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Robust Speech Recognition via Large-Scale Weak Supervision


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# Whisper

[Permalink: Whisper](#whisper)

[\[Blog\]](https://openai.com/blog/whisper) [\[Paper\]](https://arxiv.org/abs/2212.04356) [\[Model card\]](https://github.com/openai/whisper/blob/main/model-card.md) [\[Colab example\]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)

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

[Permalink: Approach](#approach)

[![Approach](https://raw.githubusercontent.com/openai/whisper/main/approach.png)](https://raw.githubusercontent.com/openai/whisper/main/approach.png)

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

[Permalink: Setup](#setup)

We 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:

```
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`](https://ffmpeg.org/) 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`](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:

```
pip install setuptools-rust
```

## Available models and languages

[Permalink: Available models and languages](#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.

SizeParametersEnglish-only modelMultilingual modelRequired VRAMRelative speedtiny39 M`tiny.en``tiny`~1 GB~32xbase74 M`base.en``base`~1 GB~16xsmall244 M`small.en``small`~2 GB~6xmedium769 M`medium.en``medium`~5 GB~2xlarge1550 MN/A`large`~10 GB1x

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](https://arxiv.org/abs/2212.04356), as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.

[![WER breakdown by language](https://private-user-images.githubusercontent.com/266841/280740425-f4619d66-1058-4005-8f67-a9d811b77c62.svg?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjYyNzI3ODcsIm5iZiI6MTcyNjI3MjQ4NywicGF0aCI6Ii8yNjY4NDEvMjgwNzQwNDI1LWY0NjE5ZDY2LTEwNTgtNDAwNS04ZjY3LWE5ZDgxMWI3N2M2Mi5zdmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwOTE0JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDkxNFQwMDA4MDdaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1kNWNkOTQ3ODUzN2U2NTAxNTZiNzQyMWZiYjY4NTBlYjg2YjI5NDI4MmNiYjZmNzQwNzAyNTkyMWIzNjhlMjJiJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.Z4Qto0OO8IViN1a3M5TOq5hnU2Q1_0TzMqRLDMXWsrA)](https://private-user-images.githubusercontent.com/266841/280740425-f4619d66-1058-4005-8f67-a9d811b77c62.svg?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjYyNzI3ODcsIm5iZiI6MTcyNjI3MjQ4NywicGF0aCI6Ii8yNjY4NDEvMjgwNzQwNDI1LWY0NjE5ZDY2LTEwNTgtNDAwNS04ZjY3LWE5ZDgxMWI3N2M2Mi5zdmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwOTE0JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDkxNFQwMDA4MDdaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1kNWNkOTQ3ODUzN2U2NTAxNTZiNzQyMWZiYjY4NTBlYjg2YjI5NDI4MmNiYjZmNzQwNzAyNTkyMWIzNjhlMjJiJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.Z4Qto0OO8IViN1a3M5TOq5hnU2Q1_0TzMqRLDMXWsrA)

## Command-line usage

[Permalink: Command-line usage](#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](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py) for the list of all available languages.

## Python usage

[Permalink: Python usage](#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

[Permalink: More examples](#more-examples)

Please 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.

## License

[Permalink: License](#license)

Whisper's code and model weights are released under the MIT License. See [LICENSE](https://github.com/openai/whisper/blob/main/LICENSE) for further details.

## About

Robust Speech Recognition via Large-Scale Weak Supervision


### Resources

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### License

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### Stars

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## [Releases\  10](/openai/whisper/releases)

[v20231117\
Latest\
\
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[\+ 9 releases](/openai/whisper/releases)

## [Contributors\  69](/openai/whisper/graphs/contributors)

- [![@jongwook](https://avatars.githubusercontent.com/u/266841?s=64&v=4)](https://github.com/jongwook)
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[\+ 55 contributors](/openai/whisper/graphs/contributors)

## Languages

- [Python100.0%](/openai/whisper/search?l=python)

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