- Category
- AI Tools
- Rank
- No. 53Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- openai
- GitHub
- 108.7k stars
- Latest release
- v20250625
- Date
About
OpenAI's robust speech recognition model trained on 680k hours of multilingual audio, with strong performance on accents and technical language.
What it does
Whisper turns audio into text locally through a Transformer encoder and autoregressive decoder. One token-based model handles transcription, translation into English, language identification, timestamps, and speech detection. It processes full files in sliding 30-second windows and can return plain transcripts, subtitles, tabular segments, or structured JSON.
Why it's ranked here
Whisper is a strong practical choice because it combines a ready command line, a Python library, downloadable model options, and useful output formats. Its model range lets users trade memory and speed against accuracy. The implementation also exposes lower-level decoding and language detection for teams that need more control.
What's good
The package covers both quick file transcription and programmable inference. It accepts common audio formats through FFmpeg, detects language when needed, supports prompts for names or specialist vocabulary, and can produce word timestamps. English-only and multilingual variants provide sensible deployment choices, while checksum verification protects downloaded model weights from corruption.
Tradeoffs
Installation reaches beyond a normal Python package because FFmpeg must be available, and Rust may be needed when tokenizer wheels are unavailable. Memory needs range from roughly one to ten gigabytes of VRAM. Accuracy varies widely by language. Word timestamps for translations may be unreliable, and the faster turbo model cannot translate speech.
How to use it well
Use Whisper for local batch transcription, subtitle generation, multilingual speech intake, or embedding recognition inside Python workflows. Start with turbo for English transcription, then choose a smaller model when memory matters. Use a multilingual model for translation into English. It does not cover translation into arbitrary target languages, and model selection still requires testing on representative audio.
Technical notes+
pyproject.toml defines the openai-whisper setuptools package, Python 3.8 minimum, runtime dependencies, and the whisper.transcribe:cli console entry point. whisper/audio.py invokes FFmpeg, converts input to mono 16 kHz PCM, and builds 80- or 128-band log-Mel spectrograms. whisper/transcribe.py pads audio, advances through 30-second windows, detects language, retries decoding across temperatures, and optionally derives word timing. whisper/model.py implements the PyTorch encoder-decoder Transformer with cross-attention and key-value caching. whisper/timing.py uses attention weights, median filtering, and dynamic time warping for alignment. whisper/utils.py writes TXT, VTT, SRT, TSV, JSON, and JSONL. whisper/__init__.py downloads checkpoints, verifies SHA-256 hashes, and loads either named models or local checkpoints.
Observed
- License
- MIT for both code and model weights.
- Primary language
- Python, using PyTorch for model definition and inference.
- Packaging
- Installable as the openai-whisper package through pip or directly from GitHub, using setuptools.
- Interfaces
- Provides a command-line program and an importable Python library.
- Python support
- Package metadata requires Python 3.8 or newer and declares Python 3 only.
- External runtime requirement
- FFmpeg must be installed and available on the system path for audio decoding.
- Output formats
- Supports TXT, VTT, SRT, TSV, JSON, and JSONL writers.
Read from README.md, pyproject.toml, requirements.txt, whisper/audio.py, whisper/model.py, whisper/utils.py, whisper/timing.py, whisper/version.py, whisper/__init__.py, whisper/__main__.py, whisper/decoding.py, whisper/tokenizer.py, whisper/transcribe.py.
What it can do
Transcribe speech to text
Audio file with spoken content → Text transcription
Recognize speech in multiple languages
Audio file in any supported language → Text transcription in the source language
Transcribe accented speech
Audio file with non-native or regional accents → Accurate text transcription
Convert technical audio to text
Audio containing technical jargon or specialized terminology → Text with correctly recognized technical terms
Process multilingual audio content
Audio file containing multiple languages → Text transcription preserving language switches
Intel on Whisper
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