Package: openai-whisper
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.ApproachA 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.SetupWe used Python 3.9.9 andPyTorch1.10.1 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 notablyOpenAI's tiktokenfor 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-whisperAlternatively, 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.gitTo 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.gitIt also requires the command-line toolffmpegto be installed on your system, which is available from most package managers:# on Ubuntu or Debiansudoaptupdate&&sudoaptinstallffmpeg# on Arch Linuxsudopacman-Sffmpeg# on MacOS using Homebrew (https://brew.sh/)brewinstallffmpeg# on Windows using Chocolatey (https://chocolatey.org/)chocoinstallffmpeg# on Windows using Scoop (https://scoop.sh/)scoopinstallffmpegYou may needrustinstalled as well, in casetiktokendoes not provide a pre-built wheel for your platform. If you see installation errors during thepip installcommand above, please follow theGetting started pageto install Rust development environment. Additionally, you may need to configure thePATHenvironment variable, e.g.export PATH="$HOME/.cargo/bin:$PATH". If the installation fails withNo module named 'setuptools_rust', you need to installsetuptools_rust, e.g. by running:pipinstallsetuptools-rustAvailable models and languagesThere 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.SizeParametersEnglish-only modelMultilingual modelRequired VRAMRelative speedtiny39 Mtiny.entiny~1 GB~32xbase74 Mbase.enbase~1 GB~16xsmall244 Msmall.ensmall~2 GB~6xmedium769 Mmedium.enmedium~5 GB~2xlarge1550 MN/Alarge~10 GB1xThe.enmodels for English-only applications tend to perform better, especially for thetiny.enandbase.enmodels. We observed that the difference becomes less significant for thesmall.enandmedium.enmodels.Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown oflarge-v3andlarge-v2models by language, using WERs (word error rates) or CER (character error rates, shown inItalic) 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 ofthe paper, as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.Command-line usageThe following command will transcribe speech in audio files, using themediummodel:whisper audio.flac audio.mp3 audio.wav --model mediumThe default setting (which selects thesmallmodel) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the--languageoption:whisper japanese.wav --language JapaneseAdding--task translatewill translate the speech into English:whisper japanese.wav --language Japanese --task translateRun the following to view all available options:whisper --helpSeetokenizer.pyfor the list of all available languages.Python usageTranscription can also be performed within Python:importwhispermodel=whisper.load_model("base")result=model.transcribe("audio.mp3")print(result["text"])Internally, thetranscribe()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 ofwhisper.detect_language()andwhisper.decode()which provide lower-level access to the model.importwhispermodel=whisper.load_model("base")# load audio and pad/trim it to fit 30 secondsaudio=whisper.load_audio("audio.mp3")audio=whisper.pad_or_trim(audio)# make log-Mel spectrogram and move to the same device as the modelmel=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 audiooptions=whisper.DecodingOptions()result=whisper.decode(model,mel,options)# print the recognized textprint(result.text)More examplesPlease use the🙌 Show and tellcategory 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.LicenseWhisper's code and model weights are released under the MIT License. SeeLICENSEfor further details.