diff --git a/README.md b/README.md
index 8e5e2ca0..856fa700 100644
--- a/README.md
+++ b/README.md
@@ -103,18 +103,20 @@ up -d
      -p 6379:6379 \
      redis:latest
 
-# Start PostgreSQL
-docker run -d --name postgres \
-  -e POSTGRES_USER=${POSTGRES_USER} \
-  -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} \
-  -e POSTGRES_DB=${POSTGRES_DB} \
-  -p 5432:5432 \
-  --network frontend_net \
-  --volume postgres_data:/var/lib/postgresql/data \
-  postgres:16
-```
-
-2. **Run API Server**
+2. **Start PostgreSQL**
+    ```shell
+    docker run -d --name postgres \
+      -e POSTGRES_USER=${POSTGRES_USER} \
+      -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} \
+      -e POSTGRES_DB=${POSTGRES_DB} \
+      -p 5432:5432 \
+      --network frontend_net \
+      --volume postgres_data:/var/lib/postgresql/data \
+      postgres:16
+    ```
+
+
+3. **Run API Server**
    ```shell
    # Development Environment
     fastapi dev nilai-api/src/nilai_api/__main__.py --port 8080
@@ -123,7 +125,7 @@ docker run -d --name postgres \
    uv run fastapi run nilai-api/src/nilai_api/__main__.py --port 8080
    ```
 
-3. **Run Model Instances**
+4. **Run Model Instances**
    ```shell
    # Example: Llama 3.2 1B Model
    # Development Environment
diff --git a/docker/audio.Dockerfile b/docker/audio.Dockerfile
new file mode 100644
index 00000000..70c912d3
--- /dev/null
+++ b/docker/audio.Dockerfile
@@ -0,0 +1,26 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY --link . /app/
+WORKDIR /app/nilai-audio/
+
+# Install system dependencies
+RUN apt-get update && \
+apt-get install build-essential curl ffmpeg -y && \
+apt-get clean && \
+apt-get autoremove && \
+rm -rf /var/lib/apt/lists/* && \
+pip install uv && \
+uv sync
+
+ENV LD_LIBRARY_PATH=/app/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/:${LD_LIBRARY_PATH}
+
+# Create necessary directories
+RUN mkdir -p uploads transcript_results_diarized summaries extracted_audio
+
+# Expose port for FastAPI
+EXPOSE 8000
+
+# Command to run the application
+CMD ["uv", "run", "uvicorn", "nilai_audio.main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/docker/compose/docker-compose.audio-gpu.yml b/docker/compose/docker-compose.audio-gpu.yml
new file mode 100644
index 00000000..f980e967
--- /dev/null
+++ b/docker/compose/docker-compose.audio-gpu.yml
@@ -0,0 +1,39 @@
+# File: docker/compose/docker-compose.audio-gpu.yml
+services:
+  audio_gpu:
+    image: nillion/nilai-audio:latest
+    container_name: nilai-audio_gpu
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    ipc: host
+    ulimits:
+      memlock: -1
+      stack: 67108864
+    env_file:
+      - .env
+    restart: unless-stopped
+    volumes:
+      - hugging_face_models:/root/.cache/huggingface
+      - torch_models:/root/.cache/torch
+    networks:
+      - backend_net
+    ports:
+      - "8002:8000"
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
+      interval: 30s
+      retries: 3
+      start_period: 90s
+      timeout: 15s
+
+volumes:
+  hugging_face_models:
+  torch_models:
+
+networks:
+  backend_net:
\ No newline at end of file
diff --git a/docker/compose/docker-compose.qwen-14b-gpu.yml b/docker/compose/docker-compose.qwen-14b-gpu.yml
new file mode 100644
index 00000000..12a07363
--- /dev/null
+++ b/docker/compose/docker-compose.qwen-14b-gpu.yml
@@ -0,0 +1,48 @@
+services:
+  llama_70b_gpu:
+    image: nillion/nilai-vllm:latest
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    ipc: host
+    ulimits:
+      memlock: -1
+      stack: 67108864
+    env_file:
+      - .env
+    restart: unless-stopped
+    depends_on:
+      etcd:
+        condition: service_healthy
+    command: >
+      --model Qwen/Qwen2.5-14B-Instruct-1M
+      --gpu-memory-utilization 0.39
+      --max-model-len 50000
+      --tensor-parallel-size 1
+      --enable-auto-tool-choice
+      --uvicorn-log-level warning
+    environment:
+      - SVC_HOST=qwen_14b_gpu
+      - SVC_PORT=8000
+      - ETCD_HOST=etcd
+      - ETCD_PORT=2379
+      - TOOL_SUPPORT=true
+    volumes:
+      - hugging_face_models:/root/.cache/huggingface
+    networks:
+      - backend_net
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      retries: 3
+      start_period: 60s
+      timeout: 10s
+volumes:
+  hugging_face_models:
+
+networks:
+  backend_net:
diff --git a/nilai-api/src/nilai_api/config/mainnet.py b/nilai-api/src/nilai_api/config/mainnet.py
index 2a27af1a..b7907958 100644
--- a/nilai-api/src/nilai_api/config/mainnet.py
+++ b/nilai-api/src/nilai_api/config/mainnet.py
@@ -10,6 +10,7 @@
     "cognitivecomputations/Dolphin3.0-Llama3.1-8B": 30,
     "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B": 5,
     "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4": 5,
+    "Qwen/Qwen2.5-14B-Instruct-1M": 5,
 }
 
 # It defines the number of requests allowed for each user for a given time frame.
diff --git a/nilai-api/src/nilai_api/config/testnet.py b/nilai-api/src/nilai_api/config/testnet.py
index 8efcb7df..7ff574b7 100644
--- a/nilai-api/src/nilai_api/config/testnet.py
+++ b/nilai-api/src/nilai_api/config/testnet.py
@@ -10,6 +10,7 @@
     "cognitivecomputations/Dolphin3.0-Llama3.1-8B": 5,
     "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B": 5,
     "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4": 5,
+    "Qwen/Qwen2.5-14B-Instruct-1M": 5,
 }
 
 # It defines the number of requests allowed for each user for a given time frame.
diff --git a/nilai-audio/README.md b/nilai-audio/README.md
new file mode 100644
index 00000000..e69de29b
diff --git a/nilai-audio/pyproject.toml b/nilai-audio/pyproject.toml
new file mode 100644
index 00000000..091b7b93
--- /dev/null
+++ b/nilai-audio/pyproject.toml
@@ -0,0 +1,29 @@
+[project]
+name = "nilai-audio"
+version = "0.1.0"
+description = "WhisperX service with Nilai"
+readme = "README.md"
+authors = [
+    { name = "blefo", email = "lefort.baptiste@yahoo.fr" }
+]
+requires-python = ">=3.12"
+dependencies = [
+    "torch>=2.0.0",
+    "torchaudio>=2.0.0",
+    "whisperx==3.3.2",
+    "fastapi>=0.110.0",
+    "uvicorn[standard]>=0.29.0",
+    "python-multipart",
+    "httpx>=0.27.0",
+    "nilai-common",
+    "pyannote.audio==3.3.2",
+    "nilai-models",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.uv.sources]
+nilai-common = { workspace = true }
+nilai-models = { workspace = true }
diff --git a/nilai-audio/run.sh b/nilai-audio/run.sh
new file mode 100644
index 00000000..284377dc
--- /dev/null
+++ b/nilai-audio/run.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+set -euo pipefail
+
+start_primary_process() {
+    echo "Starting the primary process"
+    echo "Args: [$*]"
+    uv run uvicorn main:app --host 0.0.0.0 --port 8000 &
+}
+
+start_secondary_process() {
+    echo "Starting the secondary process"
+    uv run python3 -m nilai_models.daemon
+}
+
+main() {
+    echo "Starting the main process with args: $*"
+    if [[ " $* " =~ "--standalone" ]]; then
+        echo "Starting the standalone server"
+        # Remove --standalone from arguments
+        args=("${@/--standalone/}")
+        start_primary_process "${args[@]}"
+    else
+        start_primary_process "$@"
+        start_secondary_process
+    fi
+
+    # Wait for any process to exit and exit with its status
+    wait -n
+    exit $?
+}
+
+main "$@"
\ No newline at end of file
diff --git a/nilai-audio/src/nilai_audio/__init__.py b/nilai-audio/src/nilai_audio/__init__.py
new file mode 100644
index 00000000..0b805b35
--- /dev/null
+++ b/nilai-audio/src/nilai_audio/__init__.py
@@ -0,0 +1,2 @@
+def hello() -> str:
+    return "Hello from nilai-audio!"
diff --git a/nilai-audio/src/nilai_audio/main.py b/nilai-audio/src/nilai_audio/main.py
new file mode 100644
index 00000000..ca1fadab
--- /dev/null
+++ b/nilai-audio/src/nilai_audio/main.py
@@ -0,0 +1,313 @@
+import os
+import shutil
+import torch
+import whisperx
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form, Request
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+import datetime
+import uuid
+from fastapi.responses import JSONResponse
+import asyncio
+import subprocess
+from typing import Optional, Annotated
+import traceback
+import logging
+import tempfile
+import uvicorn
+
+from nilai_audio.transcription import transcribe_audio_logic
+from nilai_audio.summarization import read_transcript
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+WHISPER_MODEL_NAME = "large-v3"
+WHISPER_BATCH_SIZE = 16
+WHISPER_COMPUTE_TYPE = "float16"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+ml_models = {}
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    logger.info("Loading models...")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    logger.info(f"Using device: {device}")
+    ml_models["device"] = device
+
+    logger.info(f"Loading Whisper model: {WHISPER_MODEL_NAME} using compute type: {WHISPER_COMPUTE_TYPE}...")
+    try:
+        ml_models["whisper_model"] = whisperx.load_model(
+            WHISPER_MODEL_NAME, device, compute_type=WHISPER_COMPUTE_TYPE
+        )
+        logger.info("Whisper model loaded.")
+    except Exception as e:
+        logger.error(f"FATAL: Failed to load Whisper model: {e}", exc_info=True)
+        ml_models["whisper_model"] = None
+
+    alignment_language_code = "en"
+    logger.info(f"Loading alignment model for language: {alignment_language_code}...")
+    try:
+         ml_models["align_model_tuple"] = whisperx.load_align_model(language_code=alignment_language_code, device=device)
+         logger.info("Alignment model loaded.")
+    except Exception as e:
+         logger.warning(f"Could not load default '{alignment_language_code}' alignment model: {e}. Trying without language code...")
+         try:
+             ml_models["align_model_tuple"] = whisperx.load_align_model(language_code=None, device=device)
+             logger.info("Alignment model loaded (no language code).")
+         except Exception as e2:
+             logger.error(f"FATAL: Failed to load any alignment model: {e2}", exc_info=True)
+             ml_models["align_model_tuple"] = None
+
+    logger.info("Loading diarization pipeline...")
+    if not HF_TOKEN:
+        logger.warning("Hugging Face token (HF_TOKEN) not found in environment variables. Diarization might fail or require login.")
+    try:
+        auth_token = HF_TOKEN if HF_TOKEN else None
+        ml_models["diarize_pipeline"] = whisperx.DiarizationPipeline(use_auth_token=auth_token, device=device)
+        logger.info("Diarization pipeline loaded.")
+    except Exception as e:
+        logger.error(f"FATAL: Failed to load diarization pipeline: {e}", exc_info=True)
+        ml_models["diarize_pipeline"] = None
+
+    logger.info("--- Model loading process complete. ---")
+
+    yield
+
+    logger.info("Cleaning up models...")
+    ml_models.clear()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    logger.info("Cleanup complete.")
+
+app = FastAPI(lifespan=lifespan)
+
+origins = ["*"]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+def check_model_availability(model_keys: list[str]):
+    missing_models = [key for key in model_keys if key not in ml_models or ml_models[key] is None]
+    if missing_models:
+        detail = f"Required model(s) not available: {', '.join(missing_models)}. Server may not be ready or models failed to load."
+        logger.error(f"Model Availability Check Failed: {detail}")
+        raise HTTPException(status_code=503, detail=detail)
+
+async def run_transcription_logic_internal(
+    audio_path: str,
+    output_dir: str,
+    num_speakers: Optional[int] = None
+) -> tuple[Optional[str], Optional[str]]:
+    transcript_path: Optional[str] = None
+    transcript_content: Optional[str] = None
+    try:
+        logger.info(f"Starting transcription for temp file: {audio_path} -> output dir: {output_dir}")
+        check_model_availability(["whisper_model", "diarize_pipeline", "device"])
+
+        transcript_path = transcribe_audio_logic(
+            audio_file_path=audio_path,
+            model=ml_models["whisper_model"],
+            align_model_tuple=ml_models.get("align_model_tuple"),
+            diarize_pipeline=ml_models["diarize_pipeline"],
+            device=ml_models["device"],
+            batch_size=WHISPER_BATCH_SIZE,
+            output_dir=output_dir,
+            num_speakers=num_speakers
+        )
+        logger.info(f"Transcription processing complete. Potential output path: {transcript_path}")
+
+        if not transcript_path or not os.path.exists(transcript_path):
+            files_in_output = os.listdir(output_dir)
+            if files_in_output:
+                logger.warning(f"Transcription logic returned path '{transcript_path}', but found files: {files_in_output}. Assuming first file.")
+                transcript_path = os.path.join(output_dir, files_in_output[0])
+            else:
+                 raise ValueError("Transcription failed: No output file generated in the temporary directory.")
+
+        if not os.path.exists(transcript_path):
+             raise ValueError(f"Transcription failed: Reported output path does not exist: {transcript_path}")
+
+        transcript_content = read_transcript(transcript_path)
+        if transcript_content is None:
+            raise ValueError(f"Transcription file generated ({transcript_path}) but could not be read")
+
+        logger.info(f"Successfully read transcript content (length {len(transcript_content)}).")
+        return transcript_path, transcript_content
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"An error occurred during transcription logic: {e}", exc_info=True)
+        return None, None
+
+@app.post("/transcribe-media/")
+async def transcribe_media_endpoint(
+    mediaFile: UploadFile = File(...),
+    num_speakers: Annotated[Optional[int], Form()] = None
+):
+    if not mediaFile.filename:
+        raise HTTPException(status_code=400, detail="No filename provided.")
+
+    request_id = uuid.uuid4()
+    logger.info(f"[Request {request_id}] Received media file: {mediaFile.filename}, Content-Type: {mediaFile.content_type}")
+    if num_speakers is not None:
+        logger.info(f"[Request {request_id}] Received num_speakers hint: {num_speakers}")
+    else:
+        logger.info(f"[Request {request_id}] No num_speakers hint received, using auto-detect.")
+
+    temp_upload_dir: Optional[str] = None
+    temp_media_path: Optional[str] = None
+    temp_audio_extract_dir: Optional[str] = None
+    temp_extracted_audio_path: Optional[str] = None
+    temp_transcript_dir: Optional[str] = None
+    local_transcript_path: Optional[str] = None
+    temp_base_dir: Optional[str] = None
+
+    try:
+        temp_base_dir = tempfile.mkdtemp(prefix=f"req_{request_id.hex[:8]}_")
+        logger.debug(f"[Request {request_id}] Created base temporary directory: {temp_base_dir}")
+
+        temp_upload_dir = os.path.join(temp_base_dir, "upload")
+        os.makedirs(temp_upload_dir)
+        sanitized_filename = "".join(c if c.isalnum() or c in ['.', '-', '_'] else '_' for c in mediaFile.filename)
+        temp_media_path = os.path.join(temp_upload_dir, sanitized_filename)
+
+        logger.info(f"[Request {request_id}] Saving uploaded file to temporary path: {temp_media_path}")
+        try:
+            with open(temp_media_path, "wb") as buffer:
+                shutil.copyfileobj(mediaFile.file, buffer)
+            file_size = os.path.getsize(temp_media_path)
+            logger.info(f"[Request {request_id}] Saved {mediaFile.filename} to {temp_media_path}, Size: {file_size / (1024*1024):.2f} MB")
+        except Exception as e:
+            logger.error(f"[Request {request_id}] Failed to save uploaded file to {temp_media_path}: {e}", exc_info=True)
+            raise HTTPException(status_code=500, detail="Failed to save uploaded file.")
