# Open-Lyrics
[](https://pypi.org/project/openlrc/)
[](https://pypi.org/project/openlrc/)
[](https://pepy.tech/project/openlrc)
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 LLM,
e.g. [OpenAI-GPT](https://github.com/openai/openai-python), [Anthropic-Claude](https://github.com/anthropics/anthropic-sdk-python).
#### Key Features:
- Well preprocessed audio to reduce hallucination (Loudness Norm & optional Noise Suppression).
- Context-aware translation to improve translation quality.
Check [prompt](https://github.com/zh-plus/openlrc/blob/master/openlrc/prompter.py) for details.
- Check [here](#how-it-works) for an overview of the architecture.
## New 🚨
- 2024.5.7:
- Add custom endpoint (base_url) support for OpenAI & Anthropic:
```python
lrcer = LRCer(base_url_config={'openai': 'https://api.chatanywhere.tech',
'anthropic': 'https://example/api'})
```
- Generating bilingual subtitles
```python
lrcer.run('./data/test.mp3', target_lang='zh-cn', bilingual_sub=True)
```
- 2024.5.11: Add glossary into prompt, which is confirmed to improve domain specific translation.
Check [here](#glossary) for details.
- 2024.5.17: You can route model to arbitrary Chatbot SDK (either OpenAI or Anthropic) by setting `chatbot_model` to
`provider: model_name` together with base_url_config:
```python
lrcer = LRCer(chatbot_model='openai: claude-3-haiku-20240307',
base_url_config={'openai': 'https://api.g4f.icu/v1/'})
```
- 2024.6.25: Support Gemini as translation engine LLM, try using `gemini-1.5-flash`:
```python
lrcer = LRCer(chatbot_model='gemini-1.5-flash')
```
- 2024.9.10: Now openlrc depends on
a [specific commit](https://github.com/SYSTRAN/faster-whisper/commit/d57c5b40b06e59ec44240d93485a95799548af50) of
faster-whisper, which is not published on PyPI. Install it from source:
```shell
pip install "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/8327d8cc647266ed66f6cd878cf97eccface7351.tar.gz"
```
## 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 LLM API keys, you can either:
- Add your [OpenAI API key](https://platform.openai.com/account/api-keys) to environment variable `OPENAI_API_KEY`.
- Add your [Anthropic API key](https://console.anthropic.com/settings/keys) to environment variable
`ANTHROPIC_API_KEY`.
- Add your [Google API Key](https://aistudio.google.com/app/apikey) to environment variable `GOOGLE_API_KEY`.
3. Install [ffmpeg](https://ffmpeg.org/download.html) and add `bin` directory
to your `PATH`.
4. 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/openlrc
```
5. Install latest [fast-whisper](https://github.com/guillaumekln/faster-whisper) from source:
```shell
pip install "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/8327d8cc647266ed66f6cd878cf97eccface7351.tar.gz"
```
6. Install [PyTorch](https://pytorch.org/get-started/locally/):
```shell
pip install --force-reinstall torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
```
7. Fix the `typing-extensions` issue:
```shell
pip install typing-extensions -U
```
## Usage 🐍
[//]: # (### GUI)
[//]: # ()
[//]: # (> [!NOTE])
[//]: # (> We are migrating the GUI from streamlit to Gradio. The GUI is still under development.)
[//]: # ()
[//]: # (```shell)
[//]: # (openlrc gui)
[//]: # (```)
[//]: # ()
[//]: # ()
### Python code
```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 glossary to improve translation
lrcer = LRCer(glossary='./data/aoe4-glossary.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)
# Change the LLM model for translation
lrcer = LRCer(chatbot_model='claude-3-sonnet-20240229')
lrcer.run('./data/test.mp3', target_lang='zh-cn')
# Clear temp folder after processing done
lrcer.run('./data/test.mp3', target_lang='zh-cn', clear_temp=True)
# Change base_url
lrcer = LRCer(base_url_config={'openai': 'https://api.g4f.icu/v1',
'anthropic': 'https://example/api'})
# Route model to arbitrary Chatbot SDK
lrcer = LRCer(chatbot_model='openai: claude-3-sonnet-20240229',
base_url_config={'openai': 'https://api.g4f.icu/v1/'})
# Bilingual subtitle
lrcer.run('./data/test.mp3', target_lang='zh-cn', bilingual_sub=True)
```
Check more details in [Documentation](https://zh-plus.github.io/openlrc/#/).
