# Automatic Speech Recognition Asr

> Transcribe audio segments to text using Whisper models. Use larger models (small, base, medium, large-v3) for better accuracy, or faster-whisper for optimized performance. Always align transcription timestamps with diarization segments for accurate speaker-labeled subtitles. Use when this capability is needed.

- Skill: `tomevault-io/automatic-speech-recognition-asr` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/automatic-speech-recognition-asr`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/automatic-speech-recognition-asr/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/automatic-speech-recognition-asr

---


# Automatic Speech Recognition (ASR)

## Overview

After speaker diarization, you need to transcribe each speech segment to text. Whisper is the current state-of-the-art for ASR, with multiple model sizes offering different trade-offs between accuracy and speed.

## When to Use

- After speaker diarization is complete
- Need to generate speaker-labeled transcripts
- Creating subtitles from audio segments
- Converting speech segments to text

## Whisper Model Selection

### Model Size Comparison

| Model | Size | Speed | Accuracy | Best For |
|-------|------|-------|----------|----------|
| tiny | 39M | Fastest | Lowest | Quick testing, low accuracy needs |
| base | 74M | Fast | Low | Fast processing with moderate accuracy |
| small | 244M | Medium | Good | **Recommended balance** |
| medium | 769M | Slow | Very Good | High accuracy needs |
| large-v3 | 1550M | Slowest | **Best** | **Maximum accuracy** |

### Recommended: Use `small` or `large-v3`

**For best accuracy (recommended for this task):**
```python
import whisper

model = whisper.load_model("large-v3")  # Best accuracy
result = model.transcribe(audio_path)
```

**For balanced performance:**
```python
import whisper

model = whisper.load_model("small")  # Good balance
result = model.transcribe(audio_path)
```

## Faster-Whisper (Optimized Alternative)

For faster processing with similar accuracy, use `faster-whisper`:

```python
from faster_whisper import WhisperModel

# Use small model with CPU int8 quantization
model = WhisperModel("small", device="cpu", compute_type="int8")

# Transcribe
segments, info = model.transcribe(audio_path, beam_size=5)

# Process segments
for segment in segments:
    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
```

**Advantages:**
- Faster than standard Whisper
- Lower memory usage with quantization
- Similar accuracy to standard Whisper

## Aligning Transcriptions with Diarization Segments

After diarization, you need to map Whisper transcriptions to speaker segments:

```python
# After diarization, you have turns with speaker labels
turns = [
    {'start': 0.8, 'duration': 0.86, 'speaker': 'SPEAKER_01'},
    {'start': 5.34, 'duration': 0.21, 'speaker': 'SPEAKER_01'},
    # ...
]

# Run Whisper transcription
model = whisper.load_model("large-v3")
result = model.transcribe(audio_path)

# Map transcriptions to turns
transcripts = {}
for i, turn in enumerate(turns):
    turn_start = turn['start']
    turn_end = turn['start'] + turn['duration']
    
    # Find overlapping Whisper segments
    overlapping_text = []
    for seg in result['segments']:
        seg_start = seg['start']
        seg_end = seg['end']
        
        # Check if Whisper segment overlaps with diarization turn
        if seg_start < turn_end and seg_end > turn_start:
            overlapping_text.append(seg['text'].strip())
    
    # Combine overlapping segments
    transcripts[i] = ' '.join(overlapping_text) if overlapping_text else '[INAUDIBLE]'
```

## Handling Empty or Inaudible Segments

```python
# If no transcription found for a segment
if not overlapping_text:
    transcripts[i] = '[INAUDIBLE]'
    
# Or skip very short segments
if turn['duration'] < 0.3:
    transcripts[i] = '[INAUDIBLE]'
```

## Language Detection

Whisper can auto-detect language, but you can also specify:

```python
# Auto-detect (recommended)
result = model.transcribe(audio_path)

# Or specify language for better accuracy
result = model.transcribe(audio_path, language="en")
```

## Best Practices

1. **Use larger models for better accuracy**: `small` minimum, `large-v3` for best results
2. **Align timestamps carefully**: Match Whisper segments with diarization turns
3. **Handle overlaps**: Multiple Whisper segments may overlap with one diarization turn
4. **Handle gaps**: Some diarization turns may have no corresponding transcription
5. **Post-process text**: Clean up punctuation, capitalization if needed

## Common Issues

1. **Low transcription accuracy**: Use larger model (small → medium → large-v3)
2. **Slow processing**: Use faster-whisper or smaller model
3. **Misaligned timestamps**: Check time alignment between diarization and transcription
4. **Missing transcriptions**: Check for very short segments or silence

## Integration with Subtitle Generation

After transcription, combine with speaker labels for subtitles:

```python
def generate_subtitles_ass(turns, transcripts, output_path):
    # ... header code ...
    
    for i, turn in enumerate(turns):
        start_time = format_time(turn['start'])
        end_time = format_time(turn['start'] + turn['duration'])
        speaker = turn['speaker']
        text = transcripts.get(i, "[INAUDIBLE]")
        
        # Format: SPEAKER_XX: text
        f.write(f"Dialogue: 0,{start_time},{end_time},Default,,0,0,0,,{speaker}: {text}\n")
```

## Performance Tips

1. **For accuracy**: Use `large-v3` model
2. **For speed**: Use `faster-whisper` with `small` model
3. **For memory**: Use `faster-whisper` with `int8` quantization
4. **Batch processing**: Process multiple segments together if possible

---
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<!-- tomevault:4.0:skill_md:2026-04-11 -->