https://github.com/openai/whisper Skip to content Toggle navigation Sign up * Product + Actions Automate any workflow + Packages Host and manage packages + Security Find and fix vulnerabilities + Codespaces Instant dev environments + Copilot Write better code with AI + Code review Manage code changes + Issues Plan and track work + Discussions Collaborate outside of code Explore + All features + Documentation + GitHub Skills + Blog * Solutions For + Enterprise + Teams + Startups + Education By Solution + CI/CD & Automation + DevOps + DevSecOps Resources + Learning Pathways + White papers, Ebooks, Webinars + Customer Stories + Partners * Open Source + GitHub Sponsors Fund open source developers + The ReadME Project GitHub community articles Repositories + Topics + Trending + Collections * Pricing Search or jump to... Search code, repositories, users, issues, pull requests... Search [ ] Clear Search syntax tips Provide feedback We read every piece of feedback, and take your input very seriously. [ ] [ ] Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Name [ ] Query [ ] To see all available qualifiers, see our documentation. Cancel Create saved search Sign in Sign up 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 {{ message }} openai / whisper Public * Notifications * Fork 5.4k * Star 47.5k Robust Speech Recognition via Large-Scale Weak Supervision License MIT license 47.5k stars 5.4k forks Activity Star Notifications * Code * Pull requests 28 * Discussions * Actions * Security * Insights More * Code * Pull requests * Discussions * Actions * Security * Insights openai/whisper This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main Switch branches/tags [ ] Branches Tags Could not load branches Nothing to show {{ refName }} default View all branches Could not load tags Nothing to show {{ refName }} default View all tags Name already in use A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch? Cancel Create 7 branches 9 tags Code * Local * Codespaces * Clone HTTPS GitHub CLI [https://github.com/o] Use Git or checkout with SVN using the web URL. [gh repo clone openai] Work fast with our official CLI. Learn more about the CLI. * Open with GitHub Desktop * Download ZIP Sign In Required Please sign in to use Codespaces. Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Launching Xcode If nothing happens, download Xcode and try again. Launching Visual Studio Code Your codespace will open once ready. There was a problem preparing your codespace, please try again. Latest commit @jongwook jongwook Release 20231106 ... fcfeaf1 Nov 6, 2023 Release 20231106 fcfeaf1 Git stats * 132 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time .github/workflows data notebooks tests whisper .flake8 .gitattributes .gitignore .pre-commit-config.yaml CHANGELOG.md LICENSE MANIFEST.in README.md approach.png language-breakdown.svg model-card.md pyproject.toml requirements.txt setup.py View code [ ] Whisper Approach Setup Available models and languages Command-line usage Python usage More examples License README.md 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 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 five 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; actual speed may vary depending on many factors including the available hardware. Size Parameters English-only Multilingual Required Relative model model VRAM speed tiny 39 M tiny.en tiny ~1 GB ~32x base 74 M base.en base ~1 GB ~16x small 244 M small.en small ~2 GB ~6x medium 769 M medium.en medium ~5 GB ~2x large 1550 M N/A large ~10 GB 1x 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. 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. WER breakdown by language Command-line usage The following command will transcribe speech in audio files, using the medium model: whisper audio.flac audio.mp3 audio.wav --model medium The default setting (which selects the small model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the --language option: whisper japanese.wav --language Japanese Adding --task translate will translate the speech into English: whisper japanese.wav --language Japanese --task translate 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("base") 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("base") # 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).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 License MIT license Activity Stars 47.5k stars Watchers 415 watching Forks 5.4k forks Report repository Releases 9 v20231106 Latest Nov 6, 2023 + 8 releases Contributors 66 * @jongwook * @ryanheise * @petterreinholdtsen * @HennerM * @VulumeCode * @guillaumekln * @vickianand * @EliEron * @fcakyon * @jumon * @tomstuart + 55 contributors Languages * Python 100.0% Footer (c) 2023 GitHub, Inc. Footer navigation * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time.