diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c9147a6..4a0fd80 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,16 +1,12 @@
----
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.6.0
     hooks:
       - id: check-added-large-files
       - id: check-ast
       - id: check-builtin-literals
       - id: check-case-conflict
       - id: check-docstring-first
-      - id: check-json
       - id: check-merge-conflict
       - id: check-shebang-scripts-are-executable
       - id: check-symlinks
@@ -22,21 +18,24 @@ repos:
       - id: destroyed-symlinks
       - id: detect-private-key
       - id: end-of-file-fixer
-        types: [ python ]
+        types: [python]
       - id: fix-byte-order-marker
       - id: mixed-line-ending
       - id: name-tests-test
-        args: [ --pytest-test-first ]
+        args: [--pytest-test-first]
       - id: trailing-whitespace
-        types: [ python ]
-
-  - repo: https://github.com/psf/black
-    rev: 23.7.0
+        types: [python]
+  - repo: https://github.com/hhatto/autopep8
+    rev: v2.2.0
     hooks:
-      - id: black
-        additional_dependencies: [click==8.0.4]
-
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+      - id: autopep8
+        args: ['-i', '--max-line-length=120', '--indent-size=2', '--ignore=E121']
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
     hooks:
       - id: isort
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.10.0
+    hooks:
+      - id: mypy
+        additional_dependencies: ['types-requests']
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..cc9c048
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1971 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
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+description = "A module for Audio/Acoustic Activity Detection"
+optional = false
+python-versions = "*"
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+    {file = "auditok-0.2.0.tar.gz", hash = "sha256:46c512e287b2e13df4819774166af32b236d754afbe6b0dc87281b233e5ae688"},
+]
+
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+    {file = "websockets-12.0-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba0cab91b3956dfa9f512147860783a1829a8d905ee218a9837c18f683239611"},
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+    {file = "websockets-12.0-py3-none-any.whl", hash = "sha256:dc284bbc8d7c78a6c69e0c7325ab46ee5e40bb4d50e494d8131a07ef47500e9e"},
+    {file = "websockets-12.0.tar.gz", hash = "sha256:81df9cbcbb6c260de1e007e58c011bfebe2dafc8435107b0537f393dd38c8b1b"},
+]
+
+[[package]]
+name = "yt-dlp"
+version = "2024.5.27"
+description = "A feature-rich command-line audio/video downloader"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "yt_dlp-2024.5.27-py3-none-any.whl", hash = "sha256:5dbedb7610ae9440cc44e744f528e078828142bbe09af950cf7e99ce69c866d9"},
+    {file = "yt_dlp-2024.5.27.tar.gz", hash = "sha256:3566c0de240d0cd3d1c2285ce655f72ca38dfc618d634d46818b00d89d5288be"},
+]
+
+[package.dependencies]
+brotli = {version = "*", markers = "implementation_name == \"cpython\""}
+brotlicffi = {version = "*", markers = "implementation_name != \"cpython\""}
+certifi = "*"
+mutagen = "*"
+pycryptodomex = "*"
+requests = ">=2.31.0,<3"
+urllib3 = ">=1.26.17,<3"
+websockets = ">=12.0"
+
+[package.extras]
+build = ["build", "hatchling", "pip", "setuptools (>=66.1.0,<70)", "wheel"]
+curl-cffi = ["curl-cffi (==0.5.10)"]
+dev = ["autopep8 (>=2.0,<3.0)", "pre-commit", "pytest (>=8.1,<9.0)", "ruff (>=0.4.4,<0.5.0)"]
+py2exe = ["py2exe (>=0.12)", "requests (==2.31.*)"]
+pyinstaller = ["pyinstaller (==5.13.2)", "pyinstaller (>=6.3)"]
+secretstorage = ["cffi", "secretstorage"]
+static-analysis = ["autopep8 (>=2.0,<3.0)", "ruff (>=0.4.4,<0.5.0)"]
+test = ["pytest (>=8.1,<9.0)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.12"
+content-hash = "c6e68835761af44c3e92b149241e7883e3dc2781deb0edf6513226c7f5638225"
diff --git a/pyproject.toml b/pyproject.toml
index 0431246..e8117c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,14 +1,12 @@
-[build-system]
-requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
-
-[project]
+[tool.poetry]
 name = "tafrigh"
 version = "1.1.4"
 description = "تفريغ النصوص وإنشاء ملفات SRT و VTT باستخدام نماذج Whisper وتقنية wit.ai."
+authors = ["EasyBooks <easybooksdev@gmail.com>"]
+license = "MIT"
 readme = "README.md"
-license = { file = "LICENSE" }
-requires-python = ">=3.9"
+packages = [{include = "src"}]
+keywords = ["tafrigh", "speech-to-text", "wit.ai", "whisper"]
 classifiers = [
     "Development Status :: 5 - Production/Stable",
     "Intended Audience :: Developers",
@@ -17,45 +15,41 @@ classifiers = [
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
-authors = [{ name = "الكتب المٌيسّرة", email = "easybooksdev@gmail.com" }]
-keywords = ["tafrigh", "speech-to-text", "wit.ai", "whisper"]
-dependencies = ["tqdm>=4.66.4", "yt-dlp>=2024.4.9"]
+homepage = "https://tafrigh.ieasybooks.com"
+repository = "https://github.com/ieasybooks/tafrigh"
 
-[project.optional-dependencies]
-wit = [
-    "auditok>=0.2.0",
-    "numpy>=1.26.4",
-    "pydub>=0.25.1",
-    "requests>=2.32.0",
-]
-whisper = [
-    "faster-whisper>=1.0.2",
-    "openai-whisper>=20231117",
-    "stable-ts>=2.17.2",
-]
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+tqdm = ">=4.66.4"
+yt-dlp = ">=2024.4.9"
+auditok = {version = ">=0.2.0", extras = ["wit"]}
+pydub = {version = ">=0.25.1", extras = ["wit"]}
+requests = {version = ">=2.32.0", extras = ["wit"]}
+faster-whisper = {version = ">=1.0.2", extras = ["whisper"]}
+openai-whisper = {git = "https://github.com/openai/whisper.git", extras = ["whisper"]}
+stable-ts = {version = ">=2.17.2", extras = ["whisper"]}
 
-[project.urls]
-homepage = "https://github.com/ieasybooks/tafrigh"
-repository = "https://github.com/ieasybooks/tafrigh"
+[tool.poetry.scripts]
+tafrigh = "src.cli:main"
 
-[project.scripts]
-tafrigh = "tafrigh.cli:main"
+[tool.poetry.group.dev.dependencies]
+pre-commit = "^3.7.1"
 
-[tool.black]
-line-length = 120
-skip-string-normalization = true
+[tool.autopep8]
+max-line-length = 120
+indent-size = 2
+ignore = ["E121"]
 
 [tool.isort]
-profile = "black"
-src_paths = "tafrigh"
 line_length = 120
+src_paths = ["src"]
 lines_between_types = 1
 lines_after_imports = 2
-case_sensitive = true
-include_trailing_comma = true
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..aba4c1b
--- /dev/null
+++ b/src/__init__.py
@@ -0,0 +1,29 @@
+__all__ = [
+    "farrigh",
+    "Config",
+    "Downloader",
+    "TranscriptType",
+    "Writer",
