vwxyzjn

from collections import defaultdict
import random
from huggingface_hub import hf_hub_download
from datasets import Dataset
import numpy as np
import pandas as pd
from transformers import AutoTokenizer
from rich.console import Console
from rich.table import Table
from trl import DPOTrainer

ArthurZucker

from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache
import torch
from typing import Optional
device = "cuda"

# Copied from the gpt-fast repo
def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization
    q = torch.empty_like(probs_sort).exponential_(1)
    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)

MihailCosmin

#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

denguir

How to install CUDA / cuDNN / TensorRT on Ubuntu

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

init27

# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

ketanhdoshi

#Spectrogram is a 2D numpy array
print(type(mel_sgram), mel_sgram.shape)
# <class 'numpy.ndarray'> (128, 134)

ketanhdoshi

# use the decibel scale to get the final Mel Spectrogram
mel_sgram = librosa.amplitude_to_db(mel_scale_sgram, ref=np.min)
librosa.display.specshow(mel_sgram, sr=sample_rate, x_axis='time', y_axis='mel')
plt.colorbar(format='%+2.0f dB')

mowshon

import moviepy.editor as mp
import math
from PIL import Image
import numpy


def zoom_in_effect(clip, zoom_ratio=0.04):
    def effect(get_frame, t):
        img = Image.fromarray(get_frame(t))
        base_size = img.size

alecjacobson

#!/bin/bash

if [ -z "$2" ];then
  echo 'USAGE:

    denoise input.mov output.mov

OR 

    denoise input.mov output.mov [ambient-noise-start-time] [ambient-noise-duration] [sox-noisered-amount] [sox-norm-param]

amir-saniyan

# --------------------------------------------------

function (build_external_project target file_name)

    set(CMAKELIST_CONTENT "
        cmake_minimum_required(VERSION ${CMAKE_MINIMUM_REQUIRED_VERSION})

        project(build_external_project)

        file(MD5 \"${file_name}\" FILE_HASH)