carlfm01

import os
import time
from typing import Optional, Tuple

import torch
from PIL import Image

import onnxruntime as onnxrt
import requests
from transformers import AutoConfig, AutoModelForVision2Seq, TrOCRProcessor, VisionEncoderDecoderModel

carlfm01

var message = new MimeMessage();
    message.From.Add(new MailboxAddress("Your Name", "your-email@yourdomain.com"));
    message.To.Add(new MailboxAddress("", recipientEmail));
    message.Subject = subject;

    message.Body = new TextPart("html")
    {
        Text = body
    };

carlfm01

#!/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

carlfm01

#!/bin/bash
## This gist contains instructions about cuda v10.1 and cudnn 7.6 installation in Ubuntu 18.04 for Tensorflow 2.1.0

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

carlfm01

     total_parameters = 0
     for variable in tf.trainable_variables():
         # shape is an array of tf.Dimension
         shape = variable.get_shape()
         print(shape)
         variable_parameters = 1
         for dim in shape:
             variable_parameters *= dim.value
         total_parameters += variable_parameters
     print(f'TOTAL: {total_parameters}')

carlfm01

for (size_t n = 0; n < totalFrameCnt / 160; n++) {
      get_frame_f32(pWavIn, frameIn, 160);
      float *input = (float *)malloc(480 * sizeof(float));
      float *output = (float *)malloc(160 * sizeof(float));

      //Upsample from 16KHz to 48KHz
      Resample_f32(frameIn, input, 16000, