{"id":13405605,"url":"https://github.com/openai/whisper","last_synced_at":"2025-05-12T17:39:21.680Z","repository":{"id":59843037,"uuid":"537603333","full_name":"openai/whisper","owner":"openai","description":"Robust Speech Recognition via Large-Scale Weak Supervision","archived":false,"fork":false,"pushed_at":"2025-01-04T20:56:17.000Z","size":4095,"stargazers_count":81147,"open_issues_count":97,"forks_count":9753,"subscribers_count":645,"default_branch":"main","last_synced_at":"2025-05-05T15:08:33.563Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/openai.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-09-16T20:02:54.000Z","updated_at":"2025-05-05T14:54:39.000Z","dependencies_parsed_at":"2023-11-06T10:44:08.332Z","dependency_job_id":"d3a968b1-1f8e-47d0-8002-775e6116d232","html_url":"https://github.com/openai/whisper","commit_stats":{"total_commits":99,"total_committers":45,"mean_commits":2.2,"dds":0.6666666666666667,"last_synced_commit":"6dea21fd7f7253bfe450f1e2512a0fe47ee2d258"},"previous_names":[],"tags_count":12,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openai%2Fwhisper","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openai%2Fwhisper/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openai%2Fwhisper/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openai%2Fwhisper/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/openai","download_url":"https://codeload.github.com/openai/whisper/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253789287,"owners_count":21964649,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-30T19:02:06.428Z","updated_at":"2025-05-12T17:39:21.653Z","avatar_url":"https://github.com/openai.png","language":"Python","readme":"# Whisper\n\n[[Blog]](https://openai.com/blog/whisper)\n[[Paper]](https://arxiv.org/abs/2212.04356)\n[[Model card]](https://github.com/openai/whisper/blob/main/model-card.md)\n[[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)\n\nWhisper 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.\n\n\n## Approach\n\n![Approach](https://raw.githubusercontent.com/openai/whisper/main/approach.png)\n\nA 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.\n\n\n## Setup\n\nWe used Python 3.9.9 and [PyTorch](https://pytorch.org/) 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](https://github.com/openai/tiktoken) for their fast tokenizer implementation. You can download and install (or update to) the latest release of Whisper with the following command:\n\n    pip install -U openai-whisper\n\nAlternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:\n\n    pip install git+https://github.com/openai/whisper.git \n\nTo update the package to the latest version of this repository, please run:\n\n    pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git\n\nIt also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers:\n\n```bash\n# on Ubuntu or Debian\nsudo apt update \u0026\u0026 sudo apt install ffmpeg\n\n# on Arch Linux\nsudo pacman -S ffmpeg\n\n# on MacOS using Homebrew (https://brew.sh/)\nbrew install ffmpeg\n\n# on Windows using Chocolatey (https://chocolatey.org/)\nchoco install ffmpeg\n\n# on Windows using Scoop (https://scoop.sh/)\nscoop install ffmpeg\n```\n\nYou may need [`rust`](http://rust-lang.org) installed as well, in case [tiktoken](https://github.com/openai/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](https://www.rust-lang.org/learn/get-started) 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:\n\n```bash\npip install setuptools-rust\n```\n\n\n## Available models and languages\n\nThere are six model sizes, four with English-only versions, offering speed and accuracy tradeoffs.\nBelow are the names of the available models and their approximate memory requirements and inference speed relative to the large model.\nThe 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.\n\n|  Size  | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |\n|:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:|\n|  tiny  |    39 M    |     `tiny.en`      |       `tiny`       |     ~1 GB     |      ~10x      |\n|  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~7x       |\n| small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~4x       |\n| medium |   769 M    |    `medium.en`     |      `medium`      |     ~5 GB     |      ~2x       |\n| large  |   1550 M   |        N/A         |      `large`       |    ~10 GB     |       1x       |\n| turbo  |   809 M    |        N/A         |      `turbo`       |     ~6 GB     |      ~8x       |\n\nThe `.