add funasr paraformer asr service impl

.github/workflows/docker/compose/third_parties-compose.yaml

@@ -117,3 +117,7 @@ services:
     build:
       dockerfile: comps/third_parties/sglang/src/Dockerfile
     image: ${REGISTRY:-opea}/sglang:${TAG:-latest}
+  funasr-paraformer:
+    build:
+      dockerfile: comps/third_parties/funasr/src/Dockerfile
+    image: ${REGISTRY:-opea}/funasr-paraformer:${TAG:-latest}

comps/asr/deployment/docker_compose/compose.yaml

@@ -3,6 +3,7 @@
 
 include:
   - ../../../third_parties/whisper/deployment/docker_compose/compose.yaml
+  - ../../../third_parties/funasr/deployment/docker_compose/compose.yaml
 
 services:
   asr:
@@ -33,6 +34,15 @@ services:
     depends_on:
       whisper-gaudi-service:
         condition: service_healthy
+  asr-funasr-paraformer:
+    extends: asr
+    container_name: asr-funasr-paraformer-service
+    environment:
+      ASR_COMPONENT_NAME: ${ASR_COMPONENT_NAME:-OPEA_PARAFORMER_ASR}
+      ENABLE_MCP: ${ENABLE_MCP:-False}
+    depends_on:
+      funasr-paraformer-service:
+        condition: service_healthy
 
 networks:
   default:

comps/asr/src/README.md

@@ -12,14 +12,16 @@ ASR (Audio-Speech-Recognition) microservice helps users convert speech to text.
 
 - **ASR Server**: This microservice is responsible for converting speech audio into text. It receives an audio file as input and returns the transcribed text, enabling downstream applications such as conversational bots to process spoken language. The ASR server supports deployment on both CPU and HPU platforms.
 - **Whisper Server**: This microservice is responsible for converting speech audio into text using the Whisper model. It exposes an API endpoint that accepts audio files and returns the transcribed text, supporting both CPU and HPU deployments. The Whisper server acts as the backend for ASR functionality in the overall architecture.
+- **FunASR Paraformer Server**: This microservice is responsible for converting speech audio into text using the Paraformer model with the FunASR toolkit. Similar to the Whisper Server, it exposes an API endpoint that accepts audio files and returns the transcribed text, supporting CPU deployments. The FunASR Paraformer server acts as the backend for ASR functionality in the overall architecture.
 
 ## Deployment Options
 
 For detailed, step-by-step instructions on how to deploy the ASR microservice using Docker Compose on different Intel platforms, please refer to the deployment guide. The guide contains all necessary steps, including building images, configuring the environment, and running the service.
 
-| Platform          | Deployment Method | Link                                                       |
-| ----------------- | ----------------- | ---------------------------------------------------------- |
-| Intel Xeon/Gaudi2 | Docker Compose    | [Deployment Guide](../deployment/docker_compose/README.md) |
+| Platform          | Deployment Method | Link                                       |
+| ----------------- | ----------------- | ------------------------------------------ |
+| Intel Xeon/Gaudi2 | Docker Compose    | [Deployment Guide](./README_whisper.md)    |
+| Intel Core        | Docker Compose    | [Deployment Guide](./README_paraformer.md) |
 
 ## Validated Configurations
 
@@ -28,3 +30,4 @@ The following configurations have been validated for the ASR microservice.
 | **Deploy Method** | **Core Models** | **Platform**      |
 | ----------------- | --------------- | ----------------- |
 | Docker Compose    | Whisper         | Intel Xeon/Gaudi2 |
+| Docker Compose    | Paraformer      | Intel Core        |

