openai-whisper
Robust Speech Recognition via Large-Scale Weak Supervision
Decision gist · record as of 2026-08-14
Yes, if you have the system dependencies and can tolerate high install friction. Whisper is actively maintained, permissively licensed, widely used (top 5000 packages), and has no known vulnerabilities. The heavy numerical dependencies (torch, numba, triton) are unavoidable for the task. Install only if you need multilingual speech recognition or translation; for English-only transcription, smaller or specialized models may be more practical.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires ffmpeg installed on your system (apt/pacman/brew/choco/scoop).
- May also require Rust if tiktoken has no pre-built wheel for your platform.
- High install friction: requires torch, numba, triton, and other heavy numerical dependencies.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
last release 2025-06-26 (414 days) · last repo commit 2026-07-28 · 107,269 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,241,268 downloads/mo, #2,355 on PyPI
Alternatives
Verify before relying
pip install openai-whisper
import whisper
model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")
print(result["text"])- Whether pre-built tiktoken wheels cover common platforms or if Rust compilation is frequently needed
- Real-world transcription speed and accuracy on languages beyond English
- Memory and compute requirements for models beyond the documented VRAM estimates
What it is and what it does
Whisper is OpenAI's general-purpose speech recognition model that transcribes, translates, and identifies languages in audio. It uses a Transformer sequence-to-sequence architecture trained on diverse multilingual audio data, allowing a single model to handle multiple speech-processing tasks that traditionally required separate pipeline stages. The model comes in six sizes (tiny, base, small, medium, large, turbo) with English-only and multilingual variants, offering speed-accuracy tradeoffs from ~1 GB to ~10 GB VRAM.
You can use it via command-line (e.g., `whisper audio.mp3 --model turbo`) or Python API. It processes audio in 30-second sliding windows and supports language specification and translation tasks. Installation requires torch, numba, triton, and other heavy numerical libraries, plus ffmpeg on your system. The package is actively maintained and has no known vulnerabilities.
Use it for
- Transcribe English audio files quickly using the turbo model for real-time or batch processing
- Translate non-English speech to English by specifying language and task parameters
- Identify the spoken language in an audio file before further processing
- Build a speech-to-text pipeline that handles multiple languages with a single model
- Process audio in Python with fine-grained control via lower-level APIs like detect_language() and decode()
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have the system dependencies and can tolerate high install friction.
Whisper is actively maintained, permissively licensed, widely used (top 5000 packages), and has no known vulnerabilities. The heavy numerical dependencies (torch, numba, triton) are unavoidable for the task. Install only if you need multilingual speech recognition or translation; for English-only transcription, smaller or specialized models may be more practical.
Install
openai-whisper on PyPI
Before you install
High install friction: requires torch, numba, triton, and other heavy numerical dependencies. Also requires ffmpeg as a system dependency and may need Rust installed if tiktoken lacks a pre-built wheel for your platform. Maintenance is active with recent commits.
Requires ffmpeg installed on your system (apt/pacman/brew/choco/scoop). May also require Rust if tiktoken has no pre-built wheel for your platform.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
Quickstart
pip install openai-whisper
import whisper
model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")
print(result["text"])Verify before relying
- Whether pre-built tiktoken wheels cover common platforms or if Rust compilation is frequently needed
- Real-world transcription speed and accuracy on languages beyond English
- Memory and compute requirements for models beyond the documented VRAM estimates
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | High. Source build required |
| Runtime dependencies | 7 packagesmore-itertoolsnumbanumpytiktokentorchtqdmtriton |
| Maintenance | Actively maintained 414 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,241,268 / month, #2,355 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: openai_whisper-20250625.tar.gz
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See also faster-whisper · lhotse · mlx-whisper · SpeechRecognition · whisper-normalizer · vosk · whisper-timestamped · whisperx · pywhispercpp · omnivoice