[DOCS] adding link blocks, index to md

libraries/video-chunking-utils/docs/user-guide/index.md

@@ -1,5 +1,16 @@
 # A Python Module for Video Chunking Utils
 
+<!--hide_directive
+<div class="component_card_widget">
+  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/video-chunking-utils">
+     GitHub project
+  </a>
+  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-libraries/blob/main/libraries/video-chunking-utils/README.md">
+     Readme
+  </a>
+</div>
+hide_directive-->
+
 ## Introduction
 
 This is a Python module designed for video chunking. It allows users to split video files into smaller, manageable segments. The module is designed to be easily installable via pip and can be used in various applications such as video processing, analysis, and content delivery.
@@ -39,7 +50,7 @@ for i, micro_chunk in enumerate(micro_chunks_list):
 print(f"Total {len(micro_chunks_list)} chunks are generated.")
 ```
 
-### Method:  Pelt Chunking
+### Method: Pelt Chunking
 
 ```python
 from video_chunking import PeltChunking

microservices/audio-analyzer/docs/user-guide/index.md

@@ -0,0 +1,137 @@
+# Audio Analyzer
+
+<!--hide_directive
+<div class="component_card_widget">
+  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/audio-analyzer">
+     GitHub project
+  </a>
+  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/audio-analyzer/README.md">
+     Readme
+  </a>
+</div>
+hide_directive-->
+
+Audio Analyzer microservice is used to generate transcription of audio from video files.
+
+## Overview
+
+The Audio Analyzer microservice provides an automated solution for extracting and transcribing
+audio from video files. Designed for seamless integration into modern AI pipelines this
+microservice enables converting spoken content within videos into accurate, searchable text.
+By leveraging state-of-the-art speech-to-text models, the service supports a wide range of
+audio formats and languages, making it suitable for diverse applications such as video summary,
+media analysis, compliance monitoring, and content indexing.
+
+The microservice operates by first isolating the audio track from the input video file.
+Once extracted, the audio is processed using advanced transcription models to generate a
+time-aligned text transcript. This transcript can be used for downstream tasks such as keyword
+search, sentiment analysis, or integration with other AI-driven analytics.
+
+Key features include robust handling of noisy or low-quality audio, support for batch and
+real-time processing, and easy deployment as a RESTful API. The service is optimized for edge
+and cloud environments, ensuring low latency and scalability. Developers can interact with the
+microservice through simple API endpoints, enabling rapid integration into existing workflows.
+
+By automating the extraction and transcription of audio from video, the Audio Analyzer
+microservice streamlines content analysis, improves accessibility, and unlocks new possibilities
+for leveraging audio data in various video analytics use cases.
+
+### Key Benefits
+
+- **Benefit 1**: Enables multimodal analysis of video data by extracting information from its
+audio track.
+- **Benefit 2**: Seamless integration through RESTful APIs with various video analytics use
+cases that benefit from audio processing.
+- **Benefit 3**: Flexibility to use different ASR models as per use case requirements.
+
+### Features
+
+- **Feature 1**: Extract audio from video files.
+- **Feature 2**: Transcribe speech using Whispercpp (CPU).
+- **Feature 3**: RESTful API with FastAPI.
+- **Feature 4**: Containerization with Docker.
+- **Feature 5**: Automatic model download and conversion on startup.
+- **Feature 6**: Persistent model storage.
+- **Feature 7**: OpenVINO acceleration support for Intel hardware.
+- **Feature 8**: **MinIO integration** for video source and transcript storage.
+
+### Use Cases
+
+Audio Analyzer microservice can be applied to various real-world use cases and scenarios across
+different video analytics use cases cutting across different industry segments. The motivation
+to provide the microservice primarily comes from enhancing the accuracy of the video summary
+pipeline. Here are some examples:
+
+- **Use case 1**: Ego centric videos as captured in industry segments like Safety and Security,
+Body worn camera for example, benefits from additional modality of information that Audio
+transcription provides.
+- **Use case 2**: Videos from class rooms are primarily analysed using their audio content.
+Audio Analyzer microservice helps provide transcription which can be used to chapterize a
+class room session, for example.
+- **Use case 3**: Courtroom or Legal Proceedings with legal hearings or depositions are
+primarily analysed using the spoken word.
+- **Use case 4**: Video podcasts or interview recordings where the value is in the conversation,
+discussions, or interviews, and visuals are secondary.
+- **Use case 5**: Events, like Panel Discussions and Debates, where multiple speakers discuss
+or debate topics, the audio contains the key arguments and insights.
+
+## How It Works
+
+The Audio Analyzer microservice accepts a video file for transcription from either file system
+or minIO storage. Using the configured Whisper model, the transcription is created. The output
+transcription along with the configured metadata is then stored in configured destination
+location. It provides a RESTful API to configure and utilize the capabilities.
+
+## Models supported
+
+The service automatically downloads and manages the required models based on configuration.
+Two types of models are supported:
+
+1. **GGML Models** Primarily used for inference on CPU using whispercpp backend.
+2. **OpenVINO Models** Optimized for GPU inference on Intel GPUs.
+
+Models are downloaded on application startup, converted to OpenVINO format if needed, and
+stored in persistent volumes for reuse. The conversion process includes:
+
+- Downloading the original Hugging Face Whisper model
+- Converting the PyTorch model to OpenVINO format.
+- Storing the encoder and decoder components separately for efficient inference
+
+### Available Whisper Models
+
+The following Whisper model variants are supported by the service (for both GGML and OpenVINO
+formats):
+
+ | Model ID  |  Description       |   Size   |  Languages   |
+ | --------- | ------------------ | -------- | ------------ |
+ | tiny      |  Tiny model        |  ~75M    | Multilingual |
+ | tiny.en   |  Tiny model        |  ~75M    | English-only |
+ | base      |  Base model        |  ~150M   | Multilingual |
+ | base.en   |  Base model        |  ~150M   | English-only |
+ | small     |  Small model       |  ~450M   | Multilingual |
+ | small.en  |  Small model       |  ~450M   | English-only |
+ | medium    |  Medium model      |  ~1.5GB  | Multilingual |
+ | medium.en |  Medium model      |  ~1.5GB  | English-only |
+ | large-v1  |  Large model (v1)  |  ~2.9GB  | Multilingual |
+ | large-v2  |  Large model (v2)  |  ~2.9GB  | Multilingual |
+ | large-v3  |  Large model (v3)  |  ~2.9GB  | Multilingual |
+
+## Supporting Resources
+
+- [Get Started Guide](./get-started)
+- [API Reference](./api-reference)
+- [System Requirements](./system-requirements)
+
+<!--hide_directive
+:::{toctree}
+:hidden:
+
+overview-architecture
+system-requirements
+get-started
+how-to-build-from-source
+api-reference
+release-notes
+
+:::
+hide_directive-->

