diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
deleted file mode 100644
index 36e62bdd0942c2ea7f7af0728d7e7bf35de7f363..0000000000000000000000000000000000000000
--- a/.devcontainer/devcontainer.json
+++ /dev/null
@@ -1,44 +0,0 @@
-// For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-docker-compose
-{
-	"name": "Existing Docker Compose (Extend)",
-
-	// Update the 'dockerComposeFile' list if you have more compose files or use different names.
-	// The .devcontainer/docker-compose.yml file contains any overrides you need/want to make.
-	"dockerComposeFile": [
-		"../docker-compose.yml",
-		"docker-compose.yml"
-	],
-
-	// The 'service' property is the name of the service for the container that VS Code should
-	// use. Update this value and .devcontainer/docker-compose.yml to the real service name.
-	"service": "whisper-asr-webservice",
-
-	// The optional 'workspaceFolder' property is the path VS Code should open by default when
-	// connected. This is typically a file mount in .devcontainer/docker-compose.yml
-	"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
-
-	// "overrideCommand": "/bin/sh -c 'while sleep 1000; do :; done'"
-	"overrideCommand": true
-
-	// Features to add to the dev container. More info: https://containers.dev/features.
-	// "features": {},
-
-	// Use 'forwardPorts' to make a list of ports inside the container available locally.
-	// "forwardPorts": [],
-
-	// Uncomment the next line if you want start specific services in your Docker Compose config.
-	// "runServices": [],
-
-	// Uncomment the next line if you want to keep your containers running after VS Code shuts down.
-	// "shutdownAction": "none",
-
-	// Uncomment the next line to run commands after the container is created.
-	// "postCreateCommand": "cat /etc/os-release",
-
-	// Configure tool-specific properties.
-	// "customizations": {},
-
-	// Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root.
-	// "remoteUser": "devcontainer"
-}
diff --git a/.devcontainer/docker-compose.yml b/.devcontainer/docker-compose.yml
deleted file mode 100644
index c3e29c5e06776f94279b8abf25a044dfa2bd00f7..0000000000000000000000000000000000000000
--- a/.devcontainer/docker-compose.yml
+++ /dev/null
@@ -1,30 +0,0 @@
-version: '3.4'
-services:
-  # Update this to the name of the service you want to work with in your docker-compose.yml file
-  whisper-asr-webservice:
-    # Uncomment if you want to override the service's Dockerfile to one in the .devcontainer 
-    # folder. Note that the path of the Dockerfile and context is relative to the *primary* 
-    # docker-compose.yml file (the first in the devcontainer.json "dockerComposeFile"
-    # array). The sample below assumes your primary file is in the root of your project.
-    #
-    # build:
-    #   context: .
-    #   dockerfile: .devcontainer/Dockerfile
-    env_file: .devcontainer/dev.env
-    environment:
-      ASR_ENGINE: ${ASR_ENGINE}
-      HF_TOKEN: ${HF_TOKEN}
-
-    volumes:
-      # Update this to wherever you want VS Code to mount the folder of your project
-      - ..:/workspaces:cached
-
-    # Uncomment the next four lines if you will use a ptrace-based debugger like C++, Go, and Rust.
-    # cap_add:
-    #   - SYS_PTRACE
-    # security_opt:
-    #   - seccomp:unconfined
-
-    # Overrides default command so things don't shut down after the process ends.
-    command: sleep infinity
- 
diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml
index 574ea12319ae7ea5771ef380c2bdfdfb6adaa31e..571d2b8dd956c95e2ba76d5a77141ea7f188e25a 100644
--- a/.github/workflows/docker-publish.yml
+++ b/.github/workflows/docker-publish.yml
@@ -12,7 +12,7 @@ env:
   REPO_NAME: ${{secrets.REPO_NAME}}
 jobs:
   build:
-    runs-on: [self-hosted, ubuntu-latest]
+    runs-on: ubuntu-latest
     strategy:
       matrix:
         include:
@@ -22,10 +22,6 @@ jobs:
             tag_extension: -gpu
             platforms: linux/amd64
     steps:
-    - name: Remove unnecessary files
-      run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
     - name: Checkout
       uses: actions/checkout@v3
     - name: Set up QEMU
diff --git a/.gitignore b/.gitignore
index 3a43ec4111fae3e500b9474f717b689d3d77dfbd..35e58693279343c7e00e2efa392a6bf0e95b9973 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,5 +42,3 @@ pip-wheel-metadata
 poetry/core/*
 
 public
-
-.devcontainer/dev.env
diff --git a/Dockerfile b/Dockerfile
index 1fe4306641dc3306e0b6f76c4c1821c9030df8f9..cda99b8bad6aad3610428d6dd53035805794c8d2 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,12 +4,6 @@ FROM swaggerapi/swagger-ui:v5.9.1 AS swagger-ui
 
 FROM python:3.10-bookworm
 
-RUN export DEBIAN_FRONTEND=noninteractive \
-    && apt-get -qq update \
-    && apt-get -qq install --no-install-recommends \
-    libsndfile1 \
-    && rm -rf /var/lib/apt/lists/*
-
 ENV POETRY_VENV=/app/.venv
 
 RUN python3 -m venv $POETRY_VENV \
@@ -28,11 +22,6 @@ COPY --from=swagger-ui /usr/share/nginx/html/swagger-ui-bundle.js swagger-ui-ass
 RUN poetry config virtualenvs.in-project true
 RUN poetry install
 
-RUN $POETRY_VENV/bin/pip install pandas transformers nltk pyannote.audio
-RUN git clone --depth 1 https://github.com/m-bain/whisperX.git \
-    && cd whisperX \
-    && $POETRY_VENV/bin/pip install -e .
-
 EXPOSE 9000
 
 ENTRYPOINT ["whisper-asr-webservice"]
diff --git a/Dockerfile.gpu b/Dockerfile.gpu
index 6e3630c3543f694e8fef4a3df4ef03f49c6853fc..414c92c0e1392670d96ac3701e2845add3ff3ba7 100644
--- a/Dockerfile.gpu
+++ b/Dockerfile.gpu
@@ -6,12 +6,6 @@ FROM nvidia/cuda:12.6.3-cudnn-runtime-ubuntu22.04
 
 ENV PYTHON_VERSION=3.10
 
-RUN export DEBIAN_FRONTEND=noninteractive \
-    && apt-get -qq update \
-    && apt-get -qq install --no-install-recommends \
-    libsndfile1 \
-    && rm -rf /var/lib/apt/lists/*
-
 ENV POETRY_VENV=/app/.venv
 
 RUN export DEBIAN_FRONTEND=noninteractive \
@@ -47,11 +41,6 @@ COPY --from=swagger-ui /usr/share/nginx/html/swagger-ui-bundle.js swagger-ui-ass
 RUN poetry install
 RUN $POETRY_VENV/bin/pip install torch==2.6.0 --index-url https://download.pytorch.org/whl/cu121
 
-RUN $POETRY_VENV/bin/pip install pandas transformers nltk pyannote.audio
-RUN git clone --depth 1 https://github.com/m-bain/whisperX.git \
-    && cd whisperX \
-    && $POETRY_VENV/bin/pip install -e .
-
 EXPOSE 9000
 
 CMD whisper-asr-webservice
diff --git a/README.md b/README.md
index 21dcb00f1e5d8b9df8761be65c5b0e7c331c27f7..be4208276eb0b09446c62b27e2165b09518797c6 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,9 @@
 ![Build](https://img.shields.io/github/actions/workflow/status/ahmetoner/whisper-asr-webservice/docker-publish.yml.svg)
 ![Licence](https://img.shields.io/github/license/ahmetoner/whisper-asr-webservice.svg)
 
-# Whisper ASR Webservice
+# Whisper ASR Box
 
-Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitask model that can perform multilingual speech recognition as well as speech translation and language identification. For more details: [github.com/openai/whisper](https://github.com/openai/whisper/)
+Whisper ASR Box is a general-purpose speech recognition toolkit. Whisper Models are trained on a large dataset of diverse audio and is also a multitask model that can perform multilingual speech recognition as well as speech translation and language identification.
 
 ## Features
 
@@ -19,24 +19,73 @@ Current release (v1.7.1) supports following whisper models:
 
 ### CPU
 
-```sh
-docker run -d -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest
+```shell
+docker run -d -p 9000:9000 \
+  -e ASR_MODEL=base \
+  -e ASR_ENGINE=openai_whisper \
+  onerahmet/openai-whisper-asr-webservice:latest
 ```
 
 ### GPU
 
-```sh
-docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest-gpu
+```shell
+docker run -d --gpus all -p 9000:9000 \
+  -e ASR_MODEL=base \
+  -e ASR_ENGINE=openai_whisper \
+  onerahmet/openai-whisper-asr-webservice:latest-gpu
 ```
 
-for more information:
+#### Cache
 
-- [Documentation/Run](https://ahmetoner.github.io/whisper-asr-webservice/run)
-- [Docker Hub](https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice)
+To reduce container startup time by avoiding repeated downloads, you can persist the cache directory:
+
+```shell
+docker run -d -p 9000:9000 \
+  -v $PWD/cache:/root/.cache/ \
+  onerahmet/openai-whisper-asr-webservice:latest
+```
+
+## Key Features
+
+- Multiple ASR engines support (OpenAI Whisper, Faster Whisper, WhisperX)
+- Multiple output formats (text, JSON, VTT, SRT, TSV)
+- Word-level timestamps support
+- Voice activity detection (VAD) filtering
+- Speaker diarization (with WhisperX)
+- FFmpeg integration for broad audio/video format support
+- GPU acceleration support
+- Configurable model loading/unloading
+- REST API with Swagger documentation
+
+## Environment Variables
+
+Key configuration options:
+
+- `ASR_ENGINE`: Engine selection (openai_whisper, faster_whisper, whisperx)
+- `ASR_MODEL`: Model selection (tiny, base, small, medium, large-v3, etc.)
+- `ASR_MODEL_PATH`: Custom path to store/load models
+- `ASR_DEVICE`: Device selection (cuda, cpu)
+- `MODEL_IDLE_TIMEOUT`: Timeout for model unloading
 
 ## Documentation
 
-Explore the documentation by clicking [here](https://ahmetoner.github.io/whisper-asr-webservice).
+For complete documentation, visit:
+[https://ahmetoner.github.io/whisper-asr-webservice](https://ahmetoner.github.io/whisper-asr-webservice)
+
+## Development
+
+```shell
+# Install poetry
+pip3 install poetry
+
+# Install dependencies
+poetry install
+
+# Run service
+poetry run whisper-asr-webservice --host 0.0.0.0 --port 9000
+```
+
+After starting the service, visit `http://localhost:9000` or `http://0.0.0.0:9000` in your browser to access the Swagger UI documentation and try out the API endpoints.
 
 ## Credits
 
diff --git a/app/asr_models/asr_model.py b/app/asr_models/asr_model.py
index 8ec9890cbf9e02aa08b757fb2bf453a56eaaa92d..8c625ecf2e8717f5b4f8776db47ede1a71a2ff9f 100644
--- a/app/asr_models/asr_model.py
+++ b/app/asr_models/asr_model.py
@@ -15,8 +15,6 @@ class ASRModel(ABC):
     """
 
     model = None
-    diarize_model = None  # used for WhisperX
-    x_models = dict()  # used for WhisperX
     model_lock = Lock()
     last_activity_time = time.time()
 
@@ -75,6 +73,4 @@ class ASRModel(ABC):
         torch.cuda.empty_cache()
         gc.collect()
         self.model = None
-        self.diarize_model = None
-        self.x_models = dict()
         print("Model unloaded due to timeout")
diff --git a/app/asr_models/faster_whisper_engine.py b/app/asr_models/faster_whisper_engine.py
index 3cf21f8e92a71005777ad529e8c777350d143cf7..d7c3c44fb9352857ab5222fd59075a80e5e832ad 100644
--- a/app/asr_models/faster_whisper_engine.py
+++ b/app/asr_models/faster_whisper_engine.py
@@ -38,7 +38,8 @@ class FasterWhisperASR(ASRModel):
         self.last_activity_time = time.time()
 
         with self.model_lock:
-            if self.model is None: self.load_model()
+            if self.model is None:
+                self.load_model()
 
         options_dict = {"task": task}
         if language:
@@ -91,7 +92,5 @@ class FasterWhisperASR(ASRModel):
             WriteTSV(ResultWriter).write_result(result, file=file)
         elif output == "json":
             WriteJSON(ResultWriter).write_result(result, file=file)
-        elif output == "txt":
-            WriteTXT(ResultWriter).write_result(result, file=file)
         else:
-            return "Please select an output method!"
+            WriteTXT(ResultWriter).write_result(result, file=file)
diff --git a/app/asr_models/mbain_whisperx_engine.py b/app/asr_models/mbain_whisperx_engine.py
index 0e104bf6298e36e0601cd3d8ffeb2c425f0d8c02..87494a3c788dc7a31c2c548e948a901d8041919f 100644
--- a/app/asr_models/mbain_whisperx_engine.py
+++ b/app/asr_models/mbain_whisperx_engine.py
@@ -1,27 +1,41 @@
-from typing import BinaryIO, Union
+import time
 from io import StringIO
-import whisperx
+from threading import Thread
+from typing import BinaryIO, Union
+
 import whisper
-from whisperx.utils import SubtitlesWriter, ResultWriter
+import whisperx
+from whisperx.utils import ResultWriter, SubtitlesWriter, WriteJSON, WriteSRT, WriteTSV, WriteTXT, WriteVTT
 
 from app.asr_models.asr_model import ASRModel
 from app.config import CONFIG
-from app.utils import WriteTXT, WriteSRT, WriteVTT, WriteTSV, WriteJSON
 
 
 class WhisperXASR(ASRModel):
     def __init__(self):
-        self.x_models = dict()
+        super().__init__()
+        self.model = {
+            'whisperx': None,
+            'diarize_model': None,
+            'align_model': {}
+        }
 
     def load_model(self):
-
         asr_options = {"without_timestamps": False}
-        self.model = whisperx.load_model(
-            CONFIG.MODEL_NAME, device=CONFIG.DEVICE, compute_type="float32", asr_options=asr_options
+        self.model['whisperx'] = whisperx.load_model(
+            CONFIG.MODEL_NAME,
+            device=CONFIG.DEVICE,
+            compute_type=CONFIG.MODEL_QUANTIZATION,
+            asr_options=asr_options
         )
 
         if CONFIG.HF_TOKEN != "":
-            self.diarize_model = whisperx.DiarizationPipeline(use_auth_token=CONFIG.HF_TOKEN, device=CONFIG.DEVICE)
+            self.model['diarize_model'] = whisperx.DiarizationPipeline(
+                use_auth_token=CONFIG.HF_TOKEN,
+                device=CONFIG.DEVICE
+            )
+
+        Thread(target=self.monitor_idleness, daemon=True).start()
 
     def transcribe(
         self,
@@ -34,39 +48,42 @@ class WhisperXASR(ASRModel):
         options: Union[dict, None],
         output,
     ):
+        self.last_activity_time = time.time()
+        with self.model_lock:
+            if self.model is None:
+                self.load_model()
+
         options_dict = {"task": task}
         if language:
             options_dict["language"] = language
         if initial_prompt:
             options_dict["initial_prompt"] = initial_prompt
         with self.model_lock:
-            if self.model is None:
-                self.load_model()
-            result = self.model.transcribe(audio, **options_dict)
+            result = self.model['whisperx'].transcribe(audio, **options_dict)
+            language = result["language"]
 
