Building Transcription: Why Whisper Is Still the Best

🎯 The Need

I needed transcription for my app. Users upload audio, I need text. Simple requirement, but finding the right solution wasn't.

I tried Google Speech-to-Text, AWS Transcribe, and Whisper. Whisper won.

The winner: Whisper. Here's why it's still the best choice.

📊 Comparison

✅ Why Whisper Wins

1. Accuracy

Whisper is more accurate, especially for:

• Accented speech
• Technical terms
• Multiple languages
• Background noise

2. Cost

Whisper is free. Run it locally, no API costs.

3. Privacy

Everything runs locally. No data leaves your server.

4. No Rate Limits

No API rate limits. Process as much as you want.

❌ Why Cloud Services Lose

• Cost: Gets expensive at scale
• Privacy: Data goes to third parties
• Rate limits: API throttling
• Dependency: Requires internet

💡 My Setup

I run Whisper in a Docker container:

FROM python:3.11

RUN pip install openai-whisper

WORKDIR /app
COPY transcribe.py .

CMD ["python", "transcribe.py"]

import whisper

model = whisper.load_model("base")

def transcribe(audio_file):
    result = model.transcribe(audio_file)
    return result["text"]

📊 Real Results

Test file: 5-minute technical presentation

• Whisper: 95% accuracy, 30 seconds processing
• Google: 92% accuracy, 10 seconds, $0.03
• AWS: 90% accuracy, 12 seconds, $0.002

Whisper is more accurate and free. The processing time is acceptable.

💡 Key Takeaways

• Whisper is more accurate than cloud services
• It's free and runs locally
• Privacy is guaranteed (no data leaves your server)
• No rate limits or API costs
• Processing time is acceptable for most use cases

For transcription, Whisper is still the best choice. It's accurate, free, and private. The only downside is processing time, but that's acceptable for most applications.