+    "WhisperRecognizer",
+    "AudioSplitter",
+    "WitRecognizer",
+]
+
+
+from .cli import farrigh
+from .config import Config
+from .downloader import Downloader
+from .types.transcript_type import TranscriptType
+from .writer import Writer
+
+
+try:
+    from .recognizers.whisper_recognizer import WhisperRecognizer
+except ModuleNotFoundError:
+    pass
+
+try:
+    from .audio_splitter import AudioSplitter
+    from .recognizers.wit_recognizer import WitRecognizer
+except ModuleNotFoundError:
+    pass
diff --git a/src/audio_splitter.py b/src/audio_splitter.py
new file mode 100644
index 0000000..b08bdbd
--- /dev/null
+++ b/src/audio_splitter.py
@@ -0,0 +1,77 @@
+import os
+
+from auditok import AudioRegion
+from auditok.core import split
+from pydub import AudioSegment
+from pydub.generators import WhiteNoise
+
+
+class AudioSplitter:
+    def split(
+        self,
+        file_path: str,
+        output_dir: str,
+        min_dur: float = 0.5,
+        max_dur: float = 15,
+        max_silence: float = 0.5,
+        energy_threshold: float = 50,
+        expand_segments_with_noise: bool = False,
+        noise_seconds: int = 1,
+        noise_amplitude: int = 0,
+    ) -> list[tuple[str, float, float]]:
+        segments = split(
+            file_path,
+            min_dur=min_dur,
+            max_dur=max_dur,
+            max_silence=max_silence,
+            energy_threshold=energy_threshold,
+        )
+
+        if expand_segments_with_noise:
+            segments = [
+                (
+                    self._expand_segment_with_noise(segment, noise_seconds, noise_amplitude),
+                    segment.meta.start,
+                    segment.meta.end,
+                ) for segment in segments
+            ]
+
+        return self._save_segments(output_dir, segments)
+
+    def _expand_segment_with_noise(
+        self,
+        segment: AudioRegion,
+        noise_seconds: int,
+        noise_amplitude: int,
+    ) -> AudioSegment:
+
+        audio_segment = AudioSegment(
+            segment._data, 
+            frame_rate=segment.sampling_rate, 
+            sample_width=segment.sample_width, 
+            channels=segment.channels,
+        )
+
+        pre_noise = WhiteNoise().to_audio_segment(duration=noise_seconds * 1000, volume=noise_amplitude)
+        post_noise = WhiteNoise().to_audio_segment(duration=noise_seconds * 1000, volume=noise_amplitude)
+
+        return pre_noise + audio_segment + post_noise
+
+    def _save_segments(
+        self,
+        output_dir: str,
+        segments: list[AudioSegment | tuple[AudioSegment, float, float]],
+    ) -> list[tuple[str, float, float]]:
+        segment_paths = []
+
+        for i, segment in enumerate(segments):
+            output_file = os.path.join(output_dir, f'segment_{i + 1}.mp3')
+
+            if isinstance(segment, tuple):
+                segment[0].export(output_file, format='mp3')
+                segment_paths.append((output_file, segment[1], segment[2]))
+            else:
+                segment.save(output_file)
+                segment_paths.append((output_file, segment.meta.start, segment.meta.end))
+            
+        return segment_paths
diff --git a/src/cli.py b/src/cli.py
new file mode 100644
index 0000000..069d9e3
--- /dev/null
+++ b/src/cli.py
@@ -0,0 +1,264 @@
+import csv
+import logging
+import os
+import random
+import re
+import sys
+
+from collections import deque
+from pathlib import Path
+from typing import Any, Generator
+
+from tqdm import tqdm
+
+from .config import Config
+from .downloader import Downloader
+from .utils import cli_utils, file_utils, time_utils
+from .writer import Writer
+
+
+try:
+    import requests
+
+    from .recognizers.wit_recognizer import WitRecognizer
+    from .utils.wit import file_utils as wit_file_utils
+except ModuleNotFoundError:
+    pass
+
+try:
+    from .recognizers.whisper_recognizer import WhisperRecognizer
+    from .types.whisper.type_hints import WhisperModel
+    from .utils.whisper import whisper_utils
+except ModuleNotFoundError:
+    pass
+
+
+def main():
+    args = cli_utils.parse_args(sys.argv[1:])
+
+    config = Config(
+        urls_or_paths=args.urls_or_paths,
+        skip_if_output_exist=args.skip_if_output_exist,
+        playlist_items=args.playlist_items,
+        verbose=args.verbose,
+        #
+        model_name_or_path=args.model_name_or_path,
+        task=args.task,
+        language=args.language,
+        use_faster_whisper=args.use_faster_whisper,
+        beam_size=args.beam_size,
+        ct2_compute_type=args.ct2_compute_type,
+        #
+        wit_client_access_tokens=args.wit_client_access_tokens,
+        max_cutting_duration=args.max_cutting_duration,
+        min_words_per_segment=args.min_words_per_segment,
+        #
+        save_files_before_compact=args.save_files_before_compact,
+        save_yt_dlp_responses=args.save_yt_dlp_responses,
+        output_sample=args.output_sample,
+        output_formats=args.output_formats,
+        output_dir=args.output_dir,
+    )
+
+    if config.use_wit() and config.input.skip_if_output_exist:
+        retries = 3
+
+        while retries > 0:
+            try:
+                deque(farrigh(config), maxlen=0)
+                break
+            except requests.exceptions.RetryError:
+                retries -= 1
+    else:
+        deque(farrigh(config), maxlen=0)
+
+
+def farrigh(config: Config) -> Generator[dict[str, int], None, None]:
+    prepare_output_dir(config.output.output_dir)
+
+    model = None
+    if not config.use_wit():
+        model = whisper_utils.load_model(config.whisper)
+
+    segments = []
+
+    for idx, item in enumerate(tqdm(config.input.urls_or_paths, desc='URLs or local paths')):
+        progress_info = {
+            'outer_total': len(config.input.urls_or_paths),
+            'outer_current': idx + 1,
+            'outer_status': 'processing',
+        }
+
+        if Path(item).exists():
+            file_or_folder = Path(item)
+            for progress_info, local_elements_segments in process_local(file_or_folder, model, config, progress_info):
+                segments.extend(local_elements_segments)
+                yield progress_info
+        elif re.match('(https?://)', item):
+            for progress_info, url_elements_segments in process_url(item, model, config, progress_info):
+                segments.extend(url_elements_segments)
+                yield progress_info
+        else:
+            logging.error(f'Path {item} does not exist and is not a URL either.')
