phi-4 multimodal demo added (NVIDIA#276)

* phi-4 multimodal demo added

* Update Phi-4 multimodal podcast assistant notebook

* Update repository URL in ai-podcast-assistant README

* Remove .DS_Store file

* Added instructions to setup API key

* removed outputs

Author: jayrodge

community/README.md

@@ -68,6 +68,10 @@ Community examples are sample code and deployments for RAG pipelines that are no
 
   This tool demonstrates how to utilize a user-friendly interface to interact with NVIDIA NIMs, including those available in the API catalog, self-deployed NIM endpoints, and NIMs hosted on Hugging Face. It also provides settings to integrate RAG pipelines with either local and temporary vector stores or self-hosted search engines. Developers can use this tool to design system prompts, few-shot prompts, and configure LLM settings.
 
-  * [Chatbot with RAG and Glean](./chat-and-rag-glean/)
+* [Chatbot with RAG and Glean](./chat-and-rag-glean/)
 
   This tool shows how to build a chat interface that uses NVIDIA NIMs along with the Glean Search API to enable internal knowledge base search, chat, and retrieval. 
+
+* [AI Podcast Assistant](./ai-podcast-assistant/)
+
+  This example demonstrates a comprehensive workflow for processing podcast audio using the Phi-4-Multimodal LLM through NVIDIA NIM Microservices. It includes functionality for generating detailed notes from audio content, creating concise summaries, and translating both transcriptions and summaries into different languages. The implementation handles long audio files by automatically chunking them for efficient processing and preserves formatting during translation.

community/ai-podcast-assistant/README.md

@@ -0,0 +1,92 @@
+# AI Podcast Assistant
+
+A comprehensive toolkit for generating detailed notes, summary, and translation of podcast content using the Phi-4-Multimodal LLM with NVIDIA NIM Microservices.
+
+## Overview
+
+This repository contains a Jupyter notebook that demonstrates a complete workflow for processing podcast audio:
+
+1. **Notes generation**: Convert spoken content from podcasts into detailed text notes
+2. **Summarization**: Generate concise summaries of the transcribed content
+3. **Translation**: Translate both the transcription and summary into different languages
+
+The implementation leverages the powerful Phi-4-Multimodal LLM (5.6B parameters) through NVIDIA's NIM Microservices, enabling efficient processing of long-form audio content.
+
+Learn more about the model [here](https://developer.nvidia.com/blog/latest-multimodal-addition-to-microsoft-phi-slms-trained-on-nvidia-gpus/).
+
+## Features
+
+- **Long Audio Processing**: Automatically chunks long audio files for processing
+- **Detailed Notes Generation**: Creates well-formatted, detailed notes from audio content
+- **Summarization**: Generates concise summaries capturing key points
+- **Translation**: Translates content to multiple languages while preserving formatting
+- **File Export**: Saves results as text files for easy sharing and reference
+
+## Requirements
+
+- Python 3.10+
+- Jupyter Notebook or JupyterLab
+- NVIDIA API Key (see [Installation](#installation) section for setup instructions)
+- Required Python packages:
+  - requests
+  - base64
+  - pydub
+  - Pillow (PIL)
+
+## Installation
+
+1. Clone this repository:
+   ```bash
+   git clone https://github.com/NVIDIA/GenerativeAIExamples.git
+   cd GenerativeAIExamples/community/ai-podcast-assistant
+   ```
+
+2. Set up your NVIDIA API key:
+   - Sign up for [NVIDIA NIM Microservices](https://build.nvidia.com/explore/discover?signin=true)
+   - Generate an [API key](https://build.nvidia.com/microsoft/phi-4-multimodal-instruct?api_key=true)
+   - Replace the placeholder in the notebook with your API key
+
+## Usage
+
+1. Open the Jupyter notebook:
+   ```bash
+   jupyter notebook ai-podcast-assistant-phi4-mulitmodal.ipynb
+   ```
+
+2. Update the `podcast_audio_path` variable with the path to your audio file.
+
+3. Run the notebook cells sequentially to:
+   - Process the audio file
+   - Generate detailed notes
+   - Create a summary
+   - Translate the content (optional)
+   - Save results to text files
+
+## Example Output
+
+The notebook generates:
+
+1. **Detailed Notes**: Bullet-pointed notes capturing the main content of the podcast
+2. **Summary**: A concise paragraph summarizing the key points
+3. **Translation**: The notes and summary translated to your chosen language
+
+All outputs are saved as text files for easy reference and sharing.
+
+## Model Details
+
+The Phi-4-Multimodal LLM used in this project has the following specifications:
+- **Parameters**: 5.6B
+- **Inputs**: Text, Image, Audio
+- **Context Length**: 128K tokens
+- **Training Data**: 5T text tokens, 2.3M speech hours, 1.1T image-text tokens
+- **Supported Languages**: Multilingual text and audio (English, Chinese, German, French, etc.)
+
+
+## Acknowledgments
+
+- Microsoft for providing access to the Phi-4 multimodal
+- NVIDIA for providing access to the NIM microservices preview 
+
+## Contact
+
+For questions, feedback, or collaboration opportunities, please open an issue in this repository.