diff --git a/demos/audio/README.md b/demos/audio/README.md
index 963ced134a..0329c14007 100644
--- a/demos/audio/README.md
+++ b/demos/audio/README.md
@@ -11,7 +11,7 @@ Check supported [Speech Recognition Models](https://openvinotoolkit.github.io/op
 
 **Model preparation**: Python 3.10 or higher with pip
 
-**Model Server deployment**: Installed Docker Engine or OVMS binary package according to the [baremetal deployment guide](../../../docs/deploying_server_baremetal.md)
+**Model Server deployment**: Installed Docker Engine or OVMS binary package according to the [baremetal deployment guide](../../docs/deploying_server_baremetal.md)
 
 **Client**: curl or Python for using OpenAI client package
 
diff --git a/demos/integration_with_OpenWebUI/RAG-enabled_model_demo.png b/demos/integration_with_OpenWebUI/RAG-enabled_model_demo.png
index b4f4a9aa32..8b85bfa8af 100644
Binary files a/demos/integration_with_OpenWebUI/RAG-enabled_model_demo.png and b/demos/integration_with_OpenWebUI/RAG-enabled_model_demo.png differ
diff --git a/demos/integration_with_OpenWebUI/README.md b/demos/integration_with_OpenWebUI/README.md
index 2ca04f5057..5bbad16a95 100644
--- a/demos/integration_with_OpenWebUI/README.md
+++ b/demos/integration_with_OpenWebUI/README.md
@@ -16,7 +16,7 @@ In this demo, OpenVINO Model Server is deployed on Linux with CPU using Docker a
 
 * [Docker Engine](https://docs.docker.com/engine/) installed
 * Host with x86_64 architecture
-* Linux, macOS, or Windows via [WSL](https://learn.microsoft.com/en-us/windows/wsl/)
+* Linux, macOS, or Windows
 * Python 3.11 with pip 
 * HuggingFace account to download models
 
@@ -24,39 +24,35 @@ There are other options to fulfill the prerequisites like [OpenVINO Model Server
 
 This demo was tested on CPU but most of the models could be also run on Intel accelerators like GPU and NPU.
 
-### Step 1: Preparation
-
-Download the export script, install its dependencies and create the directory for models:
-
-```bash
-curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/export_model.py -o export_model.py
-pip install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/requirements.txt
+## Step 1: Pull model and start the OVMS sever
+::::{tab-set}
+:::{tab-item} Windows
+:sync: Windows
+```bat
 mkdir models
+ovms.exe --pull --source_model Godreign/llama-3.2-3b-instruct-openvino-int4-model --model_repository_path models --task text_generation
+ovms.exe --add_to_config --config_path  models\config.json --model_path Godreign\llama-3.2-3b-instruct-openvino-int4-model --model_name Godreign/llama-3.2-3b-instruct-openvino-int4-model
+ovms.exe --rest_port 8000 --config_path models\config.json
 ```
-
-### Step 2: Export Model
-
-The text generation model used in this demo is [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). If the model is not downloaded before, access must be requested. Run the export script to download and quantize the model:
-
-```bash
-python export_model.py text_generation --source_model meta-llama/Llama-3.2-1B-Instruct --weight-format int8 --kv_cache_precision u8 --config_file_path models/config.json
-```
-
-### Step 3: Server Deployment
-
-Deploy with docker:
-
+:::
+:::{tab-item} Linux (using Docker)
+:sync: Linux
 ```bash
-docker run -d -p 8000:8000 -v $(pwd)/models:/workspace:ro openvino/model_server --rest_port 8000 --config_path /workspace/config.json
+mkdir models
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --pull --source_model Godreign/llama-3.2-3b-instruct-openvino-int4-model --model_repository_path /models --task text_generation
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --add_to_config --config_path  /models/config.json --model_path Godreign/llama-3.2-3b-instruct-openvino-int4-model --model_name Godreign/llama-3.2-3b-instruct-openvino-int4-model
+docker run -d -u $(id -u):$(id -g) -v $PWD/models:/models -p 8000:8000 openvino/model_server:weekly --rest_port 8000 --config_path /models/config.json
 ```
+:::
+::::
 
 Here is the basic call to check if it works:
 
-```bash
-curl http://localhost:8000/v3/chat/completions -H "Content-Type: application/json" -d "{\"model\":\"meta-llama/Llama-3.2-1B-Instruct\",\"messages\":[{\"role\":\"system\",\"content\":\"You are a helpful assistant.\"},{\"role\":\"user\",\"content\":\"Say this is a test\"}]}"
+```console
+curl http://localhost:8000/v3/chat/completions -H "Content-Type: application/json" -d "{\"model\":\"Godreign/llama-3.2-3b-instruct-openvino-int4-model\",\"messages\":[{\"role\":\"system\",\"content\":\"You are a helpful assistant.\"},{\"role\":\"user\",\"content\":\"Say this is a test\"}]}"
 ```
 
-### Step 4: Start Open WebUI
+## Step 2: Install and start OpenWebUI
 
 Install Open WebUI:
 
@@ -66,7 +62,7 @@ pip install --no-cache-dir open-webui
 
 Running Open WebUI:
 
-```bash
+```console
 open-webui serve
 ```
 
@@ -88,7 +84,7 @@ Go to [http://localhost:8080](http://localhost:8080) and create admin account to
 1. Go to **Admin Panel** → **Settings** → **Connections** ([http://localhost:8080/admin/settings/connections](http://localhost:8080/admin/settings/connections))
 2. Click **+Add Connection** under **OpenAI API**
    * URL: `http://localhost:8000/v3`
-   * Model IDs: put `meta-llama/Llama-3.2-1B-Instruct` and click **+** to add the model, or leave empty to include all models
+   * Model IDs: put `Godreign/llama-3.2-3b-instruct-openvino-int4-model` and click **+** to add the model, or leave empty to include all models
 3. Click **Save**
 
 ![connection setting](./connection_setting.png)
@@ -98,6 +94,18 @@ Click **New Chat** and select the model to start chatting
 
 ![chat demo](./chat_demo.png)
 
+### (optional) Step 3: Set request parameters
+
+There are multiple configurable parameters in OVMS, all of them for `/v3/chat/completions` endpoint are accessible in [chat api documentation](https://github.com/openvinotoolkit/model_server/blob/main/docs/model_server_rest_api_chat.md#request).
+
+To configure them in *OpenWebUI* with an example of turning off reasoning:
+1. Go to **Admin Panel** -> **Settings** -> **Models** ([http://localhost:8080/admin/settings/models](http://localhost:8080/admin/settings/models))
+2. Click on desired model, unfold **Advanced Params**.
+3. Click **+ Add Custom Parameter**.
+4. Change parameter name to `chat_template_kwargs` and content to `{"enable_thinking": false}`.
+
+![parameter set](./set_chat_template_parameter.png)
+
 ### Reference
 [https://docs.openwebui.com/getting-started/quick-start/starting-with-openai-compatible](https://docs.openwebui.com/getting-started/quick-start/starting-with-openai-compatible/#step-2-connect-your-server-to-open-webui)
 
@@ -107,16 +115,33 @@ Click **New Chat** and select the model to start chatting
 
 ### Step 1: Model Preparation
 
-In addition to text generation, endpoints for embedding and reranking in Retrieval Augmented Generation can also be deployed with OpenVINO Model Server. In this demo, the embedding model is [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and the the reranking model is [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base). Run the export script to download and quantize the models:
+In addition to text generation, endpoints for embedding and reranking in Retrieval Augmented Generation can also be deployed with OpenVINO Model Server. In this demo, the embedding model is [OpenVINO/Qwen3-Embedding-0.6B-fp16-ov](https://huggingface.co/OpenVINO/Qwen3-Embedding-0.6B-fp16-ov) and the the reranking model is [OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov](https://huggingface.co/OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov). Run the export script to download and quantize the models:
+
+::::{tab-set}
+:::{tab-item} Windows
+:sync: Windows
+```bat
+ovms.exe --pull --source_model OpenVINO/Qwen3-Embedding-0.6B-fp16-ov --model_repository_path models --task embeddings
+ovms.exe --add_to_config --config_path models\config.json --model_path OpenVINO\Qwen3-Embedding-0.6B-fp16-ov --model_name OpenVINO/Qwen3-Embedding-0.6B-fp16-ov
+ovms.exe --pull --source_model OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov --model_repository_path models --task rerank
+ovms.exe --add_to_config --config_path models\config.json --model_path OpenVINO\Qwen3-Reranker-0.6B-seq-cls-fp16-ov --model_name OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov
+```
+:::
+:::{tab-item} Linux (using Docker)
+:sync: Linux
 ```bash
-python export_model.py embeddings_ov --source_model sentence-transformers/all-MiniLM-L6-v2 --weight-format int8 --config_file_path models/config.json
-python export_model.py rerank_ov --source_model BAAI/bge-reranker-base --weight-format int8 --config_file_path models/config.json
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --pull --source_model OpenVINO/Qwen3-Embedding-0.6B-fp16-ov --model_repository_path models --task embeddings
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --add_to_config --config_path /models/config.json  --model_path OpenVINO/Qwen3-Embedding-0.6B-fp16-ov --model_name OpenVINO/Qwen3-Embedding-0.6B-fp16-ov
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --pull --source_model OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov --model_repository_path models --task rerank
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --add_to_config --config_path /models/config.json  --model_path OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov --model_name OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov
 ```
+:::
+::::
 
 Keep the model server running or restart it. Here are the basic calls to check if they work:
-```bash
-curl http://localhost:8000/v3/embeddings -H "Content-Type: application/json" -d "{\"model\":\"sentence-transformers/all-MiniLM-L6-v2\",\"input\":\"hello world\"}"
-curl http://localhost:8000/v3/rerank -H "Content-Type: application/json" -d "{\"model\":\"BAAI/bge-reranker-base\",\"query\":\"welcome\",\"documents\":[\"good morning\",\"farewell\"]}"
+```console
+curl http://localhost:8000/v3/embeddings -H "Content-Type: application/json" -d "{\"model\":\"OpenVINO/Qwen3-Embedding-0.6B-fp16-ov\",\"input\":\"hello world\"}"
+curl http://localhost:8000/v3/rerank -H "Content-Type: application/json" -d "{\"model\":\"OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov\",\"query\":\"welcome\",\"documents\":[\"good morning\",\"farewell\"]}"
 ```
 
