Whisper
Speech-to-text transcription.
Whisper converts audio into text. Useful for meeting notes, interviews, and generating subtitles/captions.
What is Whisper?
Whisper is an open-source speech-to-text model developed by OpenAI. Trained on 680,000 hours of multilingual audio data, it is one of the strongest tools in the industry for transcription accuracy.
Supporting 99 languages, Whisper demonstrates high accuracy even in challenging conditions — background noise, accents, and fast speech. Its open-source nature means it can be run completely locally and for free.
It is widely used for meeting notes, interview transcripts, video subtitle generation, and multilingual content processing. Technical users can integrate it directly via Python, while third-party applications with user-friendly interfaces make it usable without any technical knowledge.
Key Features
High Accuracy
Strong transcription even in noisy environments and with accents.
99 Language Support
Transcription and translation across a wide language range.
Open Source & Free
Run locally on your machine completely for free.
Multiple Format Support
Processes MP3, MP4, WAV, M4A, and other audio formats.
API & Python Integration
Easily integrates into applications and workflows.
Subtitle Output
Generates time-stamped subtitle files in SRT and VTT formats.
Who is Whisper ideal for?
Pricing
Prices may vary — check the official site for the latest information.
Pros & Cons
✓Strengths
- ✓Open source and completely free for local use
- ✓High accuracy in 99 languages
- ✓Strong in noisy environments
- ✓SRT subtitle output
- ✓Easy API integration
✗Things to Consider
- ✗Direct use requires technical knowledge
- ✗Real-time transcription is limited
- ✗No GUI — a third-party tool may be needed
Example Prompts & Expected Outputs
Copy and use these ready-made prompts directly.
import whisper model = whisper.load_model("medium") result = model.transcribe("meeting.mp3", language="en") print(result["text"])
Expected Output: "...we'll be discussing the project updates today. First item: marketing campaign results. Last month we achieved 18% growth and..." Note: "medium" model offers a good balance. Use "large-v3" for higher accuracy.
result = model.transcribe("video.mp4", language="en", word_timestamps=True) from whisper.utils import get_writer writer = get_writer("srt", ".") writer(result, "video.mp4")
Expected Output (video.srt): 1 00:00:00,000 --> 00:00:03,500 Hello, today we're looking at AI tools. 2 00:00:03,500 --> 00:00:07,200 Our first tool is OpenAI's Whisper model.
# Translate speech from any language to English text result = model.transcribe("speech.mp3", task="translate") print(result["text"]) # Direct English output
Expected Output: "Hello, today we are examining artificial intelligence tools. Our first tool is OpenAI's Whisper model..." Note: The translate task converts any language directly to English.
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