comps/asr/src/README_paraformer.md

@@ -0,0 +1,167 @@
+# Deploying ASR Service
+
+This document provides a comprehensive guide to deploying the ASR microservice pipeline with the Paraformer model on Intel platforms.
+
+**Note:** This is an alternative of the [Whisper ASR service](./README.md). The Paraformer model supports both English and Mandarin audio input, and empirically it shows better performance in Mandarin than English.
+
+## Table of contents
+
+- [🚀 1. Quick Start with Docker Compose](#-1-quick-start-with-docker-compose): The recommended method for a fast and easy setup.
+- [🚀 2. Manual Step-by-Step Deployment (Advanced)](#-2-manual-step-by-step-deployment-advanced): For users who want to build and run each container individually.
+- [🚀 3. Start Microservice with Python](#-3-start-microservice-with-python): For users who prefer to run the ASR microservice directly with Python scripts.
+
+## 🚀 1. Quick Start with Docker Compose
+
+This method uses Docker Compose to start all necessary services with a single command. It is the fastest and easiest way to get the service running.
+
+### 1.1. Access the Code
+
+Clone the repository and navigate to the deployment directory:
+
+```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps/comps/asr/deployment/docker_compose
+```
+
+### 1.2. Deploy the Service
+
+Choose the command corresponding to your target platform.
+
+```bash
+export ip_address=$(hostname -I | awk '{print $1}')
+export ASR_ENDPOINT=http://$ip_address:7066
+export no_proxy=localhost,$no_proxy
+```
+
+- **For Intel® Core® CPU:**
+  ```bash
+  docker compose -f ../docker_compose/compose.yaml up funasr-paraformer-service asr-funasr-paraformer -d
+  ```
+  **Note:** it might take some time for `funasr-paraformer-service` to get ready, depending on the model download time in your network environment. If it fails to start with error message `dependency failed to start: container funasr-paraformer-service is unhealthy`, try increasing healthcheck retries in `GenAIComps/comps/third_parties/funasr/deployment/docker_compose/compose.yaml`
+
+### 1.3. Validate the Service
+
+Once the containers are running, you can validate the service. **Note:** Run these commands from the root of the `GenAIComps` repository.
+
+```bash
+# Test
+wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav
+curl http://localhost:9099/v1/audio/transcriptions \
+  -H "Content-Type: multipart/form-data" \
+  -F file="@./sample.wav" \
+  -F model="paraformer-zh"
+```
+
+### 1.4. Clean Up the Deployment
+
+To stop and remove the containers, run the following command from the `comps/asr/deployment/docker_compose` directory:
+
+```bash
+docker compose down
+```
+
+---
+
+## 🚀 2. Manual Step-by-Step Deployment (Advanced)
+
+This section provides detailed instructions for building the Docker images and running each microservice container individually.
+
+### 2.1. Clone the Repository
+
+If you haven't already, clone the repository and navigate to the root directory:
+
+```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
+```
+
+### 2.2. Build the Docker Images
+
+#### 2.2.1. Build FunASR Paraformer Server Image
+
+- **For Intel® Core® CPU:**
+  ```bash
+  docker build -t opea/funasr-paraformer:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/funasr/src/Dockerfile .
+  ```
+
+#### 2.2.2. Build ASR Service Image
+
+```bash
+docker build -t opea/asr:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/src/Dockerfile .
+```
+
+### 2.3 Start FunASR Paraformer and ASR Service
+
+#### 2.3.1 Start FunASR Paraformer Server
+
+- Core CPU
+
+```bash
+docker run -p 7066:7066 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy opea/funasr-paraformer:latest
+```
+
+#### 2.3.2 Start ASR service
+
+```bash
+ip_address=$(hostname -I | awk '{print $1}')
+
+docker run -d -p 9099:9099 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e ASR_ENDPOINT=http://$ip_address:7066 opea/asr:latest
+```
+
+### 2.4 Validate the Service
+
+After starting both containers, test the asr service endpoint. Make sure you are in the root directory of the `GenAIComps` repository.
+
+```bash
+# Use curl or python
+
+# curl
+wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav
+curl http://localhost:9099/v1/audio/transcriptions \
+  -H "Content-Type: multipart/form-data" \
+  -F file="@./sample.wav" \
+  -F model="paraformer-zh"
+
+# python
+python check_asr_server.py
+```
+
+### 2.6. Clean Up the Deployment
+
+To stop and remove the containers you started manually, use the `docker stop` and `docker rm` commands.
+
+- **For Intel® Core® CPU:**
+  ```bash
+  docker stop funasr-paraformer-service asr-funasr-paraformer-service
+  docker rm funasr-paraformer-service asr-funasr-paraformer-service
+  ```
+
+## 🚀 3. Start Microservice with Python
+
+To start the ASR microservice with Python, you need to first install python packages.
+
+### 3.1 Install Requirements
+
+```bash
+pip install -r requirements-cpu.txt
+```
+
+### 3.2 Start FunASR Paraformer Service/Test
+
+- Core CPU
+
+```bash
+cd comps/third_parties/funasr/src
+nohup python funasr_server.py --device=cpu &
+python check_funasr_server.py
+```
+
+Note: please make sure that port 7066 is not occupied by other services. Otherwise, use the command `npx kill-port 7066` to free the port.
+
+### 3.3 Start ASR Service/Test
+
+```bash
+cd ../../..
+python opea_asr_microservice.py
+python check_asr_server.py
+```

comps/asr/deployment/docker_compose/README.md → comps/asr/src/README_whisper.md

@@ -2,7 +2,7 @@
 
 This document provides a comprehensive guide to deploying the ASR microservice pipeline on Intel platforms.
 