### Glossary
Add glossary to improve domain specific translation. For example `aoe4-glossary.yaml`:
```json
{
"aoe4": "帝国时代4",
"feudal": "封建时代",
"2TC": "双TC",
"English": "英格兰文明",
"scout": "侦察兵"
}
```
```python
lrcer = LRCer(glossary='./data/aoe4-glossary.yaml')
lrcer.run('./data/test.mp3', target_lang='zh-cn')
```
or directly use dictionary to add glossary:
```python
lrcer = LRCer(glossary={"aoe4": "帝国时代4", "feudal": "封建时代"})
lrcer.run('./data/test.mp3', target_lang='zh-cn')
```
## Pricing 💰
*pricing data from [OpenAI](https://openai.com/pricing)
and [Anthropic](https://docs.anthropic.com/claude/docs/models-overview#model-comparison)*
| Model Name | Pricing for 1M Tokens <br/>(Input/Output) (USD) | Cost for 1 Hour Audio <br/>(USD) |
|------------------------------|-------------------------------------------------|----------------------------------|
| `gpt-3.5-turbo` | 0.5, 1.5 | 0.01 |
| `gpt-4o-mini` | 0.5, 1.5 | 0.01 |
| `gpt-4-0125-preview` | 10, 30 | 0.5 |
| `gpt-4-turbo-preview` | 10, 30 | 0.5 |
| `gpt-4o` | 5, 15 | 0.25 |
| `claude-3-haiku-20240307` | 0.25, 1.25 | 0.015 |
| `claude-3-sonnet-20240229` | 3, 15 | 0.2 |
| `claude-3-opus-20240229` | 15, 75 | 1 |
| `claude-3-5-sonnet-20240620` | 3, 15 | 0.2 |
| `gemini-1.5-flash` | 0.175, 2.1 | 0.01 |
| `gemini-1.0-pro` | 0.5, 1.5 | 0.01 |
| `gemini-1.5-pro` | 1.75, 21 | 0.1 |
| `deepseek-chat` | 0.18, 2.2 | 0.01 |
**Note the cost is estimated based on the token count of the input and output text.
The actual cost may vary due to the language and audio speed.**
### Recommended translation model
For english audio, we recommend using `deepseek-chat`, `gpt-4o-mini` or `gemini-1.5-flash`.
For non-english audio, we recommend using `claude-3-5-sonnet-20240620`.
## How it works
To maintain context between translation segments, the process is sequential for each audio file.
[//]: # (## Comparison to https://microsoft.github.io/autogen/docs/notebooks/agentchat_video_transcript_translate_with_whisper/)
## 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.
- [X] [Feature] Multiple translate engine (Anthropic, 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.
- [x] [Feature] Web-based [streamlit](https://streamlit.io/) GUI.
- [ ] Add [fine-tuned whisper-large-v2](https://huggingface.co/models?search=whisper-large-v2) models for common
languages.
- [x] [Feature] Add custom OpenAI & Anthropic endpoint support.
- [ ] [Feature] Add local translation model support (e.g. [SakuraLLM](https://github.com/SakuraLLM/Sakura-13B-Galgame)).
- [ ] [Quality] Construct translation quality benchmark test for each patch.
- [ ] [Quality] Split subtitles using
LLM ([ref](https://github.com/Huanshere/VideoLingo/blob/ff520309e958dd3048586837d09ce37d3e9ebabd/core/prompts_storage.py#L6)).
- [ ] [Quality] Trim extra long subtitle using
LLM ([ref](https://github.com/Huanshere/VideoLingo/blob/ff520309e958dd3048586837d09ce37d3e9ebabd/core/prompts_storage.py#L311)).
