Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/axinc-ai/whisper-export

openvino version of openai/whisper
https://github.com/axinc-ai/whisper-export

Last synced: 2 months ago
JSON representation

openvino version of openai/whisper

Awesome Lists containing this project

README 

        
# Whisper ONNX Export Script



## ONNX Export



This repository based on [whisper.openvino](https://github.com/zhuzilin/whisper-openvino), but

OpenVinoAudioEncoder and OpenVinoTextDecoder were replaced by official AudioEncoder and TextDecoder for ONNX export.



The following command will onnx export:



```

python3 cli.py audio.wav --model medium --export_encoder

python3 cli.py audio.wav --model medium --export_decoder

```



You can also read weights saved_state_dicted from the original whisper.



```

python3 cli.py audio.wav --model medium --export_decoder --fine_tuning model.pth

```



The decoder fixes the size of kv_cache to avoid re-allocating tensors for each inference.



## Requirements



- windows or macOS or Linux

- torch 2.0

- onnx 1.13.1



# Whisper Original information



[[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://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.



## 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:



    python3 cli.py audio.wav --model medium



    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)

```



## License



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