         # Load the required model and cache it
         # If we transcribe models in many different languages, this may lead to OOM propblems
-        if result["language"] in self.x_models:
-            model_x, metadata = self.x_models[result["language"]]
+        if result["language"] in self.model['align_model']:
+            model_x, metadata = self.model['align_model'][result["language"]]
         else:
-            self.x_models[result["language"]] = whisperx.load_align_model(
+            self.model['align_model'][result["language"]] = whisperx.load_align_model(
                 language_code=result["language"], device=CONFIG.DEVICE
             )
-            model_x, metadata = self.x_models[result["language"]]
+            model_x, metadata = self.model['align_model'][result["language"]]
 
         # Align whisper output
         result = whisperx.align(
             result["segments"], model_x, metadata, audio, CONFIG.DEVICE, return_char_alignments=False
         )
 
-        if options.get("diarize", False):
-            if CONFIG.HF_TOKEN == "":
-                print("Warning! HF_TOKEN is not set. Diarization may not work as expected.")
+        if options.get("diarize", False) and CONFIG.HF_TOKEN != "":
             min_speakers = options.get("min_speakers", None)
             max_speakers = options.get("max_speakers", None)
             # add min/max number of speakers if known
-            diarize_segments = self.diarize_model(audio, min_speakers, max_speakers)
+            diarize_segments = self.model['diarize_model'](audio, min_speakers, max_speakers)
             result = whisperx.assign_word_speakers(diarize_segments, result)
+        result["language"] = language
 
         output_file = StringIO()
         self.write_result(result, output_file, output)
@@ -91,21 +108,19 @@ class WhisperXASR(ASRModel):
         return detected_lang_code
 
     def write_result(self, result: dict, file: BinaryIO, output: Union[str, None]):
+        default_options = {
+            "max_line_width": CONFIG.SUBTITLE_MAX_LINE_WIDTH,
+            "max_line_count": CONFIG.SUBTITLE_MAX_LINE_COUNT,
+            "highlight_words": CONFIG.SUBTITLE_HIGHLIGHT_WORDS
+        }
+
         if output == "srt":
-            if CONFIG.HF_TOKEN != "":
-                WriteSRT(SubtitlesWriter).write_result(result, file=file, options={})
-            else:
-                WriteSRT(ResultWriter).write_result(result, file=file, options={})
+            WriteSRT(SubtitlesWriter).write_result(result, file=file, options=default_options)
         elif output == "vtt":
-            if CONFIG.HF_TOKEN != "":
-                WriteVTT(SubtitlesWriter).write_result(result, file=file, options={})
-            else:
-                WriteVTT(ResultWriter).write_result(result, file=file, options={})
+            WriteVTT(SubtitlesWriter).write_result(result, file=file, options=default_options)
         elif output == "tsv":
-            WriteTSV(ResultWriter).write_result(result, file=file, options={})
+            WriteTSV(ResultWriter).write_result(result, file=file, options=default_options)
         elif output == "json":
-            WriteJSON(ResultWriter).write_result(result, file=file, options={})
-        elif output == "txt":
-            WriteTXT(ResultWriter).write_result(result, file=file, options={})
+            WriteJSON(ResultWriter).write_result(result, file=file, options=default_options)
         else:
-            return 'Please select an output method!'
+            WriteTXT(ResultWriter).write_result(result, file=file, options=default_options)
diff --git a/app/asr_models/openai_whisper_engine.py b/app/asr_models/openai_whisper_engine.py
index 205aff89080db8646e2361a2d9c46f6be4bcfe52..655d682bd26f12f9de67ed1b6fdd7253e718bd22 100644
--- a/app/asr_models/openai_whisper_engine.py
+++ b/app/asr_models/openai_whisper_engine.py
@@ -86,7 +86,5 @@ class OpenAIWhisperASR(ASRModel):
             WriteTSV(ResultWriter).write_result(result, file=file, options=options)
         elif output == "json":
             WriteJSON(ResultWriter).write_result(result, file=file, options=options)
-        elif output == "txt":
-            WriteTXT(ResultWriter).write_result(result, file=file, options=options)
         else:
-            return "Please select an output method!"
+            WriteTXT(ResultWriter).write_result(result, file=file, options=options)
diff --git a/app/config.py b/app/config.py
index 31779aa41621786e54cf9dbb6dceb6a6f32860c2..ffc8e6c2f3d77e92c846126484ea8ab5b36398b1 100644
--- a/app/config.py
+++ b/app/config.py
@@ -8,11 +8,13 @@ class CONFIG:
     Configuration class for ASR models.
     Reads environment variables for runtime configuration, with sensible defaults.
     """
-    # Determine the ASR engine ('faster_whisper' or 'openai_whisper')
+    # Determine the ASR engine ('faster_whisper', 'openai_whisper' or 'whisperx')
     ASR_ENGINE = os.getenv("ASR_ENGINE", "openai_whisper")
 
     # Retrieve Huggingface Token
     HF_TOKEN = os.getenv("HF_TOKEN", "")
+    if ASR_ENGINE == "whisperx" and HF_TOKEN == "":
+        print("You must set the HF_TOKEN environment variable to download the diarization model used by WhisperX.")
 
     # Determine the computation device (GPU or CPU)
     DEVICE = os.getenv("ASR_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")
@@ -38,3 +40,8 @@ class CONFIG:
 
     # Default sample rate for audio input. 16 kHz is commonly used in speech-to-text tasks.
     SAMPLE_RATE = int(os.getenv("SAMPLE_RATE", 16000))
+
+    # Subtitle output options for whisperx
+    SUBTITLE_MAX_LINE_WIDTH = int(os.getenv("SUBTITLE_MAX_LINE_WIDTH", 1000))
+    SUBTITLE_MAX_LINE_COUNT = int(os.getenv("SUBTITLE_MAX_LINE_COUNT", 2))
+    SUBTITLE_HIGHLIGHT_WORDS = os.getenv("SUBTITLE_HIGHLIGHT_WORDS", "false").lower() == "true"
diff --git a/app/factory/asr_model_factory.py b/app/factory/asr_model_factory.py
index 10588dd248a047a11ab052bdbc1f43c32cfda387..dbdc294f59c6b28e5fbd37b20eea87b80e03e284 100644
--- a/app/factory/asr_model_factory.py
+++ b/app/factory/asr_model_factory.py
@@ -1,7 +1,7 @@
 from app.asr_models.asr_model import ASRModel
 from app.asr_models.faster_whisper_engine import FasterWhisperASR
-from app.asr_models.openai_whisper_engine import OpenAIWhisperASR
 from app.asr_models.mbain_whisperx_engine import WhisperXASR
+from app.asr_models.openai_whisper_engine import OpenAIWhisperASR
 from app.config import CONFIG
 
 
diff --git a/app/utils.py b/app/utils.py
index 31c52fad18a4c9fa4ecadfae2497eb233b95b752..ddc8a99f475255eeaa0dfc6375604a58ff589644 100644
--- a/app/utils.py
+++ b/app/utils.py
@@ -1,7 +1,7 @@
 import json
 import os
-from dataclasses import asdict, is_dataclass
-from typing import TextIO, BinaryIO, Union
+from dataclasses import asdict
+from typing import BinaryIO, TextIO
 
 import ffmpeg
 import numpy as np
@@ -23,42 +23,14 @@ class ResultWriter:
         with open(output_path, "w", encoding="utf-8") as f:
             self.write_result(result, file=f)
 