- [ ] [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
- https://github.com/streamlit/streamlit
## Star History
[](https://star-history.com/#zh-plus/Open-Lyrics&Date)
## Citation
```
@book{openlrc2024zh,
title = {zh-plus/openlrc},
url = {https://github.com/zh-plus/openlrc},
author = {Hao, Zheng},
date = {2024-09-10},
year = {2024},
month = {9},
day = {10},
}
```
Raw data
{
"_id": null,
"home_page": "https://github.com/zh-plus/Open-Lyrics",
"name": "openlrc",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.9",
"maintainer_email": null,
"keywords": "openai-gpt3, whisper, voice transcribe, lrc",
"author": "Hao Zheng",
"author_email": "zhenghaosustc@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/d7/bb/88b05c86dd06ba59cc9911c758143ed8ca77890e02d012171276f1302bf3/openlrc-1.6.0.tar.gz",
"platform": null,
"description": "# Open-Lyrics\n\n[](https://pypi.org/project/openlrc/)\n[](https://pypi.org/project/openlrc/)\n[](https://pepy.tech/project/openlrc)\n\n\nOpen-Lyrics is a Python library that transcribes voice files using\n[faster-whisper](https://github.com/guillaumekln/faster-whisper), and translates/polishes the resulting text\ninto `.lrc` files in the desired language using LLM,\ne.g. [OpenAI-GPT](https://github.com/openai/openai-python), [Anthropic-Claude](https://github.com/anthropics/anthropic-sdk-python).\n\n#### Key Features:\n\n- Well preprocessed audio to reduce hallucination (Loudness Norm & optional Noise Suppression).\n- Context-aware translation to improve translation quality.\n Check [prompt](https://github.com/zh-plus/openlrc/blob/master/openlrc/prompter.py) for details.\n- Check [here](#how-it-works) for an overview of the architecture.\n\n## New \ud83d\udea8\n\n- 2024.5.7:\n - Add custom endpoint (base_url) support for OpenAI & Anthropic:\n ```python\n lrcer = LRCer(base_url_config={'openai': 'https://api.chatanywhere.tech',\n 'anthropic': 'https://example/api'})\n ```\n - Generating bilingual subtitles\n ```python\n lrcer.run('./data/test.mp3', target_lang='zh-cn', bilingual_sub=True)\n ``` \n- 2024.5.11: Add glossary into prompt, which is confirmed to improve domain specific translation.\n Check [here](#glossary) for details.\n- 2024.5.17: You can route model to arbitrary Chatbot SDK (either OpenAI or Anthropic) by setting `chatbot_model` to\n `provider: model_name` together with base_url_config:\n ```python\n lrcer = LRCer(chatbot_model='openai: claude-3-haiku-20240307',\n base_url_config={'openai': 'https://api.g4f.icu/v1/'})\n ```\n- 2024.6.25: Support Gemini as translation engine LLM, try using `gemini-1.5-flash`:\n ```python\n lrcer = LRCer(chatbot_model='gemini-1.5-flash')\n ```\n- 2024.9.10: Now openlrc depends on\n a [specific commit](https://github.com/SYSTRAN/faster-whisper/commit/d57c5b40b06e59ec44240d93485a95799548af50) of\n faster-whisper, which is not published on PyPI. Install it from source:\n ```shell\n pip install \"faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/8327d8cc647266ed66f6cd878cf97eccface7351.tar.gz\"\n ```\n\n## Installation \u2699\ufe0f\n\n1. Please install CUDA 11.x and [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn) first according\n to https://opennmt.net/CTranslate2/installation.html to enable `faster-whisper`.\n\n `faster-whisper` also needs [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas) installed.\n <details>\n <summary>For Windows Users (click to expand)</summary> \n\n (For Windows Users only) Windows user can Download the libraries from Purfview's repository:\n\n Purfview's [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) provides the required NVIDIA\n libraries for Windows in a [single archive](https://github.com/Purfview/whisper-standalone-win/releases/tag/libs).\n Decompress the archive and place the libraries in a directory included in the `PATH`.\n\n </details>\n\n\n2. Add LLM API keys, you can either:\n - Add your [OpenAI API key](https://platform.openai.com/account/api-keys) to environment variable `OPENAI_API_KEY`.\n - Add your [Anthropic API key](https://console.anthropic.com/settings/keys) to environment variable\n `ANTHROPIC_API_KEY`.