Try our Chrome extension
Easily add the current web-page from your browser directly into your changedetection.io tool, more great features coming soon!
Chrome Webstore
Changedetection.io needs your support!
You can help us by supporting changedetection.io on these platforms;
- Rate us at AlternativeTo.net
- Star us on GitHub
- Follow us at Twitter/X
- G2 Software reviews
- Check us out on LinkedIn
- And tell your friends and colleagues :)
The more popular changedetection.io is, the more time we can dedicate to adding amazing features!
Many thanks :)
changedetection.io team
Not yet seconds ago
False
Not yet seconds ago
Triggered text Ignored text Blocked text
18 hours ago
Skip to content
Navigation Menu
Sign in Appearance settings
* Platform
+ AI CODE CREATION
o GitHub Copilot Write better code with AI
o GitHub Copilot app Direct agents from issue to merge
o MCP Registry Integrate external tools
+ DEVELOPER WORKFLOWS
o Actions Automate any workflow
o Codespaces Instant dev environments
o Issues Plan and track work
o Code Review Manage code changes
o Code Quality Enforce quality at merge
+ APPLICATION SECURITY
o GitHub Advanced Security Find and fix vulnerabilities
o Code security Secure your code as you build
o Secret protection Stop leaks before they start
+ EXPLORE
o Why GitHub
o Documentation
o Blog
o Changelog
o Marketplace
View all features
* Solutions
+ BY COMPANY SIZE
o Enterprises
o Small and medium teams
o Startups
o Nonprofits
+ BY USE CASE
o App Modernization
o DevSecOps
o DevOps
o CI/CD
o View all use cases
+ BY INDUSTRY
o Healthcare
o Financial services
o Manufacturing
o Government
o View all industries
View all solutions
* Resources
+ EXPLORE BY TOPIC
o AI
o Software Development
o DevOps
o Security
o View all topics
+ EXPLORE BY TYPE
o Customer stories
o Events & webinars
o Ebooks & reports
o Business insights
o GitHub Skills
+ SUPPORT & SERVICES
o Documentation
o Customer support
o Community forum
o Trust center
o Partners
View all resources
* Open Source
+ COMMUNITY
o GitHub Sponsors Fund open source developers
+ PROGRAMS
o Security Lab
o Maintainer Community
o Accelerator
o GitHub Stars
o Archive Program
+ REPOSITORIES
o Topics
o Trending
o Collections
* Enterprise
+ ENTERPRISE SOLUTIONS
o Enterprise platform AI-powered developer platform
+ AVAILABLE ADD-ONS
o GitHub Advanced Security Enterprise-grade security features
o Copilot for Business Enterprise-grade AI features
o Premium Support Enterprise-grade 24/7 support
* Pricing
Type / to search
Sign in
Sign up Appearance settings
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
Uh oh!
There was an error while loading. Please reload this page.
openai / whisper Public
* Notifications You must be signed in to change notification settings
* Fork 13k
* Star 107k
* Code
* Pull requests 134
* Discussions
* Actions
* Security and quality 0
* Insights
Additional navigation options
* Code
* Pull requests
* Discussions
* Actions
* Security and quality
* Insights
main
Branches Tags
Go to file
Code
Open more actions menu
Folders and files
Name Name Last commit message Last commit date
Latest commit
History
170 Commits
170 Commits
.github .github
data data
notebooks notebooks
tests tests
whisper whisper
.flake8 .flake8
.gitattributes .gitattributes
.gitignore .gitignore
.pre-commit-config.yaml .pre-commit-config.yaml
CHANGELOG.md CHANGELOG.md
LICENSE LICENSE
MANIFEST.in MANIFEST.in
README.md README.md
approach.png approach.png
language-breakdown.svg language-breakdown.svg
model-card.md model-card.md
pyproject.toml pyproject.toml
requirements.txt requirements.txt
View all files
Repository files navigation
*
* README
* MIT license
More items
Whisper
[Blog] [Paper] [Model card] [Colab example]
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.
Approach
A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
Setup
We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. You can download and install (or update to) the latest release of Whisper with the following command:
pip install -U openai-whisper
Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:
pip install git+https://github.com/openai/whisper.git
To update the package to the latest version of this repository, please run:
pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:
# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg
# on Arch Linux
sudo pacman -S ffmpeg
# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg
# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg
# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg
You may need rust installed as well, in case tiktoken does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH". If the installation fails with No module named 'setuptools_rust', you need to install setuptools_rust, e.g. by running:
pip install setuptools-rust
Available models and languages
There are six model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model. The relative speeds below are measured by transcribing English speech on a A100, and the real-world speed may vary significantly depending on many factors including the language, the speaking speed, and the available hardware.
Size Parameters English-only model Multilingual model Required VRAM Relative speed
tiny 39 M tiny.en tiny ~1 GB ~10x
base 74 M base.en base ~1 GB ~7x
small 244 M small.en small ~2 GB ~4x
medium 769 M medium.en medium ~5 GB ~2x
large 1550 M N/A large ~10 GB 1x
turbo 809 M N/A turbo ~6 GB ~8x
The .en models for English-only applications tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models. Additionally, the turbo model is an optimized version of large-v3 that offers faster transcription speed with a minimal degradation in accuracy.
Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown of large-v3 and large-v2 models by language, using WERs (word error rates) or CER (character error rates, shown in Italic) evaluated on the Common Voice 15 and Fleurs datasets. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of the paper, as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.
Command-line usage
The following command will transcribe speech in audio files, using the turbo model:
whisper audio.flac audio.mp3 audio.wav --model turbo
The default setting (which selects the turbo model) works well for transcribing English. However, the turbo model is not trained for translation tasks. If you need to translate non-English speech into English, use one of the multilingual models (tiny, base, small, medium, large) instead of turbo.
For example, to transcribe an audio file containing non-English speech, you can specify the language:
whisper japanese.wav --language Japanese
To translate speech into English, use:
whisper japanese.wav --model medium --language Japanese --task translate
Note: The turbo model will return the original language even if --task translate is specified. Use medium or large for the best translation results.
Run the following to view all available options:
whisper --help
See tokenizer.py for the list of all available languages.
Python usage
Transcription can also be performed within Python:
import whisper
model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")
print(result["text"])
Internally, the transcribe() method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.
Below is an example usage of whisper.detect_language() and whisper.decode() which provide lower-level access to the model.
import whisper
model = whisper.load_model("turbo")
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: { max ( probs , key = probs . get ) } ")
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)
More examples
Please use the 🙌 Show and tell category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc.
License
Whisper's code and model weights are released under the MIT License. See LICENSE for further details.
About
Robust Speech Recognition via Large-Scale Weak Supervision
Resources
Readme
MIT license
Activity
Custom properties
Stars
107.0k stars
Watchers
758 watching
Forks
13.0k forks
Report repository
Releases
Used by
Contributors
Languages
Footer
© 2026 GitHub, Inc.
Footer navigation
* Terms
* Privacy
* Security
* Status
* Community
* Docs
* Contact
* Manage cookies
* Do not share my personal information
You can’t perform that action at this time.
For now, Differences are performed on text, not graphically, only the latest screenshot is available.
Screenshot requires a Content Fetcher ( Sockpuppetbrowser, selenium, etc ) that supports screenshots.