 ### Step 2: Documents Setting
@@ -124,12 +149,14 @@ curl http://localhost:8000/v3/rerank -H "Content-Type: application/json" -d "{\"
 1. Go to **Admin Panel** → **Settings** → **Documents** ([http://localhost:8080/admin/settings/documents](http://localhost:8080/admin/settings/documents))
 2. Select **OpenAI** for **Embedding Model Engine**
    * URL: `http://localhost:8000/v3`
-   * Embedding Model: `sentence-transformers/all-MiniLM-L6-v2`
+   * Set Engine type to `OpenAI` 
+   * Embedding Model: `OpenVINO/Qwen3-Embedding-0.6B-fp16-ov`
    * Put anything in API key
 3. Enable **Hybrid Search**
 4. Select **External** for **Reranking Engine**
    * URL: `http://localhost:8000/v3/rerank`
-   * Reranking Model: `BAAI/bge-reranker-base`
+   * Set Engine type to `External`
+   * Reranking Model: `OpenVINO/Qwen3-Reranker-0.6B-seq-cls-fp16-ov`
 5. Click **Save**
 
 ![embedding and retrieval setting](./embedding_and_retrieval_setting.png)
@@ -140,7 +167,7 @@ curl http://localhost:8000/v3/rerank -H "Content-Type: application/json" -d "{\"
    
    The documentation used in this demo is [https://github.com/open-webui/docs/archive/refs/heads/main.zip](https://github.com/open-webui/docs/archive/refs/heads/main.zip). Download and extract it to get the folder.
 
