lucasnewman

import datetime
from pathlib import Path

import torch
import torchaudio
from torchaudio.transforms import MelSpectrogram

from einops import rearrange

from vocos import Vocos

sgammon

There appears to be a string encoded in the binary payload:
https://gist.github.com/q3k/af3d93b6a1f399de28fe194add452d01#file-hashes-txt-L115

Which functions as a killswitch:

https://piaille.fr/@zeno/112185928685603910

Thus, one workaround for affected systems might be to add this to `/etc/environment`:

```

smx-smx

XZ Backdoor symbol deobfuscation. Updated as i make progress

OrionReed

// 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks.
// You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/)
(() => {
  const SHOW_SIDES = false; // color sides of DOM nodes?
  const COLOR_SURFACE = true; // color tops of DOM nodes?
  const COLOR_RANDOM = false; // randomise color?
  const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com)
  const MAX_ROTATION = 180; // set to 360 to rotate all the way round
  const THICKNESS = 20; // thickness of layers
  const DISTANCE = 10000; // ¯\\_(ツ)_/¯

thomwolf

""" 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

ehartford

import copy
import os
import safetensors.torch
import glob
import json


def transform_st(path: str, out_dir: str):
    data = safetensors.torch.load_file(path)
    old_keys = list(data.keys())

abacaj

{"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 

cgfarmer4

class AudioSessionManager: NSObject, ObservableObject {
    @Published var microphones: [AVCaptureDevice] = []
    var captureSession: AVCaptureSession = .init()
    var audioOutput: AVCaptureAudioDataOutput?
    var configured: Bool = false
    
    private var audioInput: AVCaptureDeviceInput?
    
    let dataOutputQueue = DispatchQueue(label: "audio_queue",
                                        qos: .userInteractive,

ChrisHayduk

"""

The code below combines approaches published by both @eugene-yh and @jinyongyoo on Github. 

Thanks for the contributions guys!

"""

import torch
import peft

briansemrau

# This script was adapted from merge.py from the KoboldAI discord server.
# I believe the original author is concedo

import os
import gc
import json
import shutil
import resource

import torch