+
+        file_type = mediaFile.content_type or ""
+        audio_path_for_transcription: Optional[str] = None
+
+        if file_type.startswith('audio/'):
+            logger.info(f"[Request {request_id}] Processing as audio file.")
+            audio_path_for_transcription = temp_media_path
+        elif file_type.startswith('video/'):
+            logger.info(f"[Request {request_id}] Processing as video file, extracting audio...")
+            temp_audio_extract_dir = os.path.join(temp_base_dir, "extracted_audio")
+            os.makedirs(temp_audio_extract_dir)
+            base_name, _ = os.path.splitext(sanitized_filename)
+            temp_audio_filename = f"{base_name}.mp3"
+            temp_extracted_audio_path = os.path.join(temp_audio_extract_dir, temp_audio_filename)
+
+            logger.info(f"[Request {request_id}] Extracting audio to: {temp_extracted_audio_path}")
+            try:
+                command = [
+                    "ffmpeg", "-i", temp_media_path, "-vn",
+                    "-acodec", "libmp3lame", "-q:a", "2",
+                    "-ar", "16000", "-ac", "1",
+                    "-y",
+                    temp_extracted_audio_path
+                ]
+                logger.info(f"[Request {request_id}] Running FFmpeg command: {' '.join(command)}")
+                process = await asyncio.create_subprocess_exec(
+                    *command,
+                    stdout=asyncio.subprocess.PIPE,
+                    stderr=asyncio.subprocess.PIPE
+                )
+                stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=600.0)
+
+                if process.returncode != 0:
+                    stderr_decoded = stderr.decode('utf-8', errors='ignore') if stderr else 'No stderr output'
+                    logger.error(f"[Request {request_id}] FFmpeg failed (Code: {process.returncode}). Stderr: {stderr_decoded}")
+                    raise RuntimeError(f"Failed to extract audio. FFmpeg error: {stderr_decoded}")
+                else:
+                    stdout_decoded = stdout.decode('utf-8', errors='ignore') if stdout else 'No stdout output'
+                    logger.info(f"[Request {request_id}] Audio extracted successfully. FFmpeg stdout: {stdout_decoded[:200]}...")
+                    logger.info(f"[Request {request_id}] FFmpeg stderr: {stderr.decode('utf-8', errors='ignore')[:200]}...")
+
+                if not os.path.exists(temp_extracted_audio_path) or os.path.getsize(temp_extracted_audio_path) == 0:
+                     logger.error(f"[Request {request_id}] FFmpeg reported success but output file is missing or empty: {temp_extracted_audio_path}")
+                     raise RuntimeError("Audio extraction resulted in an empty or missing file.")
+
+                audio_path_for_transcription = temp_extracted_audio_path
+
+            except FileNotFoundError:
+                logger.error(f"[Request {request_id}] Error: ffmpeg command not found.")
+                raise HTTPException(status_code=500, detail="Audio extraction tool (ffmpeg) not found on server.")
+            except asyncio.TimeoutError:
+                logger.error(f"[Request {request_id}] Error: FFmpeg process timed out after 600 seconds.")
+                if process.returncode is None:
+                    try:
+                        process.kill()
+                        await process.wait()
+                        logger.warning(f"[Request {request_id}] Killed timed-out FFmpeg process.")
+                    except ProcessLookupError:
+                         logger.warning(f"[Request {request_id}] Timed-out FFmpeg process already finished.")
+                    except Exception as kill_err:
+                        logger.error(f"[Request {request_id}] Error trying to kill timed-out FFmpeg process: {kill_err}")
+                raise HTTPException(status_code=500, detail="Audio extraction timed out.")
+            except Exception as e:
+                 logger.error(f"[Request {request_id}] An unexpected error occurred during audio extraction: {e}", exc_info=True)
+                 raise RuntimeError(f"An unexpected error occurred during audio extraction: {str(e)}")
+        else:
+            logger.warning(f"[Request {request_id}] Unsupported file type received: {file_type}")
+            raise HTTPException(status_code=415, detail=f"Unsupported file type: {file_type}. Please upload audio or video.")
+
+        if not audio_path_for_transcription:
+              logger.error(f"[Request {request_id}] Could not determine audio path for transcription after processing.")
+              raise HTTPException(status_code=500, detail="Could not determine audio path for transcription.")
+
+        temp_transcript_dir = os.path.join(temp_base_dir, "transcript")
+        os.makedirs(temp_transcript_dir)
+        logger.info(f"[Request {request_id}] Starting transcription for: {audio_path_for_transcription} -> {temp_transcript_dir}")
+
+        local_transcript_path, transcript_content = await run_transcription_logic_internal(
+            audio_path=audio_path_for_transcription,
+            output_dir=temp_transcript_dir,
+            num_speakers=num_speakers
+        )
+
+        if local_transcript_path is None or transcript_content is None:
+            logger.error(f"[Request {request_id}] Transcription process failed or returned None.")
+            raise HTTPException(status_code=500, detail="Transcription process failed.")
+
+        logger.info(f"[Request {request_id}] Transcription successful. Transcript path: {local_transcript_path}")
+
+        base_name, _ = os.path.splitext(sanitized_filename)
+        unique_suffix = request_id.hex[:8]
+        job_id = f"{base_name}_{unique_suffix}"
+
+        logger.info(f"[Request {request_id}] Successfully transcribed media file '{mediaFile.filename}'. Returning transcript content.")
+        response_content = {
+            "transcript_content": transcript_content,
+            "original_filename": mediaFile.filename,
+            "job_id": job_id,
+            "transcript_filename": os.path.basename(local_transcript_path)
+        }
+        return JSONResponse(content=response_content)
+
+    except HTTPException as http_exc:
+        logger.error(f"[Request {request_id}] HTTP Exception during processing file {mediaFile.filename}: {http_exc.status_code} - {http_exc.detail}", exc_info=False)
+        raise http_exc
+    except Exception as e:
+        logger.error(f"[Request {request_id}] Unexpected error processing file {mediaFile.filename}: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"An unexpected server error occurred processing the file.")
+    finally:
+        logger.info(f"[Request {request_id}] Starting final cleanup for temporary resources.")
+        await mediaFile.close()
+        logger.debug(f"[Request {request_id}] Closed uploaded file handle for {mediaFile.filename}.")
+
+        if temp_base_dir and os.path.exists(temp_base_dir):
+            try:
+                shutil.rmtree(temp_base_dir)
+                logger.info(f"[Request {request_id}] Successfully removed temporary directory: {temp_base_dir}")
+            except OSError as e:
+                logger.error(f"[Request {request_id}] Error removing temporary directory {temp_base_dir}: {e}", exc_info=True)
+        else:
+             logger.debug(f"[Request {request_id}] No base temporary directory to clean up or it was already removed.")
+
+        logger.info(f"[Request {request_id}] Final cleanup complete.")
\ No newline at end of file
diff --git a/nilai-audio/src/nilai_audio/py.typed b/nilai-audio/src/nilai_audio/py.typed
new file mode 100644
index 00000000..e69de29b
diff --git a/nilai-audio/src/nilai_audio/summarization.py b/nilai-audio/src/nilai_audio/summarization.py
new file mode 100644
index 00000000..4be60c03
--- /dev/null
+++ b/nilai-audio/src/nilai_audio/summarization.py
@@ -0,0 +1,179 @@
+import os
+import httpx
+import json
+import traceback
+import logging
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Updated Configuration ---
+# Point directly to the specific vLLM service container for summarization
+# Service name from docker-compose.llama-1b-gpu.yml
+SUMMARIZATION_VLLM_HOST = os.environ.get("SUMMARIZATION_VLLM_HOST", "llama_1b_gpu")
+# Internal port the vLLM service listens on
+SUMMARIZATION_VLLM_PORT = os.environ.get("SUMMARIZATION_VLLM_PORT", "8000")
+VLLM_URL = f"http://{SUMMARIZATION_VLLM_HOST}:{SUMMARIZATION_VLLM_PORT}"
+# Standard OpenAI-compatible endpoint provided by vLLM
+VLLM_CHAT_ENDPOINT = f"{VLLM_URL}/v1/chat/completions"
+
+# Model name expected by the vLLM payload.
+# *** IMPORTANT: This MUST match the model loaded by the target vLLM container ***
+# (e.g., "meta-llama/Llama-3.2-1B-Instruct" for the llama_1b_gpu service)
+# Ensure this env var is set correctly for the audio_gpu service.
+MODEL_NAME = os.environ.get("SUMMARIZATION_MODEL_NAME", "meta-llama/Llama-3.2-1B-Instruct")
+
+# Timeout for requests directly to the vLLM service
+VLLM_TIMEOUT = int(os.environ.get("VLLM_TIMEOUT", "300"))
+
+# --- Constants ---
+SUMMARIES_DIR = "summaries"
+DEFAULT_MAX_SUMMARY_TOKENS = 2064
+
+def read_transcript(file_path: str):
+    """Reads transcript content from a file."""
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read()
+    except FileNotFoundError:
+        logger.error(f"Transcript file not found at {file_path}")
+        return None
+    except Exception as e:
+        logger.error(f"Error reading transcript file {file_path}: {e}", exc_info=True)
+        return None
+
+def save_summary(summary_text: str, original_transcript_filename: str, model_name_used: str):
+    """Saves the generated summary to a file."""
+    if not os.path.exists(SUMMARIES_DIR):
+        try:
+            os.makedirs(SUMMARIES_DIR)
+        except OSError as e:
+            logger.error(f"Error creating summaries directory {SUMMARIES_DIR}: {e}")
+            return None
+
+    model_short_name = model_name_used.replace('/', '_').replace('\\', '_')
+    base_name = os.path.splitext(original_transcript_filename)[0]
+    base_name = os.path.basename(base_name)
+    summary_filename = f"{base_name}_summary_{model_short_name}.txt"
+    summary_path = os.path.join(SUMMARIES_DIR, summary_filename)
+
+    try:
+        with open(summary_path, 'w', encoding='utf-8') as f:
+            f.write(summary_text)
+        logger.info(f"Summary saved to {summary_path}")
+        return summary_path
+    except Exception as e:
+        logger.error(f"Error saving summary to {summary_path}: {e}", exc_info=True)
+        return None
+
+async def summarize_transcript(
+    system_prompt: str,
+    transcript_content: str,
+    max_new_tokens: int = DEFAULT_MAX_SUMMARY_TOKENS
+) -> str:
+    """
+    Sends a transcript directly to the specified vLLM service container for summarization.
+    """
+    if not transcript_content:
+        logger.error("Summarization failed: Transcript content is empty.")
+        return "Error: Could not generate summary - Transcript content is empty."
+    if not system_prompt:
+         logger.error("Summarization failed: System prompt is empty.")
+         return "Error: Could not generate summary - System prompt is empty."
+
+    logger.info(f"Attempting summarization via vLLM Service at {VLLM_CHAT_ENDPOINT} using model {MODEL_NAME}")
+    logger.info(f"System Prompt (first 100 chars): {system_prompt[:100]}...")
+    logger.info(f"Transcript Length: {len(transcript_content)}")
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"Transcript:\n{transcript_content}\n\nSummary:"}
+    ]
+
+    # Standard OpenAI-compatible payload for vLLM
+    payload = {
+        "model": MODEL_NAME, # Must match the model loaded in the target vLLM container
+        "messages": messages,
+        "max_tokens": max_new_tokens,
+        "temperature": 0.7,
+        "top_p": 0.8,
+        "repetition_penalty": 1.05,
+        "stream": False
+    }
+
+    raw_response_text = ""
+    response = None
+    try:
+        async with httpx.AsyncClient(timeout=float(VLLM_TIMEOUT)) as client:
+            logger.info(f"Sending payload to vLLM Service (Payload start): {json.dumps(payload)[:200]}...")
+            # No headers needed for direct internal call typically
+            response = await client.post(VLLM_CHAT_ENDPOINT, json=payload)
+            logger.info(f"vLLM Service Response Status Code: {response.status_code}")
+
+            try:
+                raw_response_text = response.text
+                logger.info(f"vLLM Service Raw Response Body (first 500 chars): {raw_response_text[:500]}")
+            except Exception as log_err:
+                logger.warning(f"Could not log raw response body: {log_err}")
+
+            response.raise_for_status()
+
+        result = response.json()
+
+        # Standard OpenAI-compatible response parsing
+        if "choices" in result and len(result["choices"]) > 0:
+            first_choice = result["choices"][0]
+            if "message" in first_choice:
+                message = first_choice["message"]
+                if "content" in message:
+                    summary = message["content"]
+                    if summary:
+                        logger.info("Summary received successfully from vLLM service.")
+                        logger.info(f"Returning summary (length {len(summary)}): '{summary[:100]}...'")
+                        return summary.strip()
+                    else:
+                        logger.warning("vLLM service response choice contained an empty 'content' string.")
+                        return "Error: vLLM service returned an empty summary content."
+                else:
+                     logger.warning("vLLM service response 'message' object missing 'content' key.")
+                     return "Error: vLLM service response format unexpected (missing content)."
+            else:
+                 logger.warning("vLLM service response 'choice' object missing 'message' key.")
+                 return "Error: vLLM service response format unexpected (missing message)."
+        else:
+            logger.warning(f"vLLM service response missing 'choices' key or 'choices' list is empty. Response: {result}")
+            return "Error: Unexpected response structure from vLLM service (missing choices)."
+
+    except httpx.TimeoutException as e:
+        logger.error(f"Timeout error connecting to vLLM Service at {VLLM_CHAT_ENDPOINT} after {VLLM_TIMEOUT} seconds.", exc_info=True)
+        return f"Error: Timeout connecting to vLLM service ({e})"
+    except httpx.ConnectError as e:
+        logger.error(f"Connection error connecting to vLLM Service at {VLLM_CHAT_ENDPOINT}. Is the '{SUMMARIZATION_VLLM_HOST}' service running and on the same network?", exc_info=True)
+        return f"Error: Could not connect to vLLM service ({e})"
+    except httpx.RequestError as e:
+        logger.error(f"HTTP Request error during vLLM service communication: {e}", exc_info=True)
+        return f"Error connecting to vLLM service: {e}"
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP Status Error from vLLM service: {e.response.status_code}", exc_info=False)
+        logger.error(f"vLLM Service Error Response Body: {e.response.text}")
+        return f"Error from vLLM service ({e.response.status_code}): {e.response.text[:500]}"
+    except json.JSONDecodeError as e:
+        logger.error(f"Failed to decode JSON response from vLLM service. Status: {response.status_code if response else 'N/A'}. Response text: {raw_response_text[:500]}", exc_info=True)
+        return f"Error: Could not parse response from vLLM service (Invalid JSON). Status: {response.status_code if response else 'N/A'}"
+    except Exception as e:
+        logger.error(f"An unexpected error occurred during vLLM service communication: {e}", exc_info=True)
+        return f"Error during summary generation via vLLM service: {e}"
+
+# --- Example Usage (Commented out - for reference) ---
+# import asyncio
+# async def main():
+#     transcript_path = "path/to/your/transcript_file.txt"
+#     transcript = read_transcript(transcript_path)
+#     if transcript:
+#         prompt = "Provide a concise summary."