-    def write_result(self, result: dict, file: TextIO, options: Union[dict, None]):
+    def write_result(self, result: dict, file: TextIO):
         raise NotImplementedError
-    
-    def format_segments_in_result(self, result: dict):
-        if "segments" in result:
-            # Check if result["segments"] is a list
-            if isinstance(result["segments"], list):
-                # Check if the list is empty
-                if not result["segments"]:
-                    # Handle the empty list case, you can choose to leave it as is or set it to an empty list
-                    pass
-                else:
-                    # Check if the first item in the list is a dataclass instance
-                    if is_dataclass(result["segments"][0]):
-                        result["segments"] = [asdict(segment) for segment in result["segments"]]
-                    # If it's already a list of dicts, leave it as is
-                    elif isinstance(result["segments"][0], dict):
-                        pass
-                    else:
-                        # Handle the case where the list contains neither dataclass instances nor dicts
-                        # You can choose to leave it as is or raise an error
-                        pass
-            elif isinstance(result["segments"], dict):
-                # If it's already a dict, leave it as is
-                pass
-            else:
-                # Handle the case where result["segments"] is neither a list nor a dict
-                # You can choose to leave it as is or raise an error
-                pass
-        return result
 
 
 class WriteTXT(ResultWriter):
     extension: str = "txt"
 
-    def write_result(self, result: dict, file: TextIO, options: Union[dict, None]):
+    def write_result(self, result: dict, file: TextIO):
         for segment in result["segments"]:
             print(segment.text.strip(), file=file, flush=True)
 
@@ -66,13 +38,12 @@ class WriteTXT(ResultWriter):
 class WriteVTT(ResultWriter):
     extension: str = "vtt"
 
-    def write_result(self, result: dict, file: TextIO, options: Union[dict, None]):
+    def write_result(self, result: dict, file: TextIO):
         print("WEBVTT\n", file=file)
-        result = self.format_segments_in_result(result)
         for segment in result["segments"]:
             print(
-                f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-                f"{segment['text'].strip().replace('-->', '->')}\n",
+                f"{format_timestamp(segment.start)} --> {format_timestamp(segment.end)}\n"
+                f"{segment.text.strip().replace('-->', '->')}\n",
                 file=file,
                 flush=True,
             )
@@ -81,15 +52,14 @@ class WriteVTT(ResultWriter):
 class WriteSRT(ResultWriter):
     extension: str = "srt"
 
-    def write_result(self, result: dict, file: TextIO, options: Union[dict, None]):
-        result = self.format_segments_in_result(result)
+    def write_result(self, result: dict, file: TextIO):
         for i, segment in enumerate(result["segments"], start=1):
             # write srt lines
             print(
                 f"{i}\n"
-                f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
-                f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
-                f"{segment['text'].strip().replace('-->', '->')}\n",
+                f"{format_timestamp(segment.start, always_include_hours=True, decimal_marker=',')} --> "
+                f"{format_timestamp(segment.end, always_include_hours=True, decimal_marker=',')}\n"
+                f"{segment.text.strip().replace('-->', '->')}\n",
                 file=file,
                 flush=True,
             )
@@ -107,20 +77,20 @@ class WriteTSV(ResultWriter):
 
     extension: str = "tsv"
 
-    def write_result(self, result: dict, file: TextIO, options: Union[dict, None]):
-        result = self.format_segments_in_result(result)
+    def write_result(self, result: dict, file: TextIO):
         print("start", "end", "text", sep="\t", file=file)
         for segment in result["segments"]:
-            print(round(1000 * segment["start"]), file=file, end="\t")
-            print(round(1000 * segment["end"]), file=file, end="\t")
-            print(segment["text"].strip().replace("\t", " "), file=file, flush=True)
+            print(round(1000 * segment.start), file=file, end="\t")
+            print(round(1000 * segment.end), file=file, end="\t")
+            print(segment.text.strip().replace("\t", " "), file=file, flush=True)
 
 
 class WriteJSON(ResultWriter):
     extension: str = "json"
 
-    def write_result(self, result: dict, file: TextIO, options: Union[dict, None]):
-        result = self.format_segments_in_result(result) 
+    def write_result(self, result: dict, file: TextIO):
+        if "segments" in result:
+            result["segments"] = [asdict(segment) for segment in result["segments"]]
         json.dump(result, file)
 
 
diff --git a/docker-compose.gpu.yml b/docker-compose.gpu.yml
index 82eb1c503378f968ee73fd3243d81657151d392b..a865ee56d48423aef26cf651d2192f49ae913db8 100644
--- a/docker-compose.gpu.yml
+++ b/docker-compose.gpu.yml
@@ -18,12 +18,7 @@ services:
       - "9000:9000"
     volumes:
       - ./app:/app/app
-      - cache-pip:/root/.cache/pip
-      - cache-poetry:/root/.cache/poetry
-      - cache-whisper:/root/.cache/whisper
+      - cache-whisper:/root/.cache
 
 volumes:
-  cache-pip:
-  cache-poetry:
   cache-whisper:
-  cache-faster-whisper:
diff --git a/docker-compose.yml b/docker-compose.yml
index fdf1addf8572c4892a80271b1d64e9d24d36d1c3..2fab6cbf04b893cf4c5af652f4382f85acc171a8 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -11,12 +11,7 @@ services:
       - "9000:9000"
     volumes:
       - ./app:/app/app
-      - cache-pip:/root/.cache/pip
-      - cache-poetry:/root/.cache/poetry
-      - cache-whisper:/root/.cache/whisper
+      - cache-whisper:/root/.cache
 
 volumes:
-  cache-pip:
-  cache-poetry:
   cache-whisper:
-  cache-faster-whisper:
diff --git a/docs/.overrides/main.html b/docs/.overrides/main.html
index fec62393a7dea9561cbfbe0532ca0d4b4b457319..94d9808cc760156cb7ab46e326c0267f3406125e 100644
--- a/docs/.overrides/main.html
+++ b/docs/.overrides/main.html
@@ -1,12 +1 @@
 {% extends "base.html" %}
-
-{% block announce %}
-
-For updates follow <strong>@ahmetoner</strong> on
-<a rel="me" href="https://github.com/ahmetoner">
-    <span class="twemoji github">
-      {% include ".icons/fontawesome/brands/github.svg" %}
-    </span>
-    <strong>GitHub</strong>
-</a>
-{% endblock %}
\ No newline at end of file
diff --git a/docs/build.md b/docs/build.md
index 96fa96c6fa2b51dc94f348818c29b00f2a63357e..6f315aadaa1f5afb9c7c6ef0690e6e01fe8ffc93 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -2,65 +2,60 @@
 
 Install poetry with following command:
 
-```sh
+```shell
 pip3 install poetry
 ```
 
-Install torch with following command:
-
-```sh
-# just for GPU:
-pip3 install torch==1.13.1+cu117 -f https://download.pytorch.org/whl/torch
-```
-
-### Run
+### Installation
 
 Install packages:
 
-```sh
+```shell
 poetry install
 ```
 
+!!! Note
+    By default, this will install the CPU version of PyTorch. For GPU support, you'll need to install the appropriate CUDA version of PyTorch separately:
+    ```shell
+    # For CUDA support (example for CUDA 11.8):
+    pip3 install torch==2.6.0 --index-url https://download.pytorch.org/whl/cu121
+    ```
+
+### Run
+
 Starting the Webservice:
 
