AIAnytime

Nucleus sampling is a technique used in large language models to control the randomness and diversity of generated text. It works by sampling from only the most likely tokens in the model’s predicted distribution.

The key parameters are:

Temperature: Controls randomness, higher values increase diversity.

Top-p (nucleus): The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.

Top-k: Sample from the k most likely next tokens at each step. Lower k focuses on higher probability tokens.

AIAnytime

import streamlit as st
import os
from tortoise.models.classifier import AudioMiniEncoderWithClassifierHead
from glob import glob
import io
import librosa
import plotly.express as px
import torch
import torch.nn.functional as F
import torchaudio

AIAnytime

# Assembly AI speech to text
def assemblyai_stt(audio_filename):
    with open(audio_filename , "rb") as f:
        response = requests.post(base_url + "/upload",
                                headers=headers,
                                data=f)

    upload_url = response.json()["upload_url"]
    data = {
        "audio_url": upload_url

AIAnytime

# PyTube function for YouTube video
def save_audio(url):
    yt = YouTube(url)
    video = yt.streams.filter(only_audio=True).first()
    out_file = video.download()
    base, ext = os.path.splitext(out_file)
    file_name = base + '.mp3'
    try:
        os.rename(out_file, file_name)
    except WindowsError:

AIAnytime

import streamlit as st
import fastai
from deoldify.visualize import *

def image_colorizer(url):
    colorizer = get_image_colorizer(artistic=True)
    source_url = url
    render_factor = 35
    watermarked = True 

AIAnytime

# -*- coding: utf-8 -*-
"""Reverse SD.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1ci11cstH7uM9SPb6q2hb7f-IiZHYvcDq
"""

!pip install clip-interrogator==0.6.0

AIAnytime

HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem">{}</div>"""

AIAnytime

from fastapi import FastAPI
import pickle
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

app = FastAPI()

# Load the Tfidf and Naive Bayes models
tfidf = pickle.load(open("tf_idf.pkt", "rb"))

AIAnytime

# -*- coding: utf-8 -*-
"""Toxicity Classifier NLP.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1UUZzQgrRUcLujGxbmhE30AlQALMsYXCm
"""

# Commented out IPython magic to ensure Python compatibility.

AIAnytime

import noisereduce as nr
from scipy.io import wavfile
 
# load data
rate, data = wavfile.read("voice_with_noise.wav")
 
# perform noise reduction
reduced_noise = nr.reduce_noise(y=data, sr=rate) It was originally published on https://www.apriorit.com/