Skip to content

Commit

Permalink
modify readme
Browse files Browse the repository at this point in the history
  • Loading branch information
@evanarlian
evanarlian committed Oct 18, 2022
1 parent d35c8f5 commit 39a4257
Show file tree
Hide file tree
Showing 2 changed files with 151 additions and 139 deletions.
151 changes: 12 additions & 139 deletions README.md
View file
Original file line number Diff line number Diff line change
@@ -1,139 +1,12 @@
# Whisper

[[Blog]](https://openai.com/blog/whisper)
[[Paper]](https://cdn.openai.com/papers/whisper.pdf)
[[Model card]](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 multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.


## Approach

![Approach](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. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different 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](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. 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

It also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers:

```bash
# 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:https://rust-lang.org) installed as well, in case [tokenizers](https://pypi.org/project/tokenizers/) 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:

```bash
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 relative speed.


| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative 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 |

For English-only applications, the `.en` models 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 WER breakdown by languages of Fleurs dataset, using the `large` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://cdn.openai.com/papers/whisper.pdf).

![WER breakdown by language](language-breakdown.svg)



## 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](whisper/tokenizer.py) for the list of all available languages.


## Python usage

Transcription can also be performed within Python:

```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.

```python
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](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

The code and the model weights of Whisper are released under the MIT License. See [LICENSE](LICENSE) for further details.
# whisper-torchscript
See original [readme](original-readme.md) and [repo](https://github.com/openai/whisper). This repo modifies Whisper a little bit to enable TorchScript.

What's new?
* TorchScript-able model.
* `kv_cache` will be passed normally instead of using hooks.
* Cannot use the existing wrapper library with this new model code.
* Some modules will be duplicated in favor of using less if elses.

Same as before:
* Original checkpoints are still valid.
* Model architecture is the same as before.
139 changes: 139 additions & 0 deletions original-readme.md
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,139 @@
# Whisper

[[Blog]](https://openai.com/blog/whisper)
[[Paper]](https://cdn.openai.com/papers/whisper.pdf)
[[Model card]](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 multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.


## Approach

![Approach](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. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different 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](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. 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

It also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers:

```bash
# 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:https://rust-lang.org) installed as well, in case [tokenizers](https://pypi.org/project/tokenizers/) 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:

```bash
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 relative speed.


| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative 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 |

For English-only applications, the `.en` models 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 WER breakdown by languages of Fleurs dataset, using the `large` model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in [the paper](https://cdn.openai.com/papers/whisper.pdf).

![WER breakdown by language](language-breakdown.svg)



## 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](whisper/tokenizer.py) for the list of all available languages.


## Python usage

Transcription can also be performed within Python:

```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.

```python
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](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

The code and the model weights of Whisper are released under the MIT License. See [LICENSE](LICENSE) for further details.

0 comments on commit 39a4257

Please sign in to comment.