kopia lustrzana https://codeberg.org/pluja/openai-telegram-bot
Bug fixes and optimizations
pluja
rodzic
62d1aef168
commit
edf2131a2e
21
example.env
21
example.env
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@ -1,3 +1,6 @@
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# # # # # # # # # #
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# OpenAI / ChatGPT #
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# # # # # # # # # #
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OPENAI_API_KEY=your-openai-api-key
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OPENAI_MODEL=gpt-3.5-turbo
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@ -8,11 +11,21 @@ CHATGPT_TEMPERATURE=1.0
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# Use Whisper transcript from voice message with ChatGPT
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WHISPER_TO_GPT=1
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# TTS Options
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# # # # # # # #
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# TTS Options #
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# # # # # # # #
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ENABLE_TTS=1
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# If USE_TTS=1, you can set the following options
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VOICE_LANGUAGE_LIST=en,es,fr,it,pt,ca
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DEFAULT_VOICE_LANGUAGE=en
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# If ENABLE_TTS=1, you can set the following options
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# Remove any language you don't want to not downlad its voice
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VOICE_LANGUAGE_LIST=en,es,fr,it,pt,ca,no,nl,de
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DEFAULT_VOICE_LANGUAGE=en
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# Do not change this line
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LANGUAGES_H_SR=en,fr,nl,no
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# # # # # # # # # #
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# Telegram Options #
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# # # # # # # # # #
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BOT_TOKEN=your-telegram-bot-token
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BOT_ALLOWED_USERS= XXXX,YYYY # Comma separated list of Telegram user IDs
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91
main.py
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main.py
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@ -1,10 +1,10 @@
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import asyncio
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import logging
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import os
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import tempfile
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import wave
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from functools import wraps
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from io import BytesIO
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import subprocess
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from typing import Tuple
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import openai
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from aiogram import Bot, Dispatcher, types
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@ -12,8 +12,8 @@ from aiogram.contrib.middlewares.logging import LoggingMiddleware
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from aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup, ParseMode
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from aiogram.utils import executor
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from dotenv import load_dotenv
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from pydub import AudioSegment
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from langdetect import detect
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from pydub import AudioSegment
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import database
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@ -46,6 +46,7 @@ WHISPER_TO_GPT = bool(int(os.environ.get("WHISPER_TO_GPT")))
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ENABLE_TTS = bool(int(os.environ.get("ENABLE_TTS")))
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DEFAULT_VOICE_LANGUAGE = os.environ.get("DEFAULT_VOICE_LANGUAGE")
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VOICE_LANGUAGE_LIST = os.environ.get("VOICE_LANGUAGE_LIST")
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LANGUAGES_22050 = os.environ.get("LANGUAGES_H_SR").split(",")
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MAX_USER_CONTEXT = int(os.environ.get("CHATGPT_MAX_USER_CONTEXT"))
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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@ -107,7 +108,15 @@ def generate_settings_markup(chat_id: str) -> InlineKeyboardMarkup:
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return InlineKeyboardMarkup(inline_keyboard=keyboard)
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async def text_to_voice(text: str, language: str = None) -> BytesIO:
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async def send_voice_message(chat_id, assistant_message):
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await bot.send_chat_action(chat_id, action=types.ChatActions.TYPING)
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audio_data, _ = await text_to_voice(assistant_message)
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audio_data.seek(0) # Reset the buffer's position to the beginning
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await bot.send_voice(chat_id, audio_data)
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async def text_to_voice(text: str, language: str = None) -> Tuple[BytesIO, str]:
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binary_path = "./piper/piper"
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if language is None:
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@ -115,34 +124,30 @@ async def text_to_voice(text: str, language: str = None) -> BytesIO:
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model_path = f"./piper/voices/{language}.onnx"
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# Generate a unique temporary filename with '.ogg' extension
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with tempfile.NamedTemporaryFile(suffix=".ogg", delete=False) as tmp:
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tmp_filename = tmp.name
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text = text.replace("\n", ". ")
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# Run the binary with the escaped text as input and the temp file as output
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with open(tmp_filename, "wb") as tmp_file:
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process = subprocess.Popen(
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[binary_path, "--model", model_path, "--output_file", "-"],
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stdin=subprocess.PIPE,
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stdout=tmp_file,
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stderr=subprocess.PIPE,
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text=True,
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encoding="utf8",
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)
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# Remove all newlines from the text so that the text is read as a single sentence
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text = text.replace("\n", ". ")
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process.communicate(input=text)
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cmd = [binary_path, "--model", model_path, "--output_raw"]
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proc = await asyncio.create_subprocess_exec(
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*cmd, stdin=asyncio.subprocess.PIPE, stdout=asyncio.subprocess.PIPE
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)
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# Open the file in binary mode and read its content into BytesIO object
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with open(tmp_filename, "rb") as file:
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bytes_io = BytesIO(file.read())
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stdout, _ = await proc.communicate(input=text.encode("utf-8"))
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# Delete the temporary file
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os.remove(tmp_filename)
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# Create a new BytesIO object to store the WAV file.