+
+            progress_info['outer_status'] = 'completed'
+            yield progress_info
+
+            continue
+
+        progress_info['outer_status'] = 'completed'
+        yield progress_info
+
+    write_output_sample(segments, config.output)
+
+
+def prepare_output_dir(output_dir: str) -> None:
+    os.makedirs(output_dir, exist_ok=True)
+
+
+def process_local(
+    path: Path,
+    model: 'WhisperModel',
+    config: Config,
+    progress_info: dict,
+) -> Generator[tuple[dict[str, int], list[list[dict[str, str | float]]]], None, None]:
+    filtered_media_files: list[Path] = file_utils.filter_media_files([path] if path.is_file() else path.iterdir())
+    files: list[dict[str, Any]] = [{'file_name': file.name, 'file_path': file} for file in filtered_media_files]
+
+    for idx, file in enumerate(tqdm(files, desc='Local files')):
+        new_progress_info = progress_info.copy()
+        new_progress_info.update(
+            {
+                'inner_total': len(files),
+                'inner_current': idx + 1,
+                'inner_status': 'processing',
+                'progress': 0.0,
+                'remaining_time': None,
+            }
+        )
+        yield new_progress_info, []
+
+        writer = Writer()
+        if config.input.skip_if_output_exist and writer.is_output_exist(Path(file['file_name']).stem, config.output):
+            new_progress_info['inner_status'] = 'completed'
+            yield new_progress_info, []
+
+            continue
+
+        file_path = str(file['file_path'].absolute())
+
+        if config.use_wit():
+            mp3_file_path = str(wit_file_utils.convert_to_mp3(file['file_path']).absolute())
+            recognize_generator = WitRecognizer(verbose=config.input.verbose).recognize(mp3_file_path, config.wit)
+        else:
+            recognize_generator = WhisperRecognizer(verbose=config.input.verbose).recognize(
+                file_path,
+                model,
+                config.whisper,
+            )
+
+        while True:
+            try:
+                new_progress_info.update(next(recognize_generator))
+                yield new_progress_info, []
+            except StopIteration as exception:
+                segments = exception.value
+                break
+
+        if config.use_wit() and file['file_path'].suffix != '.mp3':
+            Path(mp3_file_path).unlink(missing_ok=True)
+
+        writer.write_all(Path(file['file_name']).stem, segments, config.output)
+
+        for segment in segments:
+            segment['url'] = f"file://{file_path}&t={int(segment['start'])}"
+            segment['file_path'] = file_path
+
+        new_progress_info['inner_status'] = 'completed'
+        new_progress_info['progress'] = 100.0
+        yield new_progress_info, writer.compact_segments(segments, config.output.min_words_per_segment)
+
+
+def process_url(
+    url: str,
+    model: 'WhisperModel',
+    config: Config,
+    progress_info: dict,
+) -> Generator[tuple[dict[str, int], list[list[dict[str, str | float]]]], None, None]:
+    url_data = Downloader(playlist_items=config.input.playlist_items, output_dir=config.output.output_dir).download(
+        url,
+        save_response=config.output.save_yt_dlp_responses,
+    )
+
+    if '_type' in url_data and url_data['_type'] == 'playlist':
+        url_data = url_data['entries']
+    else:
+        url_data = [url_data]
+
+    for idx, element in enumerate(tqdm(url_data, desc='URL elements')):
+        if not element:
+            continue
+
+        new_progress_info = progress_info.copy()
+        new_progress_info.update(
+            {
+                'inner_total': len(url_data),
+                'inner_current': idx + 1,
+                'inner_status': 'processing',
+                'progress': 0.0,
+                'remaining_time': None,
+            }
+        )
+        yield new_progress_info, []
+
+        writer = Writer()
+        if config.input.skip_if_output_exist and writer.is_output_exist(element['id'], config.output):
+            new_progress_info['inner_status'] = 'completed'
+            yield new_progress_info, []
+
+            continue
+
+        file_path = os.path.join(config.output.output_dir, f"{element['id']}.mp3")
+
+        if config.use_wit():
+            recognize_generator = WitRecognizer(verbose=config.input.verbose).recognize(file_path, config.wit)
+        else:
+            recognize_generator = WhisperRecognizer(verbose=config.input.verbose).recognize(
+                file_path,
+                model,
+                config.whisper,
+            )
+
+        while True:
+            try:
+                new_progress_info.update(next(recognize_generator))
+                yield new_progress_info, []
+            except StopIteration as exception:
+                segments = exception.value
+                break
+
+        writer.write_all(element['id'], segments, config.output)
+
+        for segment in segments:
+            segment['url'] = f"https://youtube.com/watch?v={element['id']}&t={int(segment['start'])}"
+            segment['file_path'] = file_path
+
+        new_progress_info['inner_status'] = 'completed'
+        new_progress_info['progress'] = 100.0
+        yield new_progress_info, writer.compact_segments(segments, config.output.min_words_per_segment)
+
+
+def write_output_sample(segments: list[dict[str, str | float]], output: Config.Output) -> None:
+    if output.output_sample == 0:
+        return
+
+    random.shuffle(segments)
+
+    with open(os.path.join(output.output_dir, 'sample.csv'), 'w') as fp:
+        writer = csv.DictWriter(fp, fieldnames=['start', 'end', 'text', 'url', 'file_path'])
+        writer.writeheader()
+
+        for segment in segments[: output.output_sample]:
+            segment['start'] = time_utils.format_timestamp(segment['start'], include_hours=True, decimal_marker=',')
+            segment['end'] = time_utils.format_timestamp(segment['end'], include_hours=True, decimal_marker=',')
+            writer.writerow(segment)
diff --git a/src/config.py b/src/config.py
new file mode 100644
index 0000000..0b44601
--- /dev/null
+++ b/src/config.py
@@ -0,0 +1,118 @@
+import logging
+
+from .types.transcript_type import TranscriptType
+
+
+class Config:
+    def __init__(
+        self,