\n - Add your [Google API Key](https://aistudio.google.com/app/apikey) to environment variable `GOOGLE_API_KEY`.\n\n3. Install [ffmpeg](https://ffmpeg.org/download.html) and add `bin` directory\n to your `PATH`.\n\n4. This project can be installed from PyPI:\n\n ```shell\n pip install openlrc\n ```\n\n or install directly from GitHub:\n\n ```shell\n pip install git+https://github.com/zh-plus/openlrc\n ```\n\n5. Install latest [fast-whisper](https://github.com/guillaumekln/faster-whisper) from source:\n ```shell\n pip install \"faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/8327d8cc647266ed66f6cd878cf97eccface7351.tar.gz\"\n ```\n\n6. Install [PyTorch](https://pytorch.org/get-started/locally/):\n ```shell\n pip install --force-reinstall torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124\n ```\n\n7. Fix the `typing-extensions` issue:\n ```shell\n pip install typing-extensions -U\n ```\n\n## Usage \ud83d\udc0d\n\n[//]: # (### GUI)\n\n[//]: # ()\n\n[//]: # (> [!NOTE])\n\n[//]: # (> We are migrating the GUI from streamlit to Gradio. The GUI is still under development.)\n\n[//]: # ()\n\n[//]: # (```shell)\n\n[//]: # (openlrc gui)\n\n[//]: # (```)\n\n[//]: # ()\n\n[//]: # ()\n\n### Python code\n\n```python\nfrom openlrc import LRCer\n\nif __name__ == '__main__':\n lrcer = LRCer()\n\n # Single file\n lrcer.run('./data/test.mp3',\n target_lang='zh-cn') # Generate translated ./data/test.lrc with default translate prompt.\n\n # Multiple files\n lrcer.run(['./data/test1.mp3', './data/test2.mp3'], target_lang='zh-cn')\n # Note we run the transcription sequentially, but run the translation concurrently for each file.\n\n # Path can contain video\n lrcer.run(['./data/test_audio.mp3', './data/test_video.mp4'], target_lang='zh-cn')\n # Generate translated ./data/test_audio.lrc and ./data/test_video.srt\n\n # Use glossary to improve translation\n lrcer = LRCer(glossary='./data/aoe4-glossary.yaml')\n\n # To skip translation process\n lrcer.run('./data/test.mp3', target_lang='en', skip_trans=True)\n\n # Change asr_options or vad_options, check openlrc.defaults for details\n vad_options = {\"threshold\": 0.1}\n lrcer = LRCer(vad_options=vad_options)\n lrcer.run('./data/test.mp3', target_lang='zh-cn')\n\n # Enhance the audio using noise suppression (consume more time).\n lrcer.run('./data/test.mp3', target_lang='zh-cn', noise_suppress=True)\n\n # Change the LLM model for translation\n lrcer = LRCer(chatbot_model='claude-3-sonnet-20240229')\n lrcer.run('./data/test.mp3', target_lang='zh-cn')\n\n # Clear temp folder after processing done\n lrcer.run('./data/test.mp3', target_lang='zh-cn', clear_temp=True)\n\n # Change base_url\n lrcer = LRCer(base_url_config={'openai': 'https://api.g4f.icu/v1',\n 'anthropic': 'https://example/api'})\n\n # Route model to arbitrary Chatbot SDK\n lrcer = LRCer(chatbot_model='openai: claude-3-sonnet-20240229',\n base_url_config={'openai': 'https://api.g4f.icu/v1/'})\n\n # Bilingual subtitle\n lrcer.run('./data/test.mp3', target_lang='zh-cn', bilingual_sub=True)\n```\n\nCheck more details in [Documentation](https://zh-plus.github.io/openlrc/#/).\n\n### Glossary\n\nAdd glossary to improve domain specific translation. For example `aoe4-glossary.yaml`:\n\n```json\n{\n \"aoe4\": \"\u5e1d\u56fd\u65f6\u4ee34\",\n \"feudal\": \"\u5c01\u5efa\u65f6\u4ee3\",\n \"2TC\": \"\u53ccTC\",\n \"English\": \"\u82f1\u683c\u5170\u6587\u660e\",\n \"scout\": \"\u4fa6\u5bdf\u5175\"\n}\n```\n\n```python\nlrcer = LRCer(glossary='./data/aoe4-glossary.yaml')\nlrcer.run('./data/test.mp3', target_lang='zh-cn')\n```\n\nor directly use dictionary to add glossary:\n\n```python\nlrcer = LRCer(glossary={\"aoe4\": \"\u5e1d\u56fd\u65f6\u4ee34\", \"feudal\": \"\u5c01\u5efa\u65f6\u4ee3\"})\nlrcer.run('./data/test.mp3', target_lang='zh-cn')\n```\n\n## Pricing \ud83d\udcb0\n\n*pricing data from [OpenAI](https://openai.com/pricing)\nand [Anthropic](https://docs.anthropic.com/claude/docs/models-overview#model-comparison)*\n\n| Model Name | Pricing for 1M Tokens <br/>(Input/Output) (USD) | Cost for 1 Hour Audio <br/>(USD) |\n|------------------------------|-------------------------------------------------|----------------------------------|\n| `gpt-3.5-turbo` | 0.5, 1.5 | 0.01 |\n| `gpt-4o-mini` | 0.5, 1.5 | 0.01 |\n| `gpt-4-0125-preview` | 10, 30 | 0.5 |\n| `gpt-4-turbo-preview` | 10, 30 | 0.5 |\n| `gpt-4o` | 5, 15 | 0.25 |\n| `claude-3-haiku-20240307` | 0.25, 1.25 | 0.015 |\n| `claude-3-sonnet-20240229` | 3, 15 | 0.2 |\n| `claude-3-opus-20240229` | 15, 75 | 1 |\n| `claude-3-5-sonnet-20240620` | 3, 15 | 0.2 |\n| `gemini-1.5-flash` | 0.175, 2.1 | 0.01 |\n| `gemini-1.0-pro` | 0.5, 1.5 | 0.01 |\n| `gemini-1.5-pro` | 1.75, 21 | 0.1 |\n| `deepseek-chat` | 0.18, 2.2 | 0.01 |\n\n**Note the cost is estimated based on the token count of the input and output text.