+#         summary = await summarize_transcript(prompt, transcript)
+#         print(summary)
+#         if not summary.startswith("Error:"):
+#            save_summary(summary, os.path.basename(transcript_path), MODEL_NAME)
+# if __name__ == "__main__":
+#      asyncio.run(main())
\ No newline at end of file
diff --git a/nilai-audio/src/nilai_audio/transcription.py b/nilai-audio/src/nilai_audio/transcription.py
new file mode 100644
index 00000000..c4282dea
--- /dev/null
+++ b/nilai-audio/src/nilai_audio/transcription.py
@@ -0,0 +1,125 @@
+import whisperx
+import os
+import torch
+import datetime
+import traceback
+from typing import Optional
+
+def format_timestamp(seconds):
+    td = datetime.timedelta(seconds=seconds)
+    minutes, seconds = divmod(td.seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    milliseconds = int(td.microseconds / 1000)
+    return f"{hours:02}:{minutes:02}:{seconds:02}.{milliseconds:03}"
+
+def transcribe_audio_logic(
+    audio_file_path: str,
+    model,
+    align_model_tuple,
+    diarize_pipeline,
+    device: str,
+    batch_size: int,
+    output_dir: str,
+    num_speakers: Optional[int] = None
+):
+    print(f"--- Starting Transcription Logic for: {audio_file_path} ---")
+    print(f"Using device: {device}, Batch Size: {batch_size}")
+    if num_speakers is not None and num_speakers > 0:
+         print(f"Speaker number hint provided: {num_speakers}")
+    else:
+         print("No speaker number hint, using auto-detection.")
+    os.makedirs(output_dir, exist_ok=True)
+    transcript_path = None
+
+    try:
+        print("Loading audio...")
+        audio = whisperx.load_audio(audio_file_path)
+        print("Audio loaded.")
+
+        print("Starting transcription...")
+        result = model.transcribe(audio, batch_size=batch_size)
+        detected_language = result.get("language", "unknown")
+        print(f"Transcription complete. Detected language: {detected_language}")
+
+        if align_model_tuple:
+            model_a, metadata = align_model_tuple
+            print(f"Aligning transcript using language: {metadata.get('language_code', 'default')}...")
+            try:
+                result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)
+                print("Alignment complete.")
+            except Exception as align_error:
+                print(f"Warning: Alignment failed: {align_error}. Proceeding without alignment.")
+                traceback.print_exc()
+        else:
+            print("Skipping alignment as alignment model was not loaded.")
+            if "segments" not in result:
+                print("Warning: 'segments' key missing from transcription result even without alignment.")
+                return None
+
+        print("Performing diarization...")
+        try:
+            diarization_args = {}
+            if num_speakers is not None and num_speakers > 0:
+                print(f"--> Attempting diarization with fixed number of speakers: {num_speakers}")
+                diarization_args["min_speakers"] = num_speakers
+                diarization_args["max_speakers"] = num_speakers
+            else:
+                print("--> Attempting diarization with automatic speaker number detection.")
+
+            diarize_segments = diarize_pipeline(audio, **diarization_args)
+            print("Diarization complete.")
+
+            print("Assigning speakers to words...")
+            if align_model_tuple and 'segments' in result and result['segments'] and 'words' in result['segments'][0]:
+                 result = whisperx.assign_word_speakers(diarize_segments, result)
+                 print("Speaker assignment complete (using word timings).")
+            elif 'segments' in result:
+                 try:
+                     print("Warning: Word-level alignment missing or failed. Speaker assignment might be less accurate or skipped.")
+                 except AttributeError:
+                      print("Warning: Could not find assign_speakers fallback method. Proceeding without explicit segment speaker assignment.")
+                 print("Speaker assignment might be incomplete (used segment timings - potential fallback).")
+            else:
+                 print("Warning: Cannot assign speakers - 'segments' key is missing.")
+
+        except Exception as diarize_error:
+            print(f"Warning: Diarization or speaker assignment failed: {diarize_error}")
+            traceback.print_exc()
+
+        base_filename = os.path.splitext(os.path.basename(audio_file_path))[0]
+        transcript_filename = f"{base_filename}_transcript_diarized.txt"
+        transcript_path = os.path.join(output_dir, transcript_filename)
+
+        print(f"Saving transcript to: {transcript_path}")
+        with open(transcript_path, "w", encoding="utf-8") as f:
+            f.write(f"Transcript for: {os.path.basename(audio_file_path)}\n")
+            f.write(f"Detected Language: {result.get('language', 'N/A')}\n")
+            f.write("-" * 30 + "\n\n")
+
+            for segment in result.get("segments", []):
+                start_time = format_timestamp(segment['start'])
+                end_time = format_timestamp(segment.get('end', segment['start'] + 0.1))
+
+                speaker = "SPEAKER_UNKNOWN"
+                if 'speaker' in segment:
+                    speaker = segment['speaker']
+                elif 'words' in segment and segment['words'] and 'speaker' in segment['words'][0]:
+                    speaker = segment['words'][0].get('speaker', 'SPEAKER_UNKNOWN')
+
+                text = segment.get('text', '').strip()
+                f.write(f"[{start_time} --> {end_time}] {speaker}: {text}\n")
+
+        print("Transcript saved successfully.")