+        urls_or_paths: list[str],
+        skip_if_output_exist: bool,
+        playlist_items: str,
+        verbose: bool,
+        model_name_or_path: str,
+        task: str,
+        language: str,
+        use_faster_whisper: bool,
+        beam_size: int,
+        ct2_compute_type: str,
+        wit_client_access_tokens: list[str],
+        max_cutting_duration: int,
+        min_words_per_segment: int,
+        save_files_before_compact: bool,
+        save_yt_dlp_responses: bool,
+        output_sample: int,
+        output_formats: list[str],
+        output_dir: str,
+    ):
+        self.input = self.Input(urls_or_paths, skip_if_output_exist, playlist_items, verbose)
+
+        self.whisper = self.Whisper(
+            model_name_or_path,
+            task,
+            language,
+            use_faster_whisper,
+            beam_size,
+            ct2_compute_type,
+        )
+
+        self.wit = self.Wit(wit_client_access_tokens, max_cutting_duration)
+
+        self.output = self.Output(
+            min_words_per_segment,
+            save_files_before_compact,
+            save_yt_dlp_responses,
+            output_sample,
+            output_formats,
+            output_dir,
+        )
+
+    def use_wit(self) -> bool:
+        return self.wit.wit_client_access_tokens is not None and self.wit.wit_client_access_tokens != []
+
+    class Input:
+        def __init__(self, urls_or_paths: list[str], skip_if_output_exist: bool, playlist_items: str, verbose: bool):
+            self.urls_or_paths = urls_or_paths
+            self.skip_if_output_exist = skip_if_output_exist
+            self.playlist_items = playlist_items
+            self.verbose = verbose
+
+    class Whisper:
+        def __init__(
+            self,
+            model_name_or_path: str,
+            task: str,
+            language: str,
+            use_faster_whisper: bool,
+            beam_size: int,
+            ct2_compute_type: str,
+        ):
+            if model_name_or_path.endswith('.en'):
+                logging.warn(f'{model_name_or_path} is an English-only model, setting language to English.')
+                language = 'en'
+
+            self.model_name_or_path = model_name_or_path
+            self.task = task
+            self.language = language
+            self.use_faster_whisper = use_faster_whisper
+            self.beam_size = beam_size
+            self.ct2_compute_type = ct2_compute_type
+
+    class Wit:
+        def __init__(self, wit_client_access_tokens: list[str], max_cutting_duration: int):
+            if wit_client_access_tokens is None:
+                self.wit_client_access_tokens = None
+            else:
+                self.wit_client_access_tokens = [
+                    key for key in wit_client_access_tokens if key is not None and key != ''
+                ]
+
+            self.max_cutting_duration = max_cutting_duration
+
+    class Output:
+        def __init__(
+            self,
+            min_words_per_segment: int,
+            save_files_before_compact: bool,
+            save_yt_dlp_responses: bool,
+            output_sample: int,
+            output_formats: list[str],
+            output_dir: str,
+        ):
+            if 'all' in output_formats:
+                output_formats = list(TranscriptType)
+            else:
+                output_formats = [TranscriptType(output_format) for output_format in output_formats]
+
+            if TranscriptType.ALL in output_formats:
+                output_formats.remove(TranscriptType.ALL)
+
+            if TranscriptType.NONE in output_formats:
+                output_formats.remove(TranscriptType.NONE)
+
+            self.min_words_per_segment = min_words_per_segment
+            self.save_files_before_compact = save_files_before_compact
+            self.save_yt_dlp_responses = save_yt_dlp_responses
+            self.output_sample = output_sample
+            self.output_formats = output_formats
+            self.output_dir = output_dir
diff --git a/src/downloader.py b/src/downloader.py
new file mode 100644
index 0000000..026dcbc
--- /dev/null
+++ b/src/downloader.py
@@ -0,0 +1,58 @@
+import json
+import os
+
+from typing import Any
+
+import yt_dlp
+
+
+class Downloader:
+    def __init__(self, playlist_items: str, output_dir: str):
+        self.playlist_items = playlist_items
+        self.output_dir = output_dir
+        self.youtube_dl_with_archive = yt_dlp.YoutubeDL(self._config(os.path.join(self.output_dir, 'archive.txt')))
+        self.youtube_dl_without_archive = yt_dlp.YoutubeDL(self._config(False))
+
+    def _config(self, download_archive: str | bool) -> dict[str, Any]:
+        return {
+            'quiet': True,
+            'verbose': False,
+            'format': 'bestaudio',
+            'extract_audio': True,
+            'outtmpl': os.path.join(self.output_dir, '%(id)s.%(ext)s'),
+            'ignoreerrors': True,
+            'download_archive': download_archive,
+            'playlist_items': self.playlist_items,
+            'postprocessors': [
+                {
+                    'key': 'FFmpegExtractAudio',
+                    'preferredcodec': 'mp3',
+                },
+            ],
+        }
+
+    def download(self, url: str, save_response: bool = False) -> dict[str, Any]:
+        self.youtube_dl_with_archive.download(url)
+        url_data = self.youtube_dl_without_archive.extract_info(url, download=False)
+
+        if save_response:
+            self._save_response(url_data)
+
+        return url_data
+
+    def _save_response(self, url_data: dict[str, Any]) -> None:
+        if '_type' in url_data and url_data['_type'] == 'playlist':
+            for entry in url_data['entries']:
+                if entry and 'requested_downloads' in entry:
+                    self._remove_postprocessors(entry['requested_downloads'])
+        elif 'requested_downloads' in url_data:
+            self._remove_postprocessors(url_data['requested_downloads'])
+
+        file_path = os.path.join(self.output_dir, f"{url_data['id']}.json")
+
+        with open(file_path, 'w', encoding='utf-8') as fp:
+            json.dump(url_data, fp, indent=2, ensure_ascii=False)
+
+    def _remove_postprocessors(self, requested_downloads: list[dict[str, Any]]) -> None:
+        for requested_download in requested_downloads:
+            requested_download.pop('__postprocessors')
diff --git a/src/recognizers/__init__.py b/src/recognizers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/recognizers/whisper_recognizer.py b/src/recognizers/whisper_recognizer.py
new file mode 100644
index 0000000..7870002
--- /dev/null
+++ b/src/recognizers/whisper_recognizer.py
@@ -0,0 +1,104 @@
+import warnings
+
+from typing import Generator
+
+import faster_whisper
+import whisper
+
+from tqdm import tqdm
+
+from src.config import Config
+from src.types.whisper.type_hints import WhisperModel
+
+
+class WhisperRecognizer:
+    def __init__(self, verbose: bool):
+        self.verbose = verbose
+
+    def recognize(
+        self,
+        file_path: str,
+        model: WhisperModel,
+        whisper_config: Config.Whisper,
+    ) -> Generator[dict[str, float], None, list[dict[str, str | float]]]:
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')
+
+            if isinstance(model, whisper.Whisper):
+                whisper_generator = self._recognize_stable_whisper(file_path, model, whisper_config)
+            elif isinstance(model, faster_whisper.WhisperModel):
+                whisper_generator = self._recognize_faster_whisper(file_path, model, whisper_config)
+
+            while True:
+                try:
+                    yield next(whisper_generator)
+                except StopIteration as e:
+                    return e.value
+
+    def _recognize_stable_whisper(
+        self,
+        audio_file_path: str,
+        model: whisper.Whisper,
+        whisper_config: Config.Whisper,
+    ) -> Generator[dict[str, float], None, list[dict[str, str | float]]]:
+        yield {'progress': 0.0, 'remaining_time': None}
+
+        segments = model.transcribe(
+            audio=audio_file_path,
+            verbose=self.verbose,
+            task=whisper_config.task,
+            language=whisper_config.language,
+            beam_size=whisper_config.beam_size,
+        ).segments
+
+        return [
+            {
+                'start': segment.start,
+                'end': segment.end,
+                'text': segment.text.strip(),
+            }
+            for segment in segments
+        ]
+
+    def _recognize_faster_whisper(
+        self,
+        audio_file_path: str,
+        model: faster_whisper.WhisperModel,
+        whisper_config: Config.Whisper,
+    ) -> Generator[dict[str, float], None, list[dict[str, str | float]]]:
+        segments, info = model.transcribe(
+            audio=audio_file_path,
+            task=whisper_config.task,
+            language=whisper_config.language,
+            beam_size=whisper_config.beam_size,
+        )
+
+        converted_segments = []
+        last_end = 0
+        with tqdm(
+            total=round(info.duration, 2),
+            unit='sec',
+            bar_format='{desc}: {percentage:.2f}%|{bar}| {n:.2f}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]',
+            disable=self.verbose is not False,
+        ) as pbar:
+            for segment in segments:
+                converted_segments.append(
+                    {
+                        'start': segment.start,
+                        'end': segment.end,
+                        'text': segment.text.strip(),
+                    }
+                )
+
+                pbar_update = min(segment.end - last_end, info.duration - pbar.n)
+                pbar.update(pbar_update)
+                last_end = segment.end
+
+                yield {
+                    'progress': round(pbar.n / pbar.total * 100, 2),
+                    'remaining_time': (pbar.total - pbar.n) / pbar.format_dict['rate']
+                    if pbar.format_dict['rate'] and pbar.total
+                    else None,
+                }
+
+        return converted_segments
diff --git a/src/recognizers/wit_calling_throttle.py b/src/recognizers/wit_calling_throttle.py
new file mode 100644
index 0000000..18dde0c
--- /dev/null
+++ b/src/recognizers/wit_calling_throttle.py
@@ -0,0 +1,38 @@
+import time
+
+from multiprocessing.managers import BaseManager
+from threading import Lock
+
+
+class WitCallingThrottle:
+    def __init__(self, wit_client_access_tokens_count: int, call_times_limit: int = 1, expired_time: int = 1):
+        self.wit_client_access_tokens_count = wit_client_access_tokens_count
+        self.call_times_limit = call_times_limit
+        self.expired_time = expired_time
+        self.call_timestamps = [[] for _ in range(self.wit_client_access_tokens_count)]
+        self.locks = [Lock() for _ in range(self.wit_client_access_tokens_count)]
+
+    def throttle(self, wit_client_access_token_index: int) -> None:
+        with self.locks[wit_client_access_token_index]:
+            while len(self.call_timestamps[wit_client_access_token_index]) == self.call_times_limit:
+                now = time.time()
+
+                self.call_timestamps[wit_client_access_token_index] = list(
+                    filter(
+                        lambda call_timestamp, now=now: now - call_timestamp < self.expired_time,
+                        self.call_timestamps[wit_client_access_token_index],
+                    )
+                )
+
+                if len(self.call_timestamps[wit_client_access_token_index]) == self.call_times_limit:
+                    time_to_sleep = self.call_timestamps[wit_client_access_token_index][0] + self.expired_time - now
+                    time.sleep(time_to_sleep)
+
+            self.call_timestamps[wit_client_access_token_index].append(time.time())
+
+
+class WitCallingThrottleManager(BaseManager):
+    pass
+
+
+WitCallingThrottleManager.register('WitCallingThrottle', WitCallingThrottle)
diff --git a/src/recognizers/wit_recognizer.py b/src/recognizers/wit_recognizer.py
new file mode 100644
index 0000000..0242f40
--- /dev/null
+++ b/src/recognizers/wit_recognizer.py
@@ -0,0 +1,158 @@
+import json
+import logging
+import multiprocessing
+import os
+import shutil
+import tempfile
+import time
+
+from typing import Generator
+
+import requests
+
+from requests.adapters import HTTPAdapter
+from tqdm import tqdm
+from urllib3.util.retry import Retry
+
+from src.audio_splitter import AudioSplitter
+from src.config import Config
+from src.recognizers.wit_calling_throttle import WitCallingThrottle, WitCallingThrottleManager