microservices/dlstreamer-pipeline-server/docs/user-guide/index.md

@@ -0,0 +1,105 @@
+# Deep Learning Streamer Pipeline Server
+
+<!--hide_directive
+<div class="component_card_widget">
+  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/dlstreamer-pipeline-server">
+     GitHub project
+  </a>
+  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-libraries/blob/main/microservices/dlstreamer-pipeline-server/README.md">
+     Readme
+  </a>
+</div>
+hide_directive-->
+
+Deep Learning Streamer Pipeline Server (DL Streamer Pipeline Server) is a Python-based,
+interoperable containerized microservice for easy development and deployment of video analytics
+pipelines.
+
+## Overview
+
+DL Streamer Pipeline Server microservice is built on top of [GStreamer](https://gstreamer.freedesktop.org/documentation/)
+and [Deep Learning Streamer (DL Streamer)](https://github.com/open-edge-platform/dlstreamer/tree/master),
+providing video ingestion and deep learning inferencing functionalities.
+
+Video analytics involves the conversion of video streams into valuable insights through the
+application of video processing, inference, and analytics operations. It finds applications
+in various business sectors including healthcare, retail, entertainment, and industrial domains.
+The algorithms utilized in video analytics are responsible for performing tasks such as object
+detection, classification, identification, counting, and tracking on the input video stream.
+
+## How it Works
+
+![DL Streamer Pipeline Server architecture](./images/dls-pipelineserver-simplified-arch.png)
+
+Here is the high level description of functionality of DL Streamer Pipeline Server module:
+
+- **RESTful Interface**
+
+  Exposes RESTful endpoints to discover, start, stop and customize pipelines in JSON format.
+
+- **DL Streamer Pipeline Server Core**
+
+  Manages and processes the REST requests interfacing with the core DL Streamer Pipeline Server
+  components and Pipeline Server Library.
+
+- **DL Streamer Pipeline Server Configuration handler**
+
+  Reads the contents of a config file and accordingly constructs/starts pipelines. Dynamic
+  configuration change is supported via REST API.
+
+- **GST UDF Loader**
+
+  DL Streamer Pipeline Server provides a [GStreamer plugin](https://gstreamer.freedesktop.org/documentation/plugins_doc.html?gi-language=c) - `udfloader`, which can be used to configure and load arbitrary UDFs. With
+  `udfloader`, DL Streamer Pipeline Server provides an easy way to bring user developed programs
+  and run them as a part of GStreamer pipelines. A User Defined Function (UDF) is a chunk of
+  user code that can transform video frames and/or manipulate metadata. For example, a UDF can
+  act as filter, preprocessor, classifier or a detector. These User Defined Functions can be
+  developed in Python.
+
+- **DL Streamer Pipeline Server Publisher**
+
+  Supports publishing metadata to a file, MQTT/Kafka message brokers and frame along with
+  metadata to a MQTT message broker. It also supports publishing metadata and frame over OPCUA.
+  The frames can also be saved on S3 compliant storage.
+
+- **DL Streamer Pipeline Server Model Update**
+
+  Supports integration with the Model Registry service - [Model Registry](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/model-registry/index.html) for model download, deployment and management.
+
+- **Open Telemetry**
+
+  Supports gathering metrics over Open Telemetry for seamless visualization and analysis.
+
+<!--hide_directive
+:::{toctree}
+:hidden:
+
+overview-architecture
+system-requirements
+get-started
+troubleshooting-guide
+how-to-change-dlstreamer-pipeline
+how-to-use-gpu-for-decode-and-inference
+how-to-use-cpu-for-decode-and-inference
+how-to-autostart-pipelines
+how-to-launch-configurable-pipelines
+how-to-perform-webrtc-frame-streaming
+how-to-start-dlsps-mqtt-publish
+how-to-store-s3-frame
+how-to-store-metadata-influxdb
+how-to-publish-metadata-over-ros2
+how-to-launch-and-manage-pipeline
+how-to-use-rtsp-camera-as-video-source
+how-to-run-udf-pipelines
+how-to-deploy-with-helm
+how-to-use-image-file-as-source-over-request-payload
+how-to-download-and-run-yolo-models
+how-to-build-from-source
+how-to-add-system-timestamps-to-metadata
+api-reference
+environment-variables
+advanced-guide/Overview
+release_notes/Overview
+
+:::
+hide_directive-->