+        return transcript_path
+
+    except Exception as e:
+        print(f"\n--- ERROR during transcription process for {audio_file_path} ---")
+        print(f"An error occurred: {e}")
+        traceback.print_exc()
+        if transcript_path and os.path.exists(transcript_path):
+            try:
+                os.remove(transcript_path)
+                print(f"Removed incomplete transcript file: {transcript_path}")
+            except OSError as rm_err:
+                print(f"Error removing incomplete transcript file {transcript_path}: {rm_err}")
+        return None
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 07d33d85..950c949a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,7 +39,7 @@ build-backend = "setuptools.build_meta"
 find = { include = ["nilai"] }
 
 [tool.uv.workspace]
-members = ["nilai-models", "nilai-api", "packages/nilai-common", "packages/verifier"]
+members = ["nilai-models", "nilai-api", "packages/nilai-common", "packages/verifier", "nilai-audio"]
 
 [tool.uv.sources]
 nilai-common = { workspace = true }
diff --git a/uv.lock b/uv.lock
index bd2a6c0e..5b0735f4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,6 +6,7 @@ requires-python = ">=3.12"
 members = [
     "nilai",
     "nilai-api",
+    "nilai-audio",
     "nilai-common",
     "nilai-models",
     "verifier",
@@ -122,6 +123,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
+
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -145,6 +152,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c9/7f/09065fd9e27da0eda08b4d6897f1c13535066174cc023af248fc2a8d5e5a/asn1crypto-1.5.1-py2.py3-none-any.whl", hash = "sha256:db4e40728b728508912cbb3d44f19ce188f218e9eba635821bb4b68564f8fd67", size = 105045 },
 ]
 
+[[package]]
+name = "asteroid-filterbanks"
+version = "0.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "torch" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/90/fa/5c2be1f96dc179f83cdd3bb267edbd1f47d08f756785c016d5c2163901a7/asteroid-filterbanks-0.4.0.tar.gz", hash = "sha256:415f89d1dcf2b13b35f03f7a9370968ac4e6fa6800633c522dac992b283409b9", size = 24599 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c5/7c/83ff6046176a675e6a1e8aeefed8892cd97fe7c46af93cc540d1b24b8323/asteroid_filterbanks-0.4.0-py3-none-any.whl", hash = "sha256:4932ac8b6acc6e08fb87cbe8ece84215b5a74eee284fe83acf3540a72a02eaf5", size = 29912 },
+]
+
 [[package]]
 name = "asyncpg"
 version = "0.30.0"
@@ -190,6 +211,31 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e3/71/8dcec996ea8cc882cec9cace91ae1b630a226b88b0f04ab2ffa778f565ad/authlib-1.5.2-py2.py3-none-any.whl", hash = "sha256:8804dd4402ac5e4a0435ac49e0b6e19e395357cfa632a3f624dcb4f6df13b4b1", size = 232055 },
 ]
 
+[[package]]
+name = "av"
+version = "14.3.0"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1b/ff/092b5bba046a9fd7324d9eee498683ee9e410715d21eff9d3db92dd14910/av-14.3.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:8250680e4e17c404008005b60937248712e9c621689bbc647577d8e2eaa00a66", size = 20004033 },
+    { url = "https://files.pythonhosted.org/packages/90/b8/fa4fb7d5f1c6299c2f691d527c47a717155acb9ff9f3c30358d7d50d60e1/av-14.3.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:349aa6ef529daaede95f37e9825c6e36fddb15906b27938d9e22dcdca2e1f648", size = 23804484 },
+    { url = "https://files.pythonhosted.org/packages/79/f3/230b2d05a918ed4f9390f8d7ca766250662e6200d77453852e85cd854291/av-14.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f953a9c999add37b953cb3ad4ef3744d3d4eee50ef1ffeb10cb1f2e6e2cbc088", size = 33727815 },
+    { url = "https://files.pythonhosted.org/packages/95/f8/593ab784116356e8eb00e1f1b3ab2383c59c1ef40d6bcf19be7cb4679237/av-14.3.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1eaefb47d2ee178adfcedb9a70678b1a340a6670262d06ffa476da9c7d315aef", size = 32307276 },
+    { url = "https://files.pythonhosted.org/packages/40/ff/2237657852dac32052b7401da6bc7fc23127dc7a1ccbb23d4c640c8ea95b/av-14.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e3b7ca97af1eb3e41e7971a0eb75c1375f73b89ff54afb6d8bf431107160855", size = 35439982 },
+    { url = "https://files.pythonhosted.org/packages/01/f7/e4561cabd16e96a482609211eb8d260a720f222e28bdd80e3af0bbc560a6/av-14.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e2a0404ac4bfa984528538fb7edeb4793091a5cc6883a473d13cb82c505b62e0", size = 36366758 },
+    { url = "https://files.pythonhosted.org/packages/ce/ee/7334ca271b71c394ef400a11b54b1d8d3eb28a40681b37c3a022d9dc59c8/av-14.3.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:2ceb45e998184231bcc99a14f91f4265d959e6b804fe9054728e9855214b2ad5", size = 34643022 },
+    { url = "https://files.pythonhosted.org/packages/db/4f/c692ee808a68aa2ec634a00ce084d3f68f28ab6ab7a847780974d780762d/av-14.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f87df669f49d5202f3933dc94e606353f5c5f9a709a1c0823b3f6d6333560bd7", size = 37448043 },
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