+
+
+def init_pool(throttle: WitCallingThrottle) -> None:
+    global wit_calling_throttle
+
+    wit_calling_throttle = throttle
+
+
+class WitRecognizer:
+    def __init__(self, verbose: bool):
+        self.verbose = verbose
+        self.processes_per_wit_client_access_token = min(4, multiprocessing.cpu_count())
+
+    def recognize(
+        self,
+        file_path: str,
+        wit_config: Config.Wit,
+    ) -> Generator[dict[str, float], None, list[dict[str, str | float]]]:
+        temp_directory = tempfile.mkdtemp()
+
+        segments = AudioSplitter().split(
+            file_path,
+            temp_directory,
+            max_dur=wit_config.max_cutting_duration,
+            expand_segments_with_noise=True,
+        )
+
+        retry_strategy = Retry(
+            total=5,
+            status_forcelist=[429, 500, 502, 503, 504],
+            allowed_methods=['POST'],
+            backoff_factor=1,
+        )
+
+        adapter = HTTPAdapter(max_retries=retry_strategy)
+
+        session = requests.Session()
+        session.mount('https://', adapter)
+
+        pool_processes_count = min(
+            self.processes_per_wit_client_access_token * len(wit_config.wit_client_access_tokens),
+            multiprocessing.cpu_count(),
+        )
+
+        with WitCallingThrottleManager() as manager:
+            wit_calling_throttle = manager.WitCallingThrottle(len(wit_config.wit_client_access_tokens))
+
+            with multiprocessing.Pool(
+                processes=pool_processes_count,
+                initializer=init_pool,
+                initargs=(wit_calling_throttle,),
+            ) as pool:
+                async_results = [
+                    pool.apply_async(
+                        self._process_segment,
+                        (
+                            segment,
+                            file_path,
+                            wit_config,
+                            session,
+                            index % len(wit_config.wit_client_access_tokens),
+                        ),
+                    )
+                    for index, segment in enumerate(segments)
+                ]
+
+                transcriptions = []
+
+                with tqdm(total=len(segments), disable=self.verbose is not False) as pbar:
+                    for async_result in async_results:
+                        async_result.wait()
+                        pbar.update(1)
+
+                        transcriptions.append(async_result.get())
+
+                        yield {
+                            'progress': round(len(transcriptions) / len(segments) * 100, 2),
+                            'remaining_time': (pbar.total - pbar.n) / pbar.format_dict['rate']
+                            if pbar.format_dict['rate'] and pbar.total
+                            else None,
+                        }
+
+        shutil.rmtree(temp_directory)
+
+        return transcriptions
+
+    def _process_segment(
+        self,
+        segment: tuple[str, float, float],
+        file_path: str,
+        wit_config: Config.Wit,
+        session: requests.Session,
+        wit_client_access_token_index: int,
+    ) -> dict[str, str | float]:
+        wit_calling_throttle.throttle(wit_client_access_token_index)
+
+        segment_file_path, start, end = segment
+
+        with open(segment_file_path, 'rb') as mp3_file:
+            audio_content = mp3_file.read()
+
+        retries = 5
+
+        text = ''
+        while retries > 0:
+            try:
+                response = session.post(
+                    'https://api.wit.ai/speech',
+                    headers={
+                        'Accept': 'application/vnd.wit.20200513+json',
+                        'Content-Type': 'audio/mpeg3',
+                        'Authorization': f'Bearer {wit_config.wit_client_access_tokens[wit_client_access_token_index]}',
+                    },
+                    data=audio_content,
+                )
+
+                if response.status_code == 200:
+                    text = json.loads(response.text)['text']
+                    break
+                else:
+                    retries -= 1
+                    time.sleep(self.processes_per_wit_client_access_token + 1)
+            except Exception:
+                retries -= 1
+                time.sleep(self.processes_per_wit_client_access_token + 1)
+
+        if retries == 0:
+            logging.warn(
+                f"The segment from `{file_path}` file that starts at {start} and ends at {end}"
+                " didn't transcribed successfully."
+            )
+
+        os.remove(segment_file_path)
+
+        return {
+            'start': start,
+            'end': end,
+            'text': text.strip(),
+        }
diff --git a/src/types/__init__.py b/src/types/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/types/transcript_type.py b/src/types/transcript_type.py
new file mode 100644
index 0000000..3cfa105
--- /dev/null
+++ b/src/types/transcript_type.py
@@ -0,0 +1,15 @@
+from enum import Enum
+
+
+class TranscriptType(Enum):
+    ALL = 'all'
+    TXT = 'txt'
+    SRT = 'srt'
+    VTT = 'vtt'
+    CSV = 'csv'
+    TSV = 'tsv'
+    JSON = 'json'
+    NONE = 'none'
+
+    def __str__(self):
+        return self.value
diff --git a/src/types/whisper/__init__.py b/src/types/whisper/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/types/whisper/type_hints.py b/src/types/whisper/type_hints.py
new file mode 100644
index 0000000..cd13ed9
--- /dev/null
+++ b/src/types/whisper/type_hints.py
@@ -0,0 +1,11 @@
+from typing import TypeVar
+
+import faster_whisper
+import whisper
+
+
+WhisperModel = TypeVar(
+    'WhisperModel',
+    whisper.Whisper,
+    faster_whisper.WhisperModel,
+)
diff --git a/src/utils/__init__.py b/src/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/utils/cli_utils.py b/src/utils/cli_utils.py
new file mode 100644
index 0000000..fa82c8f
--- /dev/null
+++ b/src/utils/cli_utils.py
@@ -0,0 +1,200 @@
+import argparse
+import importlib.metadata
+import re
+
+from src.types.transcript_type import TranscriptType
+
+
+PLAYLIST_ITEMS_RE = re.compile(
+    r'''(?x)
+        (?P<start>[+-]?\d+)?
+        (?P<range>[:-]
+            (?P<end>[+-]?\d+|inf(?:inite)?)?
+            (?::(?P<step>[+-]?\d+))?
+        )?'''