-2. Go to **Workspace** → **Knowledge** → **+Create a Knowledge Base** ([http://localhost:8080/workspace/knowledge/create](http://localhost:8080/workspace/knowledge/create))
+2. Go to **Workspace** → **Knowledge** → **+ New Knowledge** ([http://localhost:8080/workspace/knowledge/create](http://localhost:8080/workspace/knowledge/create))
 3. Name and describe the knowledge base
 4. Click **Create Knowledge**
 5. Click **+Add Content** → **Upload directory**, then select the extracted folder. This will upload all files with suitable extensions.
@@ -160,7 +187,7 @@ curl http://localhost:8000/v3/rerank -H "Content-Type: application/json" -d "{\"
 
 ### Step 5: RAG-enabled Model
 
-1. Go to **Workspace** → **Models** → **+Add New Model** ([http://localhost:8080/workspace/models/create](http://localhost:8080/workspace/models/create))
+1. Go to **Workspace** → **Models** → **+ New Model** ([http://localhost:8080/workspace/models/create](http://localhost:8080/workspace/models/create))
 2. Configure the Model:
    * Name the model
    * Select a base model from the list
@@ -185,16 +212,29 @@ curl http://localhost:8000/v3/rerank -H "Content-Type: application/json" -d "{\"
 
 ### Step 1: Model Preparation
 
-The image generation model used in this demo is [dreamlike-art/dreamlike-anime-1.0](https://huggingface.co/dreamlike-art/dreamlike-anime-1.0). Run the export script to download and quantize the model:
+The image generation model used in this demo is [OpenVINO/FLUX.1-schnell-int4-ov](https://huggingface.co/OpenVINO/FLUX.1-schnell-int4-ov). Run the ovms with --pull parameter to download and quantize the model:
 
+::::{tab-set}
+:::{tab-item} Windows
+:sync: Windows
+```bat
+ovms.exe --pull --source_model OpenVINO/FLUX.1-schnell-int4-ov --model_repository_path models --model_name OpenVINO/FLUX.1-schnell-int4-ov --task image_generation --default_num_inference_steps 3
+ovms.exe --add_to_config --config_path models\config.json --model_path OpenVINO\FLUX.1-schnell-int4-ov --model_name OpenVINO/FLUX.1-schnell-int4-ov
+```
+:::
+:::{tab-item} Linux (using Docker)
+:sync: Linux
 ```bash
-python export_model.py image_generation --source_model dreamlike-art/dreamlike-anime-1.0 --weight-format int8 --config_file_path models/config.json
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --pull --source_model OpenVINO/FLUX.1-schnell-int4-ov --model_repository_path models --model_name OpenVINO/FLUX.1-schnell-int4-ov --task image_generation --default_num_inference_steps 3
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly  --add_to_config --config_path /models/config.json  --model_path OpenVINO/FLUX.1-schnell-int4-ov --model_name OpenVINO/FLUX.1-schnell-int4-ov
 ```
+:::
+::::
 
 Keep the model server running or restart it. Here is the basic call to check if it works:
 