-```sh
+```shell
 poetry run whisper-asr-webservice --host 0.0.0.0 --port 9000
 ```
 
 ### Build
 
-=== ":octicons-file-code-16: `Poetry`"
-
-    Build .whl package
-    
-    ```sh
-    poetry build
-    ```
 === ":octicons-file-code-16: `Docker`"
 
     With `Dockerfile`:
 
     === ":octicons-file-code-16: `CPU`"
     
-        ```sh
+        ```shell
         # Build Image
         docker build -t whisper-asr-webservice .
         
         # Run Container
         docker run -d -p 9000:9000 whisper-asr-webservice
-        # or
-        docker run -d -p 9001:9000 -e ASR_MODEL=base whisper-asr-webservice3
+        # or with specific model
+        docker run -d -p 9000:9000 -e ASR_MODEL=base whisper-asr-webservice
         ```
     
     === ":octicons-file-code-16: `GPU`"
     
-        ```sh
+        ```shell
         # Build Image
         docker build -f Dockerfile.gpu -t whisper-asr-webservice-gpu .
         
         # Run Container
         docker run -d --gpus all -p 9000:9000 whisper-asr-webservice-gpu
-        # or
+        # or with specific model
         docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base whisper-asr-webservice-gpu
         ```
 
@@ -68,12 +63,19 @@ poetry run whisper-asr-webservice --host 0.0.0.0 --port 9000
     
     === ":octicons-file-code-16: `CPU`"
     
-        ```sh
+        ```shell
         docker-compose up --build
         ```
     
     === ":octicons-file-code-16: `GPU`"
     
-        ```sh
-        docker-compose up --build -f docker-compose.gpu.yml
+        ```shell
+        docker-compose -f docker-compose.gpu.yml up --build
         ```
+=== ":octicons-file-code-16: `Poetry`"
+
+    Build .whl package
+    
+    ```shell
+    poetry build
+    ```
\ No newline at end of file
diff --git a/docs/endpoints.md b/docs/endpoints.md
index 8446abb98074983a868997a48bc520e6ed7e312c..e62f723bd413bf161821826007371bf9c06b85d7 100644
--- a/docs/endpoints.md
+++ b/docs/endpoints.md
@@ -19,14 +19,18 @@ There are 2 endpoints available:
 
 ### Request URL Query Params
 
-| Name            | Values                                         |
-|-----------------|------------------------------------------------|
-| audio_file      | File                                           |
-| output          | `text` (default), `json`, `vtt`, `srt`, `tsv` |
-| task            | `transcribe`, `translate`                      |
-| language        | `en` (default is auto recognition)             |
-| word_timestamps | false (default)                                |
-| encode          | true (default)                                 |
+| Name            | Values                                         | Description                                                    |
+|-----------------|------------------------------------------------|----------------------------------------------------------------|
+| audio_file      | File                                           | Audio or video file to transcribe                              |
+| output          | `text` (default), `json`, `vtt`, `srt`, `tsv` | Output format                                                  |
+| task            | `transcribe`, `translate`                      | Task type - transcribe in source language or translate to English |
+| language        | `en` (default is auto recognition)             | Source language code (see supported languages)                 |
+| word_timestamps | false (default)                                | Enable word-level timestamps (Faster Whisper only)             |
+| vad_filter      | false (default)                                | Enable voice activity detection filtering (Faster Whisper only) |
+| encode          | true (default)                                 | Encode audio through FFmpeg before processing                  |
+| diarize         | false (default)                                | Enable speaker diarization (WhisperX only)                     |
+| min_speakers    | null (default)                                 | Minimum number of speakers for diarization (WhisperX only)     |
+| max_speakers    | null (default)                                 | Maximum number of speakers for diarization (WhisperX only)     |
 
 Example request with cURL
 
@@ -40,11 +44,57 @@ curl -X POST -H "content-type: multipart/form-data" -F "audio_file=@/path/to/fil
 - **segments**: Contains an entry per segment. Each entry provides `timestamps`, `transcript`, `token ids`, `word level timestamps` and other metadata
 - **language**: Detected or provided language (as a language code)
 
+### Response Formats
+
+The API supports multiple output formats:
+
+- **text**: Plain text transcript (default)
+- **json**: Detailed JSON with segments, timestamps, and metadata
+- **vtt**: WebVTT subtitle format
+- **srt**: SubRip subtitle format  
+- **tsv**: Tab-separated values with timestamps
+
+### Supported Languages
+
+The service supports all languages supported by Whisper. Some common language codes:
+
+- Turkish (tr)
+- English (en)
+- Spanish (es)
+- French (fr)
+- German (de)
+- Italian (it)
+- Portuguese (pt)
+- And many more...
+
+See the [Whisper documentation](https://github.com/openai/whisper#available-models-and-languages) for the full list of supported languages.
+
+### Speaker Diarization
+
+When using the WhisperX engine with diarization enabled (`diarize=true`), the output will include speaker labels for each segment. This requires:
+
+1. WhisperX engine to be configured
+2. Valid Hugging Face token set in HF_TOKEN
+3. Sufficient memory for diarization models
+
+You can optionally specify `min_speakers` and `max_speakers` if you know the expected number of speakers.
+
 ## Language detection service /detect-language
 
 Detects the language spoken in the uploaded file. Only processes first 30 seconds.
 
 Returns a json with following fields:
 
-- **detected_language**: "english"
-- **language_code**: "en"
+- **detected_language**: Human readable language name (e.g. "english")
+- **language_code**: ISO language code (e.g. "en")
+- **confidence**: Confidence score between 0 and 1 indicating detection reliability
+
+Example response:
+
+```json
+{
+    "detected_language": "english",
+    "language_code": "en",
+    "confidence": 0.98
+}
+```
diff --git a/docs/environmental-variables.md b/docs/environmental-variables.md
index d786927b8f861b806517443e7110eae85f7e7e33..70d9f3d8961145142891260bd89ee21d9220a703 100644
--- a/docs/environmental-variables.md
+++ b/docs/environmental-variables.md
@@ -1,34 +1,49 @@
 ### Configuring the `Engine`
 
 === ":octicons-file-code-16: `openai_whisper`"
-```sh
-export ASR_ENGINE=openai_whisper
-```
+
+    ```shell
+    export ASR_ENGINE=openai_whisper
+    ```
+
 === ":octicons-file-code-16: `faster_whisper`"
-```sh
-export ASR_ENGINE=faster_whisper
-```
+
+    ```shell
+    export ASR_ENGINE=faster_whisper
+    ```
+
+=== ":octicons-file-code-16: `whisperx`"
+
+    ```shell
+    export ASR_ENGINE=whisperx
+    ```
 
 ### Configuring the `Model`
 
-```sh
+```shell
 export ASR_MODEL=base
 ```
 
-Available ASR_MODELs are `tiny`, `base`, `small`, `medium`, `large`, `large-v1`, `large-v2`, `large-v3`, `turbo` and `large-v3-turbo`.
+Available ASR_MODELs are:
+
+- Standard models: `tiny`, `base`, `small`, `medium`, `large-v1`, `large-v2`, `large-v3` (or `large`), `large-v3-turbo` (or `turbo`)
+- English-optimized models: `tiny.en`, `base.en`, `small.en`, `medium.en`
+- Distilled models: `distil-large-v2`, `distil-medium.en`, `distil-small.en`, `distil-large-v3` (only for whisperx and faster-whisper)
 
 For English-only applications, the `.en` models tend to perform better, especially for the `tiny.en` and `base.en`
 models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
 
+The distilled models offer improved inference speed while maintaining good accuracy.
+
 ### Configuring the `Model Path`
 