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wav_file = BytesIO()
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with wave.open(wav_file, "wb") as wf:
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# Assuming 1 channel, 16 bits per sample, and 22050 samples per second.
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(22050 if language in LANGUAGES_22050 else 16000)
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wf.writeframes(stdout)
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# Return the BytesIO object
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return bytes_io
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wav_file.seek(0)
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audio = AudioSegment.from_file(wav_file, format="wav")
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audio = audio.export(format="ogg", codec="libopus", parameters=["-vbr", "on"])
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return BytesIO(audio.read()), "ogg"
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def restricted(func):
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@ -254,7 +259,7 @@ async def usage(message: types.Message) -> None:
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- Generated {user_usage["dalle"]} images with DALL-E.
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- Transcribed {round(float(user_usage["whisper"]) / 60.0, 2)}min with Whisper.
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Total spent: ${user_spent} ({user_percentage:.2f}% of total)
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User total: ${user_spent} ({user_percentage:.2f}% of total)
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Total usage:
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- ChatGPT tokens: {total_usage["chatgpt"]}
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@ -297,9 +302,9 @@ async def attachment(message: types.Message):
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await bot.send_chat_action(chat_id, action=types.ChatActions.TYPING)
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transcript = {"text": ""}
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audioMessage = False
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# Handle media types
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if message.voice:
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user_data["usage"]["whisper"] += message.voice.duration
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file_id = message.voice.file_id
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@ -317,16 +322,19 @@ async def attachment(message: types.Message):
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await message.reply("Can't handle such file. Reason: unknown.")
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return
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# Download file from Telegram
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file = await bot.get_file(file_id)
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user_id = message.chat.id
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await file.download(f"{user_id}.{file_format}")
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# Convert audio to mp3 if needed, because OpenAI doesn't support ogg
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if file_format == "ogg":
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ogg_audio = AudioSegment.from_file(f"{user_id}.ogg", format="ogg")
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ogg_audio.export(f"{user_id}.mp3", format="mp3")
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os.remove(f"{user_id}.ogg")
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file_format = "mp3"
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# Transcribe audio with OpenAI API
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with open(f"{user_id}.{file_format}", "rb") as audio_file:
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try:
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await bot.send_chat_action(chat_id, action=types.ChatActions.TYPING)
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@ -336,25 +344,28 @@ async def attachment(message: types.Message):
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await message.reply("Transcript failed.")
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os.remove(f"{user_id}.{file_format}")
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return
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os.remove(f"{user_id}.{file_format}")
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os.remove(f"{user_id}.{file_format}")
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# Handle empty transcript
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if transcript["text"] == "":
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transcript["text"] = "[Silence]"
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# If whisper_to_chat is enabled, send transcript to ChatGPT and send its response along with the transcript
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chatGPT_response = False
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if audioMessage and user_data["options"]["whisper_to_chat"]:
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chatGPT_response, user_data = await messageGPT(
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transcript["text"], str(chat_id), message.from_user.full_name, user_data
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)
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transcript["text"] = "> " + transcript["text"] + "\n\n" + chatGPT_response
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transcript["text"] = ''.join(["> ", transcript["text"], "\n\n", chatGPT_response])
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# Send transcript (and ChatGPT response if enabled)
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await message.reply(transcript["text"])
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# Send ChatGPT response as voice message with piper TTS if enabled
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if user_data["options"]["assistant_voice_chat"] and chatGPT_response:
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await bot.send_chat_action(chat_id, action=types.ChatActions.TYPING)
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voice_data = await text_to_voice(chatGPT_response)
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await message.reply_voice(voice_data)
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asyncio.create_task(send_voice_message(chat_id, chatGPT_response))
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# Update user data
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database.update_user(str(chat_id), user_data)
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@ -420,9 +431,7 @@ async def chat(message: types.Message):
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await message.reply(assistant_message, parse_mode=ParseMode.MARKDOWN)
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if user_data["options"]["assistant_voice_chat"]:
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await bot.send_chat_action(chat_id, action=types.ChatActions.TYPING)
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voice_data = await text_to_voice(assistant_message)
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await message.reply_voice(voice_data)
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asyncio.create_task(send_voice_message(chat_id, assistant_message))
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if __name__ == "__main__":
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