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README.md
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README.md
@ -1,17 +1,17 @@
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# Audio/Video Transcription Web App
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# YouLama
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A web application for transcribing audio and video files using faster-whisper, with support for YouTube videos and optional summarization using Ollama.
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A powerful web application for transcribing and summarizing YouTube videos and local media files using faster-whisper and Ollama.
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## Features
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- Transcribe local audio/video files
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- Process YouTube videos (with subtitle extraction when available)
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- Automatic language detection
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- Multiple Whisper model options
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- Optional text summarization using Ollama
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- Modern web interface with Gradio
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- Docker support with CUDA
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- Configurable settings via config.ini
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- 🎥 YouTube video transcription with subtitle extraction
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- 🎙️ Local audio/video file transcription
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- 🤖 Automatic language detection
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- 📝 Multiple Whisper model options
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- 📚 AI-powered text summarization using Ollama
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- 🎨 Modern web interface with Gradio
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- 🐳 Docker support with CUDA
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- ⚙️ Configurable settings via config.ini
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## Requirements
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@ -25,7 +25,7 @@ A web application for transcribing audio and video files using faster-whisper, w
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1. Clone the repository:
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```bash
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git clone <repository-url>
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cd whisperapp
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cd youlama
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```
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2. Install NVIDIA Container Toolkit (if not already installed):
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@ -64,7 +64,7 @@ cp .env.example .env
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ollama serve
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```
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2. Build and start the Whisper app container:
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2. Build and start the YouLama container:
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```bash
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docker-compose up --build
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```
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@ -116,25 +116,25 @@ summarize_prompt = Please provide a comprehensive yet concise summary of the fol
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## Features in Detail
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### Local File Transcription
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- Supports various audio and video formats
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- Automatic language detection
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- Multiple Whisper model options
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- Optional summarization with Ollama
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### YouTube Video Processing
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- Supports youtube.com, youtu.be, and invidious URLs
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- Automatically extracts subtitles if available
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- Falls back to transcription if no subtitles found
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- Optional summarization with Ollama
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- Optional AI-powered summarization with Ollama
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### Summarization
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### Local File Transcription
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- Supports various audio and video formats
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- Automatic language detection
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- Multiple Whisper model options
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- Optional AI-powered summarization with Ollama
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### AI Summarization
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- Uses locally running Ollama for text summarization
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- Configurable model selection
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- Customizable prompt
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- Available for both local files and YouTube videos
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## Notes
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## Tips
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- For better accuracy, use larger models (medium, large)
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- Processing time increases with model size
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26
app.py
26
app.py
@ -238,10 +238,8 @@ def process_youtube_url(
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def create_interface():
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"""Create and return the Gradio interface."""
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with gr.Blocks(theme=gr.themes.Soft()) as app:
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gr.Markdown("# 🎙️ Audio/Video Transcription with Whisper")
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gr.Markdown(
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"### A powerful tool for transcribing and summarizing audio/video content"
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)
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gr.Markdown("# 🎥 YouLama")
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gr.Markdown("### AI-powered YouTube video transcription and summarization")
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with gr.Tabs() as tabs:
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with gr.TabItem("YouTube"):
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@ -252,7 +250,7 @@ def create_interface():
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- Supports youtube.com, youtu.be, and invidious URLs
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- Automatically extracts subtitles if available
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- Falls back to transcription if no subtitles found
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- Optional summarization with Ollama
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- Optional AI-powered summarization with Ollama
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"""
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)
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@ -275,7 +273,7 @@ def create_interface():
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)
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with gr.Group():
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yt_summarize_checkbox = gr.Checkbox(
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label="Generate Summary",
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label="Generate AI Summary",
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value=False,
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interactive=OLLAMA_AVAILABLE,
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)
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@ -305,7 +303,7 @@ def create_interface():
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with gr.Column():
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# YouTube output components
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yt_output_text = gr.Textbox(
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label="Result", lines=10, max_lines=20
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label="Transcription", lines=10, max_lines=20
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)
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yt_detected_language = gr.Textbox(
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label="Detected Language", interactive=False
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# Add summary text box below the main output
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if OLLAMA_AVAILABLE:
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yt_summary_text = gr.Textbox(
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label="Summary", lines=5, max_lines=10, value=""
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label="AI Summary", lines=5, max_lines=10, value=""
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)
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# Set up the event handler
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status = "Transcribing video..."
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if summarize and summary:
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status = "Generating summary..."
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status = "Generating AI summary..."
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return (
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text,
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@ -381,7 +379,7 @@ def create_interface():
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Upload an audio or video file to transcribe it using Whisper.
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- Supports various audio and video formats
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- Automatic language detection
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- Optional summarization with Ollama
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- Optional AI-powered summarization with Ollama
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"""
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)
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@ -403,7 +401,7 @@ def create_interface():
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)
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with gr.Group():
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summarize_checkbox = gr.Checkbox(
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label="Generate Summary",
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label="Generate AI Summary",
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value=False,
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interactive=OLLAMA_AVAILABLE,
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)
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@ -440,7 +438,7 @@ def create_interface():
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)
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if OLLAMA_AVAILABLE:
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summary_text = gr.Textbox(
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label="Summary", lines=5, max_lines=10, value=""
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label="AI Summary", lines=5, max_lines=10, value=""
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)
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# Set up the event handler
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@ -467,7 +465,7 @@ def create_interface():
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full_text = " ".join([segment.text for segment in segments])
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if summarize and OLLAMA_AVAILABLE:
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status = "Generating summary..."
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status = "Generating AI summary..."
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summary = ollama.summarize(full_text, ollama_model)
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return (
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full_text,
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- Maximum audio duration is {MAX_DURATION // 60} minutes
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- YouTube videos will first try to use available subtitles
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- If no subtitles are available, the video will be transcribed
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{"- Ollama summarization is available for both local files and YouTube videos" if OLLAMA_AVAILABLE else "- Ollama summarization is currently unavailable"}
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{"- AI-powered summarization is available for both local files and YouTube videos" if OLLAMA_AVAILABLE else "- AI-powered summarization is currently unavailable"}
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### Status:
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- Device: {DEVICE}
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@ -1,7 +1,7 @@
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version: '3.8'
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services:
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whisperapp:
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youlama:
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build: .
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ports:
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- "7860:7860"
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