-```sh
+```shell
 export ASR_MODEL_PATH=/data/whisper
 ```
 
 ### Configuring the `Model Unloading Timeout`
 
-```sh
+```shell
 export MODEL_IDLE_TIMEOUT=300
 ```
 
@@ -37,10 +52,47 @@ Defaults to `0`. After no activity for this period (in seconds), unload the mode
 
 ### Configuring the `SAMPLE_RATE`
 
-```sh
+```shell
 export SAMPLE_RATE=16000
 ```
 
 Defaults to `16000`. Default sample rate for audio input. `16 kHz` is commonly used in `speech-to-text` tasks.
 
+### Configuring Device and Quantization
+
+```shell
+export ASR_DEVICE=cuda  # or 'cpu'
+export ASR_QUANTIZATION=float32  # or 'float16', 'int8'
+```
+
+The `ASR_DEVICE` defaults to `cuda` if GPU is available, otherwise `cpu`. 
+
+The `ASR_QUANTIZATION` defines the precision for model weights:
+
+- `float32`: 32-bit floating-point precision (higher precision, slower inference)
+- `float16`: 16-bit floating-point precision (lower precision, faster inference)
+- `int8`: 8-bit integer precision (lowest precision, fastest inference)
+
+Defaults to `float32` for GPU, `int8` for CPU.
+
+### Configuring Subtitle Options (WhisperX)
+
+```shell
+export SUBTITLE_MAX_LINE_WIDTH=1000
+export SUBTITLE_MAX_LINE_COUNT=2
+export SUBTITLE_HIGHLIGHT_WORDS=false
+```
+
+These options only apply when using the WhisperX engine:
+
+- `SUBTITLE_MAX_LINE_WIDTH`: Maximum width of subtitle lines (default: 1000)
+- `SUBTITLE_MAX_LINE_COUNT`: Maximum number of lines per subtitle (default: 2)
+- `SUBTITLE_HIGHLIGHT_WORDS`: Enable word highlighting in subtitles (default: false)
+
+### Hugging Face Token
+
+```shell
+export HF_TOKEN=your_token_here
+```
 
+Required when using the WhisperX engine to download the diarization model.
diff --git a/docs/index.md b/docs/index.md
index 45f40ecfb391404c8babceef91a7c9610cdba45d..a7d7cddc78be5337ad177350656600d82f88f275 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -6,18 +6,19 @@ Current release (v1.7.1) supports following whisper models:
 
 - [openai/whisper](https://github.com/openai/whisper)@[v20240930](https://github.com/openai/whisper/releases/tag/v20240930)
 - [SYSTRAN/faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v1.1.0](https://github.com/SYSTRAN/faster-whisper/releases/tag/v1.1.0)
+- [whisperX](https://github.com/m-bain/whisperX)@[v3.1.1](https://github.com/m-bain/whisperX/releases/tag/v3.1.1)
 
 ## Quick Usage
 
 === ":octicons-file-code-16: `CPU`"
 
-    ```sh
+    ```shell
     docker run -d -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest
     ```
 
 === ":octicons-file-code-16: `GPU`"
 
-    ```sh
+    ```shell
     docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest-gpu
     ```
 
diff --git a/docs/run.md b/docs/run.md
index 161168cc589c5737017e69c735accec373375e26..75db423d0b7b92bb4ad1ade3e0cd293845e3a715 100644
--- a/docs/run.md
+++ b/docs/run.md
@@ -6,9 +6,12 @@ Docker Hub: <https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice>
 
 === ":octicons-file-code-16: `CPU`"
 
-    ```sh
+    ```shell
     docker pull onerahmet/openai-whisper-asr-webservice:latest
-    docker run -d -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest
+    docker run -d -p 9000:9000 \
+      -e ASR_MODEL=base \
+      -e ASR_ENGINE=openai_whisper \
+      onerahmet/openai-whisper-asr-webservice:latest
     ```
 
 === ":octicons-file-code-16: `CPU (macOS)`"
@@ -17,38 +20,57 @@ Docker Hub: <https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice>
     > 
     > The `:latest` image tag provides both amd64 and arm64 architectures:
     
-    ```sh
+    ```shell
     docker pull onerahmet/openai-whisper-asr-webservice:latest
-    docker run -d -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest
+    docker run -d -p 9000:9000 \
+      -e ASR_MODEL=base \
+      -e ASR_ENGINE=openai_whisper \
+      onerahmet/openai-whisper-asr-webservice:latest
     ```
 
 === ":octicons-file-code-16: `GPU`"
 
-    ```sh
+    ```shell
     docker pull onerahmet/openai-whisper-asr-webservice:latest-gpu
-    docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base -e ASR_ENGINE=openai_whisper onerahmet/openai-whisper-asr-webservice:latest-gpu
+    docker run -d --gpus all -p 9000:9000 \
+      -e ASR_MODEL=base \
+      -e ASR_ENGINE=openai_whisper \
+      onerahmet/openai-whisper-asr-webservice:latest-gpu
     ```
 
+### Environment Variables
+
+The following environment variables can be used to configure the service:
+
+- `ASR_MODEL`: Whisper model to use (tiny, base, small, medium, large) [default: base]
+- `ASR_ENGINE`: ASR engine to use (openai_whisper, faster_whisper) [default: openai_whisper]
+- `ASR_MODEL_PATH`: Custom path to store/load model files [optional]
+
 > Interactive Swagger API documentation is available at <http://localhost:9000/docs>
 
 ![Swagger UI](assets/images/swagger-ui.png)
 
 ## Cache
 
-The ASR model is downloaded each time you start the container, using the large model this can take some time.
-If you want to decrease the time it takes to start your container by skipping the download, you can store the cache directory (`~/.cache/whisper` or `/root/.cache/whisper`) to a persistent storage.
-Next time you start your container the ASR Model will be taken from the cache instead of being downloaded again.
+The ASR model is downloaded each time you start the container. Using the large model can take significant time to download.
+To reduce container startup time by avoiding repeated downloads, you can persist the cache directory to local storage.
+The model will then be loaded from the cache instead of being downloaded again on subsequent container starts.
 
-**Important this will prevent you from receiving any updates to the models.**
+**Important: Using a persistent cache will prevent you from receiving model updates.**
 
 === ":octicons-file-code-16: `Default cache dir`"
 
-    ```sh
-    docker run -d -p 9000:9000 -v $PWD/yourlocaldir:/root/.cache/whisper onerahmet/openai-whisper-asr-webservice:latest
+    ```shell
+    docker run -d -p 9000:9000 \
+      -v $PWD/cache:/root/.cache \
+      onerahmet/openai-whisper-asr-webservice:latest
     ```
 
 === ":octicons-file-code-16: `With ASR_MODEL_PATH`"
 