\nThe actual cost may vary due to the language and audio speed.**\n\n### Recommended translation model\n\nFor english audio, we recommend using `deepseek-chat`, `gpt-4o-mini` or `gemini-1.5-flash`.\n\nFor non-english audio, we recommend using `claude-3-5-sonnet-20240620`.\n\n## How it works\n\n\n\nTo maintain context between translation segments, the process is sequential for each audio file.\n\n\n[//]: # (## Comparison to https://microsoft.github.io/autogen/docs/notebooks/agentchat_video_transcript_translate_with_whisper/)\n\n## Todo\n\n- [x] [Efficiency] Batched translate/polish for GPT request (enable contextual ability).\n- [x] [Efficiency] Concurrent support for GPT request.\n- [x] [Translation Quality] Make translate prompt more robust according to https://github.com/openai/openai-cookbook.\n- [x] [Feature] Automatically fix json encoder error using GPT.\n- [x] [Efficiency] Asynchronously perform transcription and translation for multiple audio inputs.\n- [x] [Quality] Improve batched translation/polish prompt according\n to [gpt-subtrans](https://github.com/machinewrapped/gpt-subtrans).\n- [x] [Feature] Input video support.\n- [X] [Feature] Multiple output format support.\n- [x] [Quality] Speech enhancement for input audio.\n- [ ] [Feature] Preprocessor: Voice-music separation.\n- [ ] [Feature] Align ground-truth transcription with audio.\n- [ ] [Quality]\n Use [multilingual language model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models) to assess\n translation quality.\n- [ ] [Efficiency] Add Azure OpenAI Service support.\n- [ ] [Quality] Use [claude](https://www.anthropic.com/index/introducing-claude) for translation.\n- [ ] [Feature] Add local LLM support.\n- [X] [Feature] Multiple translate engine (Anthropic, Microsoft, DeepL, Google, etc.) support.\n- [ ] [**Feature**] Build\n a [electron + fastapi](https://ivanyu2021.hashnode.dev/electron-django-desktop-app-integrate-javascript-and-python)\n GUI for cross-platform application.\n- [x] [Feature] Web-based [streamlit](https://streamlit.io/) GUI.\n- [ ] Add [fine-tuned whisper-large-v2](https://huggingface.co/models?search=whisper-large-v2) models for common\n languages.\n- [x] [Feature] Add custom OpenAI & Anthropic endpoint support.\n- [ ] [Feature] Add local translation model support (e.g. [SakuraLLM](https://github.com/SakuraLLM/Sakura-13B-Galgame)).\n- [ ] [Quality] Construct translation quality benchmark test for each patch.\n- [ ] [Quality] Split subtitles using\n LLM ([ref](https://github.com/Huanshere/VideoLingo/blob/ff520309e958dd3048586837d09ce37d3e9ebabd/core/prompts_storage.py#L6)).\n- [ ] [Quality] Trim extra long subtitle using\n LLM ([ref](https://github.com/Huanshere/VideoLingo/blob/ff520309e958dd3048586837d09ce37d3e9ebabd/core/prompts_storage.py#L311)).\n- [ ] [Others] Add transcribed examples.\n - [ ] Song\n - [ ] Podcast\n - [ ] Audiobook\n\n## Credits\n\n- https://github.com/guillaumekln/faster-whisper\n- https://github.com/m-bain/whisperX\n- https://github.com/openai/openai-python\n- https://github.com/openai/whisper\n- https://github.com/machinewrapped/gpt-subtrans\n- https://github.com/MicrosoftTranslator/Text-Translation-API-V3-Python\n- https://github.com/streamlit/streamlit\n\n## Star History\n\n[](https://star-history.com/#zh-plus/Open-Lyrics&Date)\n\n## Citation\n\n```\n@book{openlrc2024zh,\n\ttitle = {zh-plus/openlrc},\n\turl = {https://github.com/zh-plus/openlrc},\n\tauthor = {Hao, Zheng},\n\tdate = {2024-09-10},\n\tyear = {2024},\n\tmonth = {9},\n\tday = {10},\n}\n```\n",
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