-This guide covers two deployment methods:
+## Table of contents
 
 - [🚀 1. Quick Start with Docker Compose](#-1-quick-start-with-docker-compose): The recommended method for a fast and easy setup.
 - [🚀 2. Manual Step-by-Step Deployment (Advanced)](#-2-manual-step-by-step-deployment-advanced): For users who want to build and run each container individually.

comps/asr/src/integrations/funasr_paraformer.py

@@ -0,0 +1,93 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import os
+from typing import List, Union
+
+import requests
+from fastapi import File, Form, UploadFile
+
+from comps import CustomLogger, OpeaComponent, OpeaComponentRegistry, ServiceType
+from comps.cores.proto.api_protocol import AudioTranscriptionResponse
+
+logger = CustomLogger("opea_paraformer")
+logflag = os.getenv("LOGFLAG", False)
+
+
+@OpeaComponentRegistry.register("OPEA_PARAFORMER_ASR")
+class OpeaParaformerAsr(OpeaComponent):
+    """A specialized ASR (Automatic Speech Recognition) component derived from OpeaComponent for FUNASR Paraformer ASR services.
+
+    Attributes:
+        model_name (str): The name of the ASR model used.
+    """
+
+    def __init__(self, name: str, description: str, config: dict = None):
+        super().__init__(name, ServiceType.ASR.name.lower(), description, config)
+        self.base_url = os.getenv("ASR_ENDPOINT", "http://localhost:7066")
+        health_status = self.check_health()
+        if not health_status:
+            logger.error("OpeaParaformerAsr health check failed.")
+
+    async def invoke(
+        self,
+        file: Union[str, UploadFile],  # accept base64 string or UploadFile
+        model: str = Form("paraformer-zh"),
+        language: str = Form("english"),
+        prompt: str = Form(None),
+        response_format: str = Form("json"),
+        temperature: float = Form(0),
+        timestamp_granularities: List[str] = Form(None),
+    ) -> AudioTranscriptionResponse:
+        """Involve the ASR service to generate transcription for the provided input."""
+        if isinstance(file, str):
+            data = {"audio": file}
+            # Send the file and model to the server
+            response = await asyncio.to_thread(
+                requests.post,
+                f"{self.base_url}/v1/asr",
+                json=data,
+            )
+            res = response.json()["asr_result"]
+            return AudioTranscriptionResponse(text=res)
+        else:
+            # Read the uploaded file
+            file_contents = await file.read()
+
+            # Prepare the files and data
+            files = {
+                "file": (file.filename, file_contents, file.content_type),
+            }
+            data = {
+                "model": model,
+                "language": language,
+                "prompt": prompt,
+                "response_format": response_format,
+                "temperature": temperature,
+                "timestamp_granularities": timestamp_granularities,
+            }
+
+            # Send the file and model to the server
+            response = await asyncio.to_thread(
+                requests.post, f"{self.base_url}/v1/audio/transcriptions", files=files, data=data
+            )
+            res = response.json()["text"]
+            return AudioTranscriptionResponse(text=res)
+
+    def check_health(self) -> bool:
+        """Checks the health of the embedding service.
+
+        Returns:
+            bool: True if the service is reachable and healthy, False otherwise.
+        """
+        try:
+            response = requests.get(f"{self.base_url}/health")
+            if response.status_code == 200:
+                return True
+            else:
+                return False
+        except Exception as e:
+            # Handle connection errors, timeouts, etc.
+            logger.error(f"Health check failed: {e}")
+        return False

comps/asr/src/opea_asr_microservice.py

@@ -6,6 +6,7 @@
 from typing import List, Union
 
 from fastapi import File, Form, UploadFile
+from integrations.funasr_paraformer import OpeaParaformerAsr
 from integrations.whisper import OpeaWhisperAsr
 
 from comps import (

comps/third_parties/funasr/deployment/docker_compose/compose.yaml

@@ -0,0 +1,23 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  funasr-paraformer-service:
+    image: ${REGISTRY:-opea}/funasr-paraformer:${TAG:-latest}
+    container_name: funasr-paraformer-service
+    ports:
+      - ${FUNASR_PARAFORMER_PORT:-7066}:7066
+    ipc: host
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:7066/health"]
+      interval: 10s
+      timeout: 6s
+      retries: 60
+    # mount a host directory to cache models if needed, make sure it exists before starting the container
+    # volumes:
+    #   - /home/user/.cache:/home/user/.cache:rw

comps/third_parties/funasr/src/Dockerfile

@@ -0,0 +1,31 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM python:3.11-slim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+# Set environment variables
+ENV LANG=en_US.UTF-8
+ARG ARCH=cpu
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
+    curl \
+    ffmpeg
+
+COPY --chown=user:user comps /home/user/comps
+
+ARG uvpip='uv pip install --system --no-cache-dir'
+RUN pip install --no-cache-dir --upgrade pip setuptools uv && \
+    $uvpip torch torchaudio --index-url https://download.pytorch.org/whl/cpu ; \
+    $uvpip -r /home/user/comps/third_parties/funasr/src/requirements-cpu.txt 
+
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+USER user
+WORKDIR /home/user/comps/third_parties/funasr/src
+
+ENTRYPOINT ["python", "funasr_server.py", "--device", "cpu"]

comps/third_parties/funasr/src/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0