-    ```sh
-    docker run -d -p 9000:9000 -e ASR_MODEL_PATH=/data/whisper -v $PWD/yourlocaldir:/data/whisper onerahmet/openai-whisper-asr-webservice:latest
+    ```shell
+    docker run -d -p 9000:9000 \
+      -e ASR_MODEL_PATH=/data/whisper \
+      -v $PWD/cache:/data/whisper \
+      onerahmet/openai-whisper-asr-webservice:latest
     ```
diff --git a/mkdocs.yml b/mkdocs.yml
index 5bcc8a1d651650d522c8ea6be33e4d54b056cd97..c14b4c2a32ee9a25a855a0887d96180fac1290dc 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -5,7 +5,7 @@ site_dir: public
 site_description: "OpenAI Whisper ASR Webservice API"
 repo_url: "https://github.com/ahmetoner/whisper-asr-webservice"
 repo_name: "ahmetoner/whisper-asr-webservice"
-copyright: Copyright &copy; 2023
+copyright: Copyright &copy; 2025
 edit_uri: edit/main/docs/
 
 validation:
@@ -14,14 +14,13 @@ validation:
   unrecognized_links: warn
 
 nav:
-  - Home:
-      - Whisper ASR Webservice: index.md
-      - Run: run.md
-      - Endpoints: endpoints.md
-      - Environmental Variables: environmental-variables.md
-      - Build: build.md
-      - Changelog: changelog.md
-      - Licence: licence.md
+  - Overview: index.md
+  - Installation & Usage: run.md
+  - API Endpoints: endpoints.md
+  - Configuration: environmental-variables.md
+  - Development: build.md
+  - Changelog: changelog.md
+  - License: licence.md
   - Releases: https://github.com/ahmetoner/whisper-asr-webservice/releases
   - Docker Hub: https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice
 
@@ -39,13 +38,38 @@ theme:
     - content.tooltips
     - navigation.footer
     - navigation.indexes
-    - navigation.sections
-    - navigation.tabs
-    - navigation.tabs.sticky
+    # - navigation.sections # important
     - navigation.top
+    # - navigation.tabs
+    # - navigation.tabs.sticky
     - search.highlight
     - search.suggest
     - toc.follow
+    - toc.integrate
+  palette:
+    # System preference
+    - media: "(prefers-color-scheme)"
+      toggle:
+        icon: material/brightness-auto
+        name: Switch to light mode
+    # Light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      primary: custom
+      accent: teal
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+    # Dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      primary: black
+      accent: lime
+      toggle:
+        icon: material/brightness-4
+        name: Switch to system preference
+
+
 
 extra_css:
   - assets/css/extra.css
@@ -80,6 +104,7 @@ plugins:
   - search
 
 extra:
+  generator: false 
   social:
     - icon: fontawesome/brands/github
       link: https://github.com/ahmetoner
diff --git a/poetry.lock b/poetry.lock
index 82a215977cb78d02d5f452d0871172e7a95bc39d..8455b866443f9f03684309367eb56627e171df5c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,172 @@
 # This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.6"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
+files = [
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+    {file = "aiohappyeyeballs-2.4.6.tar.gz", hash = "sha256:9b05052f9042985d32ecbe4b59a77ae19c006a78f1344d7fdad69d28ded3d0b0"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.11.12"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
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+
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+aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
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+
+[package.extras]
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+
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 version = "3.7.1"
@@ -23,6 +190,61 @@ doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-
 test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4) ; python_version < \"3.8\"", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17) ; python_version < \"3.12\" and platform_python_implementation == \"CPython\" and platform_system != \"Windows\""]
 trio = ["trio (<0.22)"]
 
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+
 [[package]]
 name = "av"
 version = "11.0.0"
@@ -92,6 +314,87 @@ files = [
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+[package.dependencies]
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+
 [[package]]
 name = "charset-normalizer"
 version = "3.1.0"
@@ -201,7 +504,7 @@ description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 groups = ["main"]
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@@ -226,6 +529,100 @@ humanfriendly = ">=9.1"
 [package.extras]
 cron = ["capturer (>=2.4)"]
 
+[[package]]
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+description = "Add colours to the output of Python's logging module."
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+
+[package.extras]
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+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.11.1)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
+
 [[package]]
 name = "ctranslate2"
 version = "4.1.0"
@@ -267,6 +664,48 @@ numpy = "*"
 pyyaml = ">=5.3,<7"
 setuptools = "*"
 
+[[package]]
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+
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+python-versions = "*"
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+
+[[package]]
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+python-versions = ">=3.8"
+groups = ["main"]
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+
 [[package]]
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 version = "1.1.3"
@@ -274,7 +713,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
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 [[package]]
 name = "faster-whisper"
-version = "1.1.1"
+version = "1.1.0"
 description = "Faster Whisper transcription with CTranslate2"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
 markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
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+    {file = "faster-whisper-1.1.0.tar.gz", hash = "sha256:cea4bba5d4527173fdbacafa56f2ffb17dd322688f6c3fdf5fd7b6b6c193ce17"},
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 [package.dependencies]
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 [[package]]
 name = "fsspec"
 version = "2025.2.0"
@@ -392,6 +1009,9 @@ files = [
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+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
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 ]
 
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 [package.extras]
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 version = "0.44.0"
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 tests = ["pytest (>=4.6)"]
 
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+
 [[package]]
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 version = "3.4.2"
@@ -739,6 +1878,33 @@ example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy
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 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
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+
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+all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
+corenlp = ["requests"]
+machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
+
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 name = "numba"
 version = "0.61.0"
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+
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+
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+
 [[package]]
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 version = "1.10.7"
@@ -1199,6 +2828,38 @@ typing-extensions = ">=4.2.0"
 dotenv = ["python-dotenv (>=0.10.4)"]
 email = ["email-validator (>=1.0.3)"]
 
+[[package]]
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+
 [[package]]
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 version = "3.4.1"
@@ -1212,6 +2873,22 @@ files = [
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+[[package]]
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+version = "2.9.0.post0"
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+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
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 [[package]]
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@@ -1241,6 +2918,76 @@ files = [
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+description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
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+pymysql = ["pymysql"]
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 version = "0.41.3"
@@ -1455,6 +3676,53 @@ mpmath = ">=1.1.0,<1.4"
 [package.extras]
 dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
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+]
+
+[package.dependencies]
+torch = "2.6.0"
+
+[[package]]
+name = "torchmetrics"
+version = "1.6.1"
+description = "PyTorch native Metrics"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
+files = [
+    {file = "torchmetrics-1.6.1-py3-none-any.whl", hash = "sha256:c3090aa2341129e994c0a659abb6d4140ae75169a6ebf45bffc16c5cb553b38e"},
+    {file = "torchmetrics-1.6.1.tar.gz", hash = "sha256:a5dc236694b392180949fdd0a0fcf2b57135c8b600e557c725e077eb41e53e64"},