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.\nAdditionally, the `turbo` model is an optimized version of `large-v3` that offers faster transcription speed with a minimal degradation in accuracy.\n\nWhisper'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](https://arxiv.org/abs/2212.04356), as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.\n\n![WER breakdown by language](https://github.com/openai/whisper/assets/266841/f4619d66-1058-4005-8f67-a9d811b77c62)\n\n\n\n## Command-line usage\n\nThe following command will transcribe speech in audio files, using the `turbo` model:\n\n    whisper audio.flac audio.mp3 audio.wav --model turbo\n\nThe default setting (which selects the `turbo` model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:\n\n    whisper japanese.wav --language Japanese\n\nAdding `--task translate` will translate the speech into English:\n\n    whisper japanese.wav --language Japanese --task translate\n\nRun the following to view all available options:\n\n    whisper --help\n\nSee [tokenizer.py](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py) for the list of all available languages.\n\n\n## Python usage\n\nTranscription can also be performed within Python: \n\n```python\nimport whisper\n\nmodel = whisper.load_model(\"turbo\")\nresult = model.transcribe(\"audio.mp3\")\nprint(result[\"text\"])\n```\n\nInternally, 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.\n\nBelow is an example usage of `whisper.detect_language()` and `whisper.decode()` which provide lower-level access to the model.\n\n```python\nimport whisper\n\nmodel = whisper.load_model(\"turbo\")\n\n# load audio and pad/trim it to fit 30 seconds\naudio = whisper.load_audio(\"audio.mp3\")\naudio = whisper.pad_or_trim(audio)\n\n# make log-Mel spectrogram and move to the same device as the model\nmel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)\n\n# detect the spoken language\n_, probs = model.detect_language(mel)\nprint(f\"Detected language: {max(probs, key=probs.get)}\")\n\n# decode the audio\noptions = whisper.DecodingOptions()\nresult = whisper.decode(model, mel, options)\n\n# print the recognized text\nprint(result.text)\n```\n\n## More examples\n\nPlease use the [🙌 Show and tell](https://github.com/openai/whisper/discussions/categories/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.\n\n\n## License\n\nWhisper's code and model weights are released under the MIT License. See [LICENSE](https://github.com/openai/whisper/blob/main/LICENSE) for further details.\n","funding_links":[],"categories":["Python","Uncategorized","Linksammlung","Speech to Text","Official","Jupyter Notebook","Model","精选文章","Others","General Resources","Audio Foundation Model","\u003cspan id=\"speech\"\u003eSpeech\u003c/span\u003e","Audio \u0026 Speech","App","Libraries","Projects","HarmonyOS","语音识别","Tools \u0026 Frameworks","Summary","[:robot: machine-learning]([robot-machine-learning)](\u003chttps://github.com/stars/ketsapiwiq/lists/robot-machine-learning\u003e))","Tools for LMS Content Creation","TTS","Whisper","Advanced Topics","Getting Started","🛠️ Tools \u0026 Frameworks","others","Speech Processing","Repos","🧠 深度学习","Projekte","🎙 Voice \u0026 Audio Tools","🧠 AI Applications \u0026 Platforms","Developer Resources","Interactive storytelling","Frameworks \u0026 Libraries","Models","Voice and Multimodal Agents","🤖 AI \u0026 LLM (人工智能与大模型)","🎵 AI Music \u0026 Audio","AI Caption \u0026 Subtitle Tools"],"sub_categories":["Uncategorized","Aufnahme und laufende Podcast-Produktion","Imgur","Audio Foundation Model","语音识别-生成字幕","\u003cspan id=\"tool\"\u003eLLM (LLM \u0026 Tool)\u003c/span\u003e","Python","Windows Manager","网络服务_其他","Open-source projects","Voice \u0026 Multimodal","Speech-to-Text","关键技术方向","🗣️ Voice","Tools","Speech To Text","Audio and video","Audio \u0026 Speech","Audio Foundation Models","Codex Resources","Audio Processing","Open-Source Transcription"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenai%2Fwhisper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenai%2Fwhisper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenai%2Fwhisper/lists"}