diff --git a/bot.py b/bot.py
new file mode 100644
index 0000000..13cd026
--- /dev/null
+++ b/bot.py
@@ -0,0 +1,80 @@
+import logging
+from aiogram import Bot, Dispatcher, types, executor
+from dotenv import load_dotenv
+from pathlib import Path
+from spectr import create_spectrogram
+import os
+
+dotenv_path = Path('.env.txt')
+load_dotenv(dotenv_path=dotenv_path)
+
+# logging so don't miss important messages
+logging.basicConfig(level=logging.INFO)
+bot_token = os.getenv("BOT_TOKEN")
+# bot's object
+bot = Bot(token=bot_token)
+dp = Dispatcher(bot)
+
+
+# handler for /start
+@dp.message_handler(commands='start')
+async def cmd_start(message: types.Message):
+    await message.answer('Hello!')
+
+
+user_home_dir = os.path.expanduser('~')
+user = os.path.split(user_home_dir)[-1]
+disk = os.path.split(user_home_dir)[0]
+common_path = os.path.join(disk, user, 'Desktop')
+
+try:
+    if not os.path.isdir('Downloaded audio'):
+        os.chdir(common_path)
+        os.mkdir('Downloaded audio"')
+except:
+    print()
+
+
+# handler for audio messages, voice messages and photos
+@dp.message_handler(content_types=['audio', 'voice', 'photo'])
+async def show_spectrogram(message: types.Message):
+    unique_id = path_to_file = format_file = file_info = ''
+    # getting a directory for saving a file
+    directory = os.path.join(disk, user, 'Desktop', 'Downloaded audio')
+    # checking a type of message
+    if message.audio is not None:
+        audio_id = message.audio.file_id
+        unique_id = message.audio.file_unique_id
+        file_info = await bot.get_file(audio_id)
+        path_to_file = os.path.join(directory, f'{unique_id}.mp3')
+        format_file = 'mp3'
+    elif message.voice is not None:
+        audio_id = message.voice.file_id
+        unique_id = message.voice.file_unique_id
+        file_info = await bot.get_file(audio_id)
+        path_to_file = os.path.join(directory, f'{unique_id}.ogg')
+        format_file = 'ogg'
+
+    # downloading a file
+    try:
+        await bot.download_file(file_info.file_path,
+                                destination=os.path.join(path_to_file))
+        await message.answer("Your audio is successfully downloaded")
+    except:
+        await message.answer("Error downloading")
+
+    create_spectrogram(path_to_file, unique_id)
+
+    path_to_photo = os.path.join(disk, user, 'Desktop', 'Created spectrograms', f'{unique_id}.png')
+    # showing spectrogram
+    print(path_to_photo)
+    try:
+        with open(path_to_photo, 'rb') as photo:
+            await message.answer_photo(photo, caption='Your spectrogram')
+        photo.close()
+    except:
+        await message.answer("Error showing")
+
+
+if __name__ == '__main__':
+    executor.start_polling(dp, skip_updates=True)
diff --git a/spectr.py b/spectr.py
new file mode 100644
index 0000000..9c75191
--- /dev/null
+++ b/spectr.py
@@ -0,0 +1,47 @@
+from pydub import AudioSegment
+import matplotlib.pyplot as plt
+from scipy.io import wavfile
+from scipy.signal import spectrogram
+import numpy as np
+import os
+
+user_home_dir = os.path.expanduser('~')
+user = os.path.split(user_home_dir)[-1]
+disk = os.path.split(user_home_dir)[0]
+common_path = os.path.join(disk, user, 'Desktop')
+
+
+def create_spectrogram(path_to_file: str, unique_id: str):
+    input_file = f'{path_to_file}'
+    output_file = f'{path_to_file}.wav'
+
+    if input_file[-3:] == 'mp3':
+        sound = AudioSegment.from_mp3(input_file)
+        sound.export(output_file, format='wav')
+    elif input_file[-3:] == 'ogg':
+        sound = AudioSegment.from_ogg(input_file)
+        sound.export(output_file, format='wav')
+
+    output_file_mono = f'{output_file}_mono.wav'
+
+    stereo_audio = AudioSegment.from_file(output_file, format='wav')
+    mono_audios = stereo_audio.split_to_mono()
+    mono_left = mono_audios[0].export(output_file_mono, format='wav')
+
+    samplerate, data = wavfile.read(output_file_mono)
+    f, t, Sxx = spectrogram(data, samplerate)
+
+    plt.pcolormesh(1000 * t, f / 1000, 10 * np.log10(Sxx / Sxx.max()), vmin=-120, vmax=0, cmap='inferno')
+    plt.ylabel('Frequency [kHz]')
+    plt.xlabel('Time [ms]')
+    plt.colorbar()
+    img = f'{unique_id}.png'
+    try:
+        if not os.path.isdir('Created spectrograms'):
+            os.chdir(common_path)
+            os.mkdir('Created spectrograms')
+    except:
+        print()
+    path = os.path.join(disk, user, 'Desktop', 'Created spectrograms', img)
+    plt.savefig(path)
+    plt.clf()
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