jumps straight to code without context
• Performance note buried at the bottom
• No troubleshooting guidance

Recommended new structure:

# Multichannel Transcription

## Overview
[What it is, when to use it]

## Prerequisites
[Audio requirements, format specifications]

## Quick Start
[Minimal working example]

## Complete Examples
[Full code examples by language]

## Response Format
[Detailed response structure]

## Performance Considerations
[Timing, cost implications]

## Troubleshooting
[Common issues and solutions]

## Related Features
[Links to speaker diarization, etc.]

5. User Experience Pain Points

Issue: Users can’t easily distinguish between multichannel transcription and speaker diarization. Recommendation: Add comparison table:

Feature	Multichannel	Speaker Diarization
Audio input	Separate channels per speaker	Single channel, multiple speakers
Use case	Phone calls, interviews	Meetings, conversations
Processing time	+25% longer	Standard
Accuracy	Higher (channel separation)	Good (AI-based separation)

Issue: No guidance on testing or validating results. Recommendation: Add validation section:

## Validating Results

1. **Check channel count**: Verify `audio_channels` matches your input
2. **Review speaker distribution**: Ensure utterances are properly distributed across channels
3. **Validate timestamps**: Check for overlapping speech detection

6. Missing Practical Guidance

Recommendation: Add these sections:

## Best Practices
- Ensure speakers are primarily on separate channels
- Test with a short sample first
- Consider audio quality requirements
- Use consistent microphone levels across channels

## Common Issues
- **Mixed channels**: If speakers appear on wrong channels, check your audio routing
- **Empty channels**: Ensure all channels contain audio data
- **Poor separation**: Verify channel isolation in your source audio

## Integration Tips
- Combine with other features (speaker names, custom vocabulary)
- Post-process results for specific formatting needs

7. Code Quality Improvements

Issue: Examples don’t show real-world usage patterns. Recommendation: Add practical example:

def process_interview_recording(audio_file_path):
    """Process a 2-channel interview recording"""
    config = aai.TranscriptionConfig(
        multichannel=True,
        punctuate=True,
        format_text=True
    )

    transcript = aai.Transcriber(config=config).transcribe(audio_file_path)

    if transcript.status == "error":
        raise RuntimeError(f"Transcription failed: {transcript.error}")

    # Separate responses by speaker/channel
    interviewer_responses = []
    interviewee_responses = []

    for utterance in transcript.utterances:
        if utterance.speaker == 1:  # Assume channel 1 is interviewer
            interviewer_responses.append(utterance.text)
        else:  # Channel 2 is interviewee
            interviewee_responses.append(utterance.text)

    return {
        'interviewer': interviewer_responses,
        'interviewee': interviewee_responses,
        'full_transcript': transcript.text
    }

Summary

The current documentation is code-heavy but context-light. Users need more conceptual understanding, clearer use cases, complete response documentation, and practical guidance to effectively implement multichannel transcription. The recommended improvements would transform this from a code reference into comprehensive, user-friendly documentation.

---