-```bash
-curl http://localhost:8000/v3/images/generations -H "Content-Type: application/json" -d "{\"model\":\"dreamlike-art/dreamlike-anime-1.0\",\"prompt\":\"anime\",\"num_inference_steps\":1,\"size\":\"256x256\",\"response_format\":\"b64_json\"}"
+```console
+curl http://localhost:8000/v3/images/generations -H "Content-Type: application/json" -d "{\"model\":\"OpenVINO/FLUX.1-schnell-int4-ov\",\"prompt\":\"anime\",\"num_inference_steps\":1,\"size\":\"256x256\",\"response_format\":\"b64_json\"}"
 ```
 
 ### Step 2: Image Generation Setting
@@ -204,7 +244,7 @@ curl http://localhost:8000/v3/images/generations -H "Content-Type: application/j
    * URL: `http://localhost:8000/v3`
    * Put anything in API key
 3. Enable **Image Generation (Experimental)**
-   * Set Default Model: `dreamlike-art/dreamlike-anime-1.0`
+   * Set Default Model: `OpenVINO/FLUX.1-schnell-int4-ov`
    * Set Image Size. Must be in WxH format, example: `256x256`
 4. Click **Save**
 
@@ -213,8 +253,9 @@ curl http://localhost:8000/v3/images/generations -H "Content-Type: application/j
 ### Step 3: Generate Image
 
 Method 1:
-1. Toggle the **Image** switch to on
-2. Enter a query and send
+1. Expand `Integrations` menu
+2. Toggle the **Image** switch to on
+3. Enter a query and send
 
 ![image generation method 1 demo](./image_generation_method_1_demo.png)
 
@@ -228,28 +269,41 @@ Method 2:
 ### Reference
 [https://docs.openvino.ai/2025/model-server/ovms_demos_image_generation.html](https://docs.openvino.ai/2025/model-server/ovms_demos_image_generation.html#export-model-for-cpu)
 
-[https://docs.openwebui.com/tutorials/images](https://docs.openwebui.com/tutorials/images/#using-image-generation)
+[https://docs.openwebui.com/features/image-generation-and-editing](https://docs.openwebui.com/features/image-generation-and-editing/openai)
 
 ---
 ## VLM
 
 ### Step 1: Model Preparation
 
-The vision language model used in this demo is [OpenGVLab/InternVL2-2B](https://huggingface.co/OpenGVLab/InternVL2-2B). Run the export script to download and quantize the model:
+The vision language model used in this demo is [OpenVINO/InternVL2-2B-int4-ov](https://huggingface.co/OpenVINO/InternVL2-2B-int4-ov). Run the ovms with --pull parameter to download and quantize the model:
 
+::::{tab-set}
+:::{tab-item} Windows
+:sync: Windows
+```bat
+ovms.exe --pull --source_model OpenVINO/InternVL2-2B-int4-ov --model_repository_path models --model_name OpenVINO/InternVL2-2B-int4-ov --task text_generation
+ovms.exe --add_to_config --config_path models\config.json --model_path OpenVINO\InternVL2-2B-int4-ov --model_name OpenVINO/InternVL2-2B-int4-ov
+```
+:::
+:::{tab-item} Linux (using Docker)
+:sync: Linux
 ```bash
-python export_model.py text_generation --source_model OpenGVLab/InternVL2-2B --weight-format int4 --pipeline_type VLM --model_name OpenGVLab/InternVL2-2B --config_file_path models/config.json
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --pull --source_model OpenVINO/InternVL2-2B-int4-ov --model_repository_path models --model_name OpenVINO/InternVL2-2B-int4-ov --task text_generation
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --add_to_config --config_path /models/config.json  --model_path OpenVINO/InternVL2-2B-int4-ov --model_name OpenVINO/InternVL2-2B-int4-ov
 ```
+:::
+::::
 
 Keep the model server running or restart it. Here is the basic call to check if it works:
 
-```bash
-curl http://localhost:8000/v3/chat/completions  -H "Content-Type: application/json" -d "{ \"model\": \"OpenGVLab/InternVL2-2B\", \"messages\":[{\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"what is in the picture?\"},{\"type\": \"image_url\", \"image_url\": {\"url\": \"http://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/static/images/zebra.jpeg\"}}]}], \"max_completion_tokens\": 100}"
+```console
+curl http://localhost:8000/v3/chat/completions  -H "Content-Type: application/json" -d "{ \"model\": \"OpenVINO/InternVL2-2B-int4-ov\", \"messages\":[{\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"what is in the picture?\"},{\"type\": \"image_url\", \"image_url\": {\"url\": \"http://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/static/images/zebra.jpeg\"}}]}], \"max_completion_tokens\": 100}"
 ```
 
 ### Step 2: Chat with VLM
 
-1. Start a **New Chat** with model set to `OpenGVLab/InternVL2-2B`
+1. Start a **New Chat** with model set to `OpenVINO/InternVL2-2B-int4-ov`
 2. Click **+More** to upload images, by capturing the screen or uploading files. The image used in this demo is [http://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/static/images/zebra.jpeg](http://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/common/static/images/zebra.jpeg).
 
