Metadata-Version: 2.1
Name: openlrc
Version: 1.1.0
Summary: Transcribe (whisper) and translate (gpt) voice into LRC file.
Home-page: https://github.com/zh-plus/Open-Lyrics
License: MIT
Keywords: openai-gpt3,whisper,voice transcribe,lrc
Author: Hao Zheng
Author-email: zhenghaosustc@gmail.com
Requires-Python: >=3.8,<4.0
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
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Requires-Dist: torchaudio (>=2.0.0,<3.0.0)
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Requires-Dist: zhconv (>=1.4.3,<2.0.0)
Project-URL: Bug Tracker, https://github.com/zh-plus/Open-Lyrics/issues
Description-Content-Type: text/markdown

# Open-Lyrics

[![PyPI](https://img.shields.io/pypi/v/openlrc)](https://pypi.org/project/openlrc/)
[![PyPI - License](https://img.shields.io/pypi/l/openlrc)](https://pypi.org/project/openlrc/)
[![Downloads](https://static.pepy.tech/badge/openlrc)](https://pepy.tech/project/openlrc)
![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/zh-plus/Open-Lyrics/ci.yml)

Open-Lyrics is a Python library that transcribes voice files using
[faster-whisper](https://github.com/guillaumekln/faster-whisper), and translates/polishes the resulting text
into `.lrc` files in the desired language using [OpenAI-GPT](https://github.com/openai/openai-python).

## Installation

1. Please install CUDA 11.x and [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn) first according to https://opennmt.net/CTranslate2/installation.html to enable `faster-whisper`.   
  
   `faster-whisper` also needs [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas) installed.
   <details>
   <summary>For Windows Users (click to expand)</summary> 
   
   (For Windows Users only) Windows user can Download the libraries from Purfview's repository:

   Purfview's [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) provides the required NVIDIA libraries for Windows in a [single archive](https://github.com/Purfview/whisper-standalone-win/releases/tag/libs). Decompress the archive and place the libraries in a directory included in the `PATH`.

   </details>

  

2. Add your [OpenAI API key](https://platform.openai.com/account/api-keys) to environment variable `OPENAI_API_KEY`.

3. Install [PyTorch](https://pytorch.org/get-started/locally/):
   ```shell
   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   ```

4. Install latest [fast-whisper](https://github.com/guillaumekln/faster-whisper)
   ```shell
   pip install git+https://github.com/guillaumekln/faster-whisper
   ```

5. Install [ffmpeg](https://ffmpeg.org/download.html) and add `bin` directory
   to your `PATH`.

6. This project can be installed from PyPI:

    ```shell
    pip install openlrc
    ```

   or install directly from GitHub:

    ```shell
    pip install git+https://github.com/zh-plus/Open-Lyrics
    ```

## Usage

```python
from openlrc import LRCer

if __name__ == '__main__':
    lrcer = LRCer()

    # Single file
    lrcer.run('./data/test.mp3',
              target_lang='zh-cn')  # Generate translated ./data/test.lrc with default translate prompt.

    # Multiple files
    lrcer.run(['./data/test1.mp3', './data/test2.mp3'], target_lang='zh-cn')
    # Note we run the transcription sequentially, but run the translation concurrently for each file.

    # Path can contain video
    lrcer.run(['./data/test_audio.mp3', './data/test_video.mp4'], target_lang='zh-cn')
    # Generate translated ./data/test_audio.lrc and ./data/test_video.srt

    # Use context.yaml to improve translation
    lrcer.run('./data/test.mp3', target_lang='zh-cn', context_path='./data/context.yaml')

    # To skip translation process
    lrcer.run('./data/test.mp3', target_lang='en', skip_trans=True)

    # Change asr_options or vad_options, check openlrc.defaults for details
    vad_options = {"threshold": 0.1}
    lrcer = LRCer(vad_options=vad_options)
    lrcer.run('./data/test.mp3', target_lang='zh-cn')

    # Enhance the audio using noise suppression (consume more time).
    lrcer.run('./data/test.mp3', target_lang='zh-cn', noise_suppress=True)
```

Check more details in [Documentation](https://zh-plus.github.io/openlrc/#/).

### Context

Utilize the available context to enhance the quality of your translation.
Save them as `context.yaml` in the same directory as your audio file.

> [!NOTE]
> The improvement of translation quality from Context is **NOT** guaranteed.

```yaml
background: "This is a multi-line background.
This is a basic example."
audio_type: Movie
description_map: {
  movie_name1 (without extension): "This
  is a multi-line description for movie1.",
  movie_name2 (without extension): "This
  is a multi-line description for movie2.",
  movie_name3 (without extension): "This is a single-line description for movie 3.",
}
```

## Todo

- [x] [Efficiency] Batched translate/polish for GPT request (enable contextual ability).
- [x] [Efficiency] Concurrent support for GPT request.
- [x] [Translation Quality] Make translate prompt more robust according to https://github.com/openai/openai-cookbook.
- [x] [Feature] Automatically fix json encoder error using GPT.
- [x] [Efficiency] Asynchronously perform transcription and translation for multiple audio inputs.
- [x] [Quality] Improve batched translation/polish prompt according
  to [gpt-subtrans](https://github.com/machinewrapped/gpt-subtrans).
- [x] [Feature] Input video support.
- [X] [Feature] Multiple output format support.
- [x] [Quality] Speech enhancement for input audio.
- [ ] [Feature] Preprocessor: Voice-music separation.
- [ ] [Feature] Align ground-truth transcription with audio.
- [ ] [Quality]
  Use [multilingual language model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models) to assess
  translation quality.
- [ ] [Efficiency] Add Azure OpenAI Service support.
- [ ] [Quality] Use [claude](https://www.anthropic.com/index/introducing-claude) for translation.
- [ ] [Feature] Add local LLM support.
- [ ] [Feature] Multiple translate engine (Microsoft, DeepL, Google, etc.) support.
- [ ] [**Feature**] Build
  a [electron + fastapi](https://ivanyu2021.hashnode.dev/electron-django-desktop-app-integrate-javascript-and-python)
  GUI for cross-platform application.
- [ ] Add [fine-tuned whisper-large-v2](https://huggingface.co/models?search=whisper-large-v2) models for common
  languages.
- [ ] [Others] Add transcribed examples.
    - [ ] Song
    - [ ] Podcast
    - [ ] Audiobook

## Credits

- https://github.com/guillaumekln/faster-whisper
- https://github.com/m-bain/whisperX
- https://github.com/openai/openai-python
- https://github.com/openai/whisper
- https://github.com/machinewrapped/gpt-subtrans
- https://github.com/MicrosoftTranslator/Text-Translation-API-V3-Python

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=zh-plus/Open-Lyrics&type=Date)](https://star-history.com/#zh-plus/Open-Lyrics&Date)