+)
+
+
+def parse_args(argv: list[str]) -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        '--version',
+        action='version',
+        version=importlib.metadata.version('tafrigh'),
+    )
+
+    input_group = parser.add_argument_group('Input')
+
+    input_group.add_argument(
+        'urls_or_paths',
+        nargs='+',
+        help='Video/Playlist URLs or local folder/file(s) to transcribe.',
+    )
+
+    input_group.add_argument(
+        '--skip_if_output_exist',
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help='Whether to skip generating the output if the output file already exists.',
+    )
+
+    input_group.add_argument(
+        '--playlist_items',
+        type=parse_playlist_items,
+        help='Comma separated playlist_index of the items to download. You can specify a range using "[START]:[STOP][:STEP]".',
+    )
+
+    input_group.add_argument(
+        '--verbose',
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help='Whether to print out the progress and debug messages.',
+    )
+
+    whisper_group = parser.add_argument_group('Whisper')
+
+    whisper_group.add_argument(
+        '-m',
+        '--model_name_or_path',
+        default='small',
+        help='Name or path of the Whisper model to use.',
+    )
+
+    whisper_group.add_argument(
+        '-t',
+        '--task',
+        default='transcribe',
+        choices=[
+            'transcribe',
+            'translate',
+        ],
+        help="Whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate').",
+    )
+
+    whisper_group.add_argument(
+        '-l',
+        '--language',
+        default=None,
+        choices=['af', 'am', 'ar', 'as', 'az', 'ba', 'be', 'bg', 'bn', 'bo', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de']
+        + ['el', 'en', 'es', 'et', 'eu', 'fa', 'fi', 'fo', 'fr', 'gl', 'gu', 'ha', 'haw', 'he', 'hi', 'hr', 'ht', 'hu']
+        + ['hy', 'id', 'is', 'it', 'ja', 'jw', 'ka', 'kk', 'km', 'kn', 'ko', 'la', 'lb', 'ln', 'lo', 'lt', 'lv', 'mg']
+        + ['mi', 'mk', 'ml', 'mn', 'mr', 'ms', 'mt', 'my', 'ne', 'nl', 'nn', 'no', 'oc', 'pa', 'pl', 'ps', 'pt', 'ro']
+        + ['ru', 'sa', 'sd', 'si', 'sk', 'sl', 'sn', 'so', 'sq', 'sr', 'su', 'sv', 'sw', 'ta', 'te', 'tg', 'th', 'tk']
+        + ['tl', 'tr', 'tt', 'uk', 'ur', 'uz', 'vi', 'yi', 'yo', 'zh'],
+        help='Language spoken in the audio, skip to perform language detection.',
+    )
+
+    whisper_group.add_argument(
+        '--use_faster_whisper',
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help='Whether to use Faster Whisper implementation.',
+    )
+
+    whisper_group.add_argument(
+        '--beam_size',
+        type=int,
+        default=5,
+        help='Number of beams in beam search, only applicable when temperature is zero.',
+    )
+
+    whisper_group.add_argument(
+        '--ct2_compute_type',
+        default='default',
+        choices=[
+            'default',
+            'int8',
+            'int8_float16',
+            'int16',
+            'float16',
+        ],
+        help='Quantization type applied while converting the model to CTranslate2 format.',
+    )
+
+    wit_group = parser.add_argument_group('Wit')
+
+    wit_group.add_argument(
+        '-w',
+        '--wit_client_access_tokens',
+        nargs='+',
+        help='List of wit.ai client access tokens. If provided, wit.ai APIs will be used to do the transcription, otherwise whisper will be used.',
+    )
+
+    wit_group.add_argument(
+        '--max_cutting_duration',
+        type=int,
+        default=15,
+        choices=range(1, 17),
+        metavar='[1-17]',
+        help='The maximum allowed cutting duration. It should be between 1 and 17.',
+    )
+
+    output_group = parser.add_argument_group('Output')
+
+    output_group.add_argument(
+        '--min_words_per_segment',
+        type=int,
+        default=1,
+        help='The minimum number of words should appear in each transcript segment. Any segment have words count less than this threshold will be merged with the next one. Pass 0 to disable this behavior.',
+    )
+
+    output_group.add_argument(
+        '--save_files_before_compact',
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help='Saves the output files before applying the compact logic that is based on --min_words_per_segment.',
+    )
+
+    output_group.add_argument(
+        '--save_yt_dlp_responses',
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help='Whether to save the yt-dlp library JSON responses or not.',
+    )
+
+    output_group.add_argument(
+        '--output_sample',
+        type=int,
+        default=0,
+        help='Samples random compacted segments from the output and generates a CSV file contains the sampled data. Pass 0 to disable this behavior.',
+    )
+
+    output_group.add_argument(
+        '-f',
+        '--output_formats',
+        nargs='+',
+        default='all',
+        choices=[transcript_type.value for transcript_type in TranscriptType],
+        help='Format of the output file; if not specified, all available formats will be produced.',
+    )
+
+    output_group.add_argument('-o', '--output_dir', default='.', help='Directory to save the outputs.')
+
+    return parser.parse_args(argv)
+
+
+def parse_playlist_items(arg_value: str) -> str:
+    for segment in arg_value.split(','):
+        if not segment:
+            raise ValueError('There is two or more consecutive commas.')
+
+        mobj = PLAYLIST_ITEMS_RE.fullmatch(segment)
+        if not mobj:
+            raise ValueError(f'{segment!r} is not a valid specification.')
+
+        _, _, step, _ = mobj.group('start', 'end', 'step', 'range')
+        if int_or_none(step) == 0:
+            raise ValueError(f'Step in {segment!r} cannot be zero.')
+
+    return arg_value
+
+
+def int_or_none(v, scale=1, default=None, get_attr=None, invscale=1):
+    if get_attr and v is not None:
+        v = getattr(v, get_attr, None)
+
+    try:
+        return int(v) * invscale // scale
+    except (ValueError, TypeError, OverflowError):
+        return default
diff --git a/src/utils/file_utils.py b/src/utils/file_utils.py
new file mode 100644
index 0000000..00f0078
--- /dev/null
+++ b/src/utils/file_utils.py
@@ -0,0 +1,20 @@
+import mimetypes
+
+from pathlib import Path
+
+
+mimetypes.init()
+
+
+def filter_media_files(paths: list[Path]) -> list[Path]:
+    # Filter out non audio or video files
+    filtered_media_files: list[str] = []
+    for path in paths:
+        mime = mimetypes.guess_type(path)[0]
+        if mime is None:
+            continue
+        mime_type = mime.split('/')[0]
+        if mime_type not in ('audio', 'video'):
+            continue
+        filtered_media_files.append(path)
+    return filtered_media_files
diff --git a/src/utils/time_utils.py b/src/utils/time_utils.py
new file mode 100644
index 0000000..4f113a4
--- /dev/null
+++ b/src/utils/time_utils.py
@@ -0,0 +1,15 @@
+def format_timestamp(seconds: float, include_hours: bool = False, decimal_marker: str = '.') -> str:
+    assert seconds >= 0, 'Non-negative timestamp expected'
+
+    total_milliseconds = int(round(seconds * 1_000))
+
+    hours, total_milliseconds = divmod(total_milliseconds, 3_600_000)
+    minutes, total_milliseconds = divmod(total_milliseconds, 60_000)
+    seconds, milliseconds = divmod(total_milliseconds, 1_000)
+
+    if include_hours or hours > 0:
+        time_str = f"{hours:02d}:{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+    else:
+        time_str = f"{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+
+    return time_str
diff --git a/src/utils/whisper/__init__.py b/src/utils/whisper/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/utils/whisper/whisper_utils.py b/src/utils/whisper/whisper_utils.py
new file mode 100644
index 0000000..83b033c
--- /dev/null
+++ b/src/utils/whisper/whisper_utils.py
@@ -0,0 +1,15 @@
+import faster_whisper
+import stable_whisper
+
+from src.config import Config
+from src.types.whisper.type_hints import WhisperModel
+
+
+def load_model(whisper_config: Config.Whisper) -> WhisperModel:
+    if whisper_config.use_faster_whisper:
+        return faster_whisper.WhisperModel(
+            whisper_config.model_name_or_path,
+            compute_type=whisper_config.ct2_compute_type,
+        )
+    else:
+        return stable_whisper.load_model(whisper_config.model_name_or_path)
diff --git a/src/utils/wit/__init__.py b/src/utils/wit/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/utils/wit/file_utils.py b/src/utils/wit/file_utils.py
new file mode 100644
index 0000000..d087d3d
--- /dev/null
+++ b/src/utils/wit/file_utils.py
@@ -0,0 +1,10 @@
+from pathlib import Path
+
+from pydub import AudioSegment
+
+
+def convert_to_mp3(file: Path) -> Path:
+    audio_file = AudioSegment.from_file(str(file))
+    converted_file_path = file.with_suffix('.mp3')
+    audio_file.export(str(converted_file_path), format='mp3')
+    return converted_file_path
diff --git a/src/writer.py b/src/writer.py
new file mode 100644
index 0000000..3944dd6
--- /dev/null
+++ b/src/writer.py
@@ -0,0 +1,161 @@
+import csv
+import json
+import os
+
+from pathlib import Path
+
+from .config import Config
+from .types.transcript_type import TranscriptType
+from .utils import time_utils
+
+
+class Writer:
+    def write_all(
+        self,
+        file_name: str,
+        segments: list[dict[str, str | float]],
+        output_config: Config.Output,
+    ) -> None:
+        if output_config.save_files_before_compact:
+            for output_format in output_config.output_formats:
+                self.write(
+                    output_format,
+                    os.path.join(output_config.output_dir, f'{file_name}-original.{output_format}'),
+                    segments,
+                )
+
+        if not output_config.save_files_before_compact or output_config.min_words_per_segment != 0:
+            compacted_segments = self.compact_segments(segments, output_config.min_words_per_segment)
+
+            for output_format in output_config.output_formats:
+                self.write(
+                    output_format,
+                    os.path.join(output_config.output_dir, f'{file_name}.{output_format}'),
+                    compacted_segments,
+                )
+
+    def write(
+        self,
+        format: TranscriptType,
+        file_path: str,
+        segments: list[dict[str, str | float]],
+    ) -> None:
+        if format == TranscriptType.TXT:
+            self.write_txt(file_path, segments)
+        elif format == TranscriptType.SRT:
+            self.write_srt(file_path, segments)
+        elif format == TranscriptType.VTT:
+            self.write_vtt(file_path, segments)
+        elif format == TranscriptType.CSV:
+            self.write_csv(file_path, segments)
+        elif format == TranscriptType.TSV:
+            self.write_csv(file_path, segments, '\t')
+        elif format == TranscriptType.JSON:
+            self.write_json(file_path, segments)
+
+    def write_txt(
+        self,
+        file_path: str,
+        segments: list[dict[str, str | float]],
+    ) -> None:
+        self._write_to_file(file_path, self.generate_txt(segments))
+
+    def write_srt(
+        self,
+        file_path: str,
+        segments: list[dict[str, str | float]],
+    ) -> None:
+        self._write_to_file(file_path, self.generate_srt(segments))
+
+    def write_vtt(
+        self,
+        file_path: str,
+        segments: list[dict[str, str | float]],
+    ) -> None:
+        self._write_to_file(file_path, self.generate_vtt(segments))
+
+    def write_csv(
+        self,
+        file_path: str,
+        segments: list[dict[str, str | float]],
+        delimiter=',',
+    ) -> None:
+        with open(file_path, 'w', encoding='utf-8') as fp:
+            writer = csv.DictWriter(fp, fieldnames=['text', 'start', 'end'], delimiter=delimiter)
+            writer.writeheader()
+            writer.writerows(segments)
+
+    def write_json(
+        self,
+        file_path: str,
+        segments: list[dict[str, str | float]],
+    ) -> None:
+        with open(file_path, 'w', encoding='utf-8') as fp:
+            json.dump(segments, fp, ensure_ascii=False, indent=2)
+
+    def generate_txt(self, segments: list[dict[str, str | float]]) -> str:
+        return '\n'.join(list(map(lambda segment: segment['text'].strip(), segments))) + '\n'
+
+    def generate_srt(self, segments: list[dict[str, str | float]]) -> str:
+        return ''.join(
+            f"{i}\n"
+            f"{time_utils.format_timestamp(segment['start'], include_hours=True, decimal_marker=',')} --> "
+            f"{time_utils.format_timestamp(segment['end'], include_hours=True, decimal_marker=',')}\n"
+            f"{segment['text'].strip()}\n\n"
+            for i, segment in enumerate(segments, start=1)
+        )
+
+    def generate_vtt(self, segments: list[dict[str, str | float]]) -> str:
+        return 'WEBVTT\n\n' + ''.join(
+            f"{time_utils.format_timestamp(segment['start'])} --> {time_utils.format_timestamp(segment['end'])}\n"
+            f"{segment['text'].strip()}\n\n"
+            for segment in segments
+        )
+
+    def compact_segments(
+        self,
+        segments: list[dict[str, str | float]],
+        min_words_per_segment: int,
+    ) -> list[dict[str, str | float]]:
+        if min_words_per_segment == 0:
+            return segments
+
+        compacted_segments = []
+        tmp_segment = None
+
+        for segment in segments:
+            if tmp_segment:
+                tmp_segment['text'] += f" {segment['text'].strip()}"
+                tmp_segment['end'] = segment['end']
+
+                if len(tmp_segment['text'].split()) >= min_words_per_segment:
+                    compacted_segments.append(tmp_segment)
+                    tmp_segment = None
+            elif len(segment['text'].split()) < min_words_per_segment:
+                tmp_segment = dict(segment)
+            elif len(segment['text'].split()) >= min_words_per_segment:
+                compacted_segments.append(dict(segment))
+
+        if tmp_segment:
+            compacted_segments.append(tmp_segment)
+
+        return compacted_segments
+
+    def is_output_exist(self, file_name: str, output_config: Config.Output):
+        if output_config.save_files_before_compact and not all(
+            Path(output_config.output_dir, f'{file_name}-original.{output_format}').is_file()
+            for output_format in output_config.output_formats
+        ):
+            return False
+
+        if (not output_config.save_files_before_compact or output_config.min_words_per_segment != 0) and not all(
+            Path(output_config.output_dir, f'{file_name}.{output_format}').is_file()
+            for output_format in output_config.output_formats
+        ):
+            return False
+
+        return True
+
+    def _write_to_file(self, file_path: str, content: str) -> None:
+        with open(file_path, 'w', encoding='utf-8') as fp:
+            fp.write(content)