 ![upload images](./upload_images.png)
@@ -266,20 +320,19 @@ curl http://localhost:8000/v3/chat/completions  -H "Content-Type: application/js
 
 ### Step 1: Start Tool Server
 
-Start a OpenAPI tool server available in the [openapi-servers repo](https://github.com/open-webui/openapi-servers). The server used in this demo is [https://github.com/open-webui/openapi-servers/tree/main/servers/time](https://github.com/open-webui/openapi-servers/tree/main/servers/time). Run it locally at `http://localhost:18000`:
+Start a OpenAPI tool server available in the [openapi-servers repo](https://github.com/open-webui/openapi-servers). The server used in this demo is [https://github.com/open-webui/openapi-servers/tree/main/servers/weather](https://github.com/open-webui/openapi-servers/tree/main/servers/weather). Run it locally at `http://localhost:9000`:
 
-```bash
-git clone https://github.com/open-webui/openapi-servers
-cd openapi-servers/servers/time
-pip install -r requirements.txt
-uvicorn main:app --host 0.0.0.0 --port 18000 --reload
+```console
+pip install mcpo
+pip install mcp_weather_server
+mcpo --port 9000 -- python -m mcp_weather_server
 ```
 
 ### Step 2: Tools Setting
 
-1. Go to **Admin Panel** → **Settings** → **Tools** ([http://localhost:8080/admin/settings/tools](http://localhost:8080/admin/settings/tools))
+1. Go to **Admin Panel** → **Settings** → **External Tools** 
 2. Click **+Add Connection**
-   * URL: `http://localhost:18000`
+   * URL: `http://localhost:9000`
    * Name the tool
 3. Click **Save**
 
@@ -296,4 +349,60 @@ uvicorn main:app --host 0.0.0.0 --port 18000 --reload
 ![chat with AI Agent demo](./chat_with_AI_Agent_demo.png)
 
 ### Reference
-[https://docs.openwebui.com/openapi-servers/open-webui](https://docs.openwebui.com/openapi-servers/open-webui/#step-2-connect-tool-server-in-open-webui)
+[https://docs.openwebui.com/features/plugin/tools/openapi-servers/open-webui](https://docs.openwebui.com/features/plugin/tools/openapi-servers/open-webui#step-2-connect-tool-server-in-open-webui)
+
+
+## Audio
+
+> **Note:** To ensure audio features work correctly, download [FFmpeg](https://ffmpeg.org/download.html) and add its executable directory to your system's `PATH` environment variable.
+
+### Step 1: Models Preparation
+
+Start by downloading `export_models.py` script and run it to download and quantize the model for speech generation:
+```console 
+curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/export_model.py -o export_model.py
+pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/requirements.txt
+python export_model.py text2speech --source_model microsoft/speecht5_tts --weight-format fp32 --model_name microsoft/speecht5_tts --config_file_path models/config.json --model_repository_path models --vocoder microsoft/speecht5_hifigan
+```
+
+Next, download and add to config model for transcription:
+
+::::{tab-set}
+:::{tab-item} Windows
+:sync: Windows
+```bat
+ovms.exe --pull --source_model OpenVINO/whisper-base-fp16-ov --model_repository_path models --task speech2text
+ovms.exe --add_to_config --config_path  models\config.json --model_path OpenVINO\whisper-base-fp16-ov --model_name OpenVINO/whisper-base-fp16-ov
+```
+:::
+:::{tab-item} Linux (using Docker)
+:sync: Linux
+```bash
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --pull --source_model OpenVINO/whisper-base-fp16-ov --model_repository_path /models --task speech2text
+docker run --rm -u $(id -u):$(id -g) -v $PWD/models:/models openvino/model_server:weekly --add_to_config --config_path /models/config.json --model_path OpenVINO/whisper-base-fp16-ov --model_name OpenVINO/whisper-base-fp16-ov
+```
+:::
+:::: 
+
+### Step 2: Audio Settings
+
+1. Go to **Admin Panel** → **Settings** → **Audio** 
+2. Select **OpenAI** for both engines
+   * URL: `http://localhost:8000/v3`
+   * Set Engine type to `OpenAI` 
+   * STT Model: `OpenVINO/whisper-base-fp16-ov`
+   * TTS Model: `microsoft/speecht5_tts`
+   * Put anything in API key
+3. Click **Save**
+
+![audio settings](./audio_configuration.png)
+
+### Step 3: Chat with AI Agent
+
+1. Click **Voice mode** icon.
+2. Start talking.
+
+![voice mode](./voice_mode.png)
+
+### Reference
+[https://docs.openwebui.com/features/#%EF%B8%8F-audio-voice--accessibility](https://docs.openwebui.com/features/#%EF%B8%8F-audio-voice--accessibility)
\ No newline at end of file
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