openlrc 1.0.0

Transcribe (whisper) and translate (gpt) voice into LRC file.

Open-Lyrics

Open-Lyrics is a Python library that transcribes voice files using faster-whisper, and translates/polishes the resulting text into .lrc files in the desired language using OpenAI-GPT.

Installation

1. Please install CUDA 11.x and cuDNN 8 for CUDA 11 first according to https://opennmt.net/CTranslate2/installation.html to enable faster-whisper.

   faster-whisper also needs cuBLAS for CUDA 11 installed.

   For Windows Users (click to expand)

   (For Windows Users only) Windows user can Download the libraries from Purfview's repository:

   Purfview's whisper-standalone-win provides the required NVIDIA libraries for Windows in a single archive. Decompress the archive and place the libraries in a directory included in the PATH.

2. Add your OpenAI API key to environment variable OPENAI_API_KEY.

3. Install PyTorch:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   

4. Install latest fast-whisper

   pip install git+https://github.com/guillaumekln/faster-whisper
   

5. (Optional) If you want to process videos, install ffmpeg and add bin directory to your PATH.

6. This project can be installed from PyPI:

   pip install openlrc
   

   or install directly from GitHub:

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

Usage

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.

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.

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

• [Efficiency] Batched translate/polish for GPT request (enable contextual ability).
• [Efficiency] Concurrent support for GPT request.
• [Translation Quality] Make translate prompt more robust according to https://github.com/openai/openai-cookbook.
• [Feature] Automatically fix json encoder error using GPT.
• [Efficiency] Asynchronously perform transcription and translation for multiple audio inputs.
• [Quality] Improve batched translation/polish prompt according to gpt-subtrans.
• [Feature] Input video support.
• [Feature] Multiple output format support.
• [Quality] Speech enhancement for input audio.
• [Feature] Preprocessor: Voice-music separation.
• [Feature] Align ground-truth transcription with audio.
• [Quality] Use multilingual language model to assess translation quality.
• [Efficiency] Add Azure OpenAI Service support.
• [Quality] Use claude for translation.
• [Feature] Add local LLM support.
• [Feature] Multiple translate engine (Microsoft, DeepL, Google, etc.) support.
• [Feature] Build a electron + fastapi GUI for cross-platform application.
• Add fine-tuned 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

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