Moved to https://api.fmhy.net
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import argparse | |
import json | |
import logging | |
import os | |
import re | |
import shutil | |
from concurrent.futures import ProcessPoolExecutor, as_completed | |
from dataclasses import dataclass | |
from datetime import datetime | |
from typing import Any, Callable, Dict, List, Literal, Optional, Tuple |
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import asyncio | |
import math | |
import re | |
from collections import defaultdict | |
from datetime import datetime, timedelta | |
from typing import Callable, Dict, List, Optional, Union | |
class BasicDBConnector: | |
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# AUTOMATE UDIO | |
# by DADABOTS dadabots.com | |
# was working on 2024-04-13 | |
# tldr; get your login cookie, use it to automate udio from python | |
import requests | |
import json | |
from time import sleep | |
import re |
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""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory | |
git clone https://github.com/myshell-ai/OpenVoice | |
cd OpenVoice | |
git clone https://huggingface.co/myshell-ai/OpenVoice | |
cp -r OpenVoice/* . | |
pip install whisper pynput pyaudio | |
""" | |
from openai import OpenAI | |
import time |
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#pip install git+https://github.com/huggingface/transformers.git | |
import datetime | |
import sys | |
from transformers import pipeline | |
from transformers.pipelines.audio_utils import ffmpeg_microphone_live | |
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=0) | |
sampling_rate = pipe.feature_extractor.sampling_rate |
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"""Performs automatic speed edits to audio books. | |
Example usage: | |
Assuming you have an audiobook book.aax on your Desktop: | |
1. Convert it to wav: | |
ffmpeg -i ~/Desktop/book.aax ~/Desktop/book.wav | |
2. Adjust the speed: |
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