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+numpy = ">1.20.0"
+packaging = ">17.1"
+torch = ">=2.0.0"
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+[package.extras]
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+audio = ["gammatone (>=1.0.0)", "librosa (>=0.10.0)", "numpy (<2.0)", "onnxruntime (>=1.12.0)", "pesq (>=0.0.4)", "pystoi (>=0.4.0)", "requests (>=2.19.0)", "torchaudio (>=2.0.1)"]
+detection = ["pycocotools (>2.0.0)", "torchvision (>=0.15.1)"]
+dev = ["PyTDC (==0.4.1) ; python_version < \"3.12\"", "SciencePlots (>=2.0.0)", "bert_score (==0.3.13)", "dython (==0.7.6) ; python_version < \"3.9\"", "dython (>=0.7.8,<0.8.0) ; python_version > \"3.8\"", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.6.3)", "gammatone (>=1.0.0)", "huggingface-hub (<0.28)", "ipadic (>=1.0.0)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "librosa (>=0.10.0)", "lpips (<=0.1.4)", "matplotlib (>=3.6.0)", "mecab-ko (>=1.0.0,<1.1.0) ; python_version < \"3.12\"", "mecab-ko-dic (>=1.0.0) ; python_version < \"3.12\"", "mecab-python3 (>=1.0.6)", "mir-eval (>=0.6)", "monai (==1.3.2) ; python_version < \"3.9\"", "monai (==1.4.0) ; python_version > \"3.8\"", "mypy (==1.14.0)", "netcal (>1.0.0)", "nltk (>3.8.1)", "numpy (<2.0)", "numpy (<2.3.0)", "onnxruntime (>=1.12.0)", "pandas (>1.4.0)", "permetrics (==2.0.0)", "pesq (>=0.0.4)", "piq (<=0.8.0)", "pycocotools (>2.0.0)", "pystoi (>=0.4.0)", "pytorch-msssim (==1.0.0)", "regex (>=2021.9.24)", "requests (>=2.19.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.3.0)", "scikit-image (>=0.19.0)", "scipy (>1.0.0)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch (==2.5.1)", "torch-fidelity (<=0.4.0)", "torch_complex (<0.5.0)", "torchaudio (>=2.0.1)", "torchvision (>=0.15.1)", "torchvision (>=0.15.1)", "tqdm (<4.68.0)", "transformers (>4.4.0)", "transformers (>=4.42.3)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
+image = ["scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchvision (>=0.15.1)"]
+multimodal = ["piq (<=0.8.0)", "transformers (>=4.42.3)"]
+text = ["ipadic (>=1.0.0)", "mecab-python3 (>=1.0.6)", "nltk (>3.8.1)", "regex (>=2021.9.24)", "sentencepiece (>=0.2.0)", "tqdm (<4.68.0)", "transformers (>4.4.0)"]
+typing = ["mypy (==1.14.0)", "torch (==2.5.1)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
+visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.6.0)"]
+
 [[package]]
 name = "tqdm"
 version = "4.67.1"
@@ -1711,6 +4084,75 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "transformers"
+version = "4.29.0"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+optional = false
+python-versions = ">=3.7.0"
+groups = ["main"]
+markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
+files = [
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+    {file = "transformers-4.29.0.tar.gz", hash = "sha256:b5dff9ce3708dc6639d892435fa69c51dee5c89870f888fa59ef0fc3baa3d8c7"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.11.0,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+tokenizers = ">=0.11.1,<0.11.3 || >0.11.3,<0.14"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.19.0)"]
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+audio = ["kenlm", "librosa", "numba (<0.57.0)", "phonemizer", "pyctcdecode (>=0.4.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
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+docs = ["Pillow", "accelerate (>=0.19.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.6.9)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "numba (<0.57.0)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
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+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune]", "sigopt"]
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+ray = ["ray[tune]"]
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+sagemaker = ["sagemaker (>=2.31.0)"]
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+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "numba (<0.57.0)", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
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+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "numba (<0.57.0)", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+torch = ["accelerate (>=0.19.0)", "torch (>=1.9,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "numba (<0.57.0)", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.11.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow"]
+
 [[package]]
 name = "triton"
 version = "3.2.0"
@@ -1732,6 +4174,25 @@ build = ["cmake (>=3.20)", "lit"]
 tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
+[[package]]
+name = "typer"
+version = "0.15.1"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
+files = [
+    {file = "typer-0.15.1-py3-none-any.whl", hash = "sha256:7994fb7b8155b64d3402518560648446072864beefd44aa2dc36972a5972e847"},
+    {file = "typer-0.15.1.tar.gz", hash = "sha256:a0588c0a7fa68a1978a069818657778f86abe6ff5ea6abf472f940a08bfe4f0a"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
+typing-extensions = ">=3.7.4.3"
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -1745,6 +4206,19 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
+[[package]]
+name = "tzdata"
+version = "2025.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+groups = ["main"]
+markers = "platform_machine != \"arm64\" and platform_machine != \"x86_64\" or sys_platform != \"linux\" and sys_platform != \"darwin\" or (sys_platform == \"darwin\" or sys_platform == \"linux\") and (platform_machine == \"arm64\" or platform_machine == \"x86_64\") or sys_platform == \"linux\" and (platform_machine == \"arm64\" or platform_machine == \"x86_64\" or platform_machine == \"aarch64\")"
+files = [
+    {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
+    {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
+]
+
 [[package]]
 name = "urllib3"
 version = "1.26.15"
@@ -1798,7 +4272,7 @@ description = "Fast implementation of asyncio event loop on top of libuv"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
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@@ -1954,7 +4428,131 @@ files = [
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 ]
 
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+groups = ["main"]
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+
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+faster-whisper = "1.1.0"
+nltk = "*"
+pandas = "*"
+"pyannote.audio" = "3.3.2"
+setuptools = ">=65"
+torch = ">=2"
+torchaudio = ">=2"
+transformers = "*"
+
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+dev = ["pytest"]
+
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+    {file = "yarl-1.18.3-cp39-cp39-win32.whl", hash = "sha256:ac36703a585e0929b032fbaab0707b75dc12703766d0b53486eabd5139ebadd5"},
+    {file = "yarl-1.18.3-cp39-cp39-win_amd64.whl", hash = "sha256:ba87babd629f8af77f557b61e49e7c7cac36f22f871156b91e10a6e9d4f829e9"},
+    {file = "yarl-1.18.3-py3-none-any.whl", hash = "sha256:b57f4f58099328dfb26c6a771d09fb20dbbae81d20cfb66141251ea063bd101b"},
+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
 [metadata]
 lock-version = "2.1"
-python-versions = "^3.10"
-content-hash = "0307c0ed11709ece4369925e445c8fc0442fa19ede337a8e4ed6e8333db726c9"
+python-versions = "<3.13,>=3.10"
+content-hash = "b183ceab703bbdc385619eb96470e22ebb936fb81c66fda728c60ba460760a37"
diff --git a/pyproject.toml b/pyproject.toml
index 3b80d6486ea669ba4636a570c0c8e41816d8d34a..c32fbd92aa6c9c61e39829734cd44d509ac1299f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,28 +11,24 @@ packages = [{ include = "app" }]
 [tool.poetry.scripts]
 whisper-asr-webservice = "app.webservice:start"
 
-# Package Sources
 [[tool.poetry.source]]
 name = "pytorch"
 url = "https://download.pytorch.org/whl/cpu"
 priority = "explicit"
 
-# Core Dependencies
 [tool.poetry.dependencies]
-python = "^3.10"
+python = "<3.13,>=3.10"
 fastapi = "^0.115.8"
 uvicorn = { extras = ["standard"], version = "^0.34.0" }
 python-multipart = "^0.0.20"
 ffmpeg-python = "^0.2.0"
-
-# ML Dependencies
+numpy = "<2.0.0"
 openai-whisper = "^20240930"
-faster-whisper = "^1.1.1"
+faster-whisper = "^1.1.0"
+whisperx = "^3.3.1"
 tqdm = "^4.67.1"
 llvmlite = "^0.44.0"
 numba = "^0.61.0"
-
-# PyTorch Dependencies (Platform Specific)
 torch = [
   { markers = "sys_platform == 'darwin' and platform_machine == 'arm64'", url = "https://download.pytorch.org/whl/cpu/torch-2.6.0-cp310-none-macosx_11_0_arm64.whl" },
   { markers = "sys_platform == 'linux' and platform_machine == 'arm64'", url = "https://download.pytorch.org/whl/cpu/torch-2.6.0-cp310-none-macosx_11_0_arm64.whl" },
@@ -42,27 +38,21 @@ torch = [
   { markers = "sys_platform == 'win' and platform_machine == 'amd64'", url = "https://download.pytorch.org/whl/cpu/torch-2.6.0%2Bcpu-cp310-cp310-win_amd64.whl" },
 ]
 
-
-# Development Dependencies
 [poetry.group.dev.dependencies]
 pytest = "^8.3.4"
 ruff = "^0.9.6"
 black = "^25.1.0"
-mkdocs = "^1.6.1"
 mkdocs-material = "^9.6.4"
 pymdown-extensions = "^10.14.3"
 
-# Build System Configuration
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
 
-# Code Formatting Configuration
 [tool.black]
 skip-string-normalization = true
 line-length = 120
 
-# Linting Configuration
 [tool.ruff]
 line-length = 120