# Whisper Transcription

Transcribe audio/video to text with word-level timestamps using OpenAI Whisper.

- Repository: https://github.com/benchflow-ai/skillsbench (1888 stars)
- Category: Media
- License: Apache-2.0
- Last updated: 2026-10-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add benchflow-ai/skillsbench --skill whisper-transcription -g -a claude-code -y`
Codex: `npx skills add benchflow-ai/skillsbench --skill whisper-transcription -g -a codex -y`
Cursor: `npx skills add benchflow-ai/skillsbench --skill whisper-transcription -g -a cursor -y`
Gemini CLI: `npx skills add benchflow-ai/skillsbench --skill whisper-transcription -g -a gemini-cli -y`

## Description

Transcribe audio/video to text with word-level timestamps using OpenAI Whisper. Use when you need speech-to-text with accurate timing information for each word.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Whisper Transcription

OpenAI Whisper provides accurate speech-to-text with word-level timestamps.

## Installation

```bash
pip install openai-whisper
```

## Model Selection

**Use the `tiny` model for fast transcription** - it's sufficient for most tasks and runs much faster:

| Model | Size | Speed | Accuracy |
|-------|------|-------|----------|
| tiny | 39 MB | Fastest | Good for clear speech |
| base | 74 MB | Fast | Better accuracy |
| small | 244 MB | Medium | High accuracy |

**Recommendation: Start with `tiny` - it handles clear interview/podcast audio well.**

## Basic Usage with Word Timestamps

```python
import whisper
import json

def transcribe_with_timestamps(audio_path, output_path):
    """
    Transcribe audio and get word-level timestamps.

    Args:
        audio_path: Path to audio/video file
        output_path: Path to save JSON output
    """
    # Use tiny model for speed
    model = whisper.load_model("tiny")

    # Transcribe with word timestamps
    result = model.transcribe(
        audio_path,
        word_timestamps=True,
        language="en"  # Specify language for better accuracy
    )

    # Extract words with timestamps
    words = []
    for segment in result["segments"]:
        if "words" in segment:
            for word_info in segment["words"]:
                words.append({
                    "word": word_info["word"].strip(),
                    "start": word_info["start"],
                    "end": word_info["end"]
                })

    with open(output_path, "w") as f:
        json.dump(words, f, indent=2)

    return words
```

## Detecting Specific Words

```python
def find_words(transcription, target_words):
    """
    Find specific words in transcription with their timestamps.

    Args:
        transcription: List of word dicts with 'word', 'start', 'end'
        target_words: Set of words to find (lowercase)

The rest: https://github.com/benchflow-ai/skillsbench/blob/HEAD/tasks-extra/video-filler-word-remover/environment/skills/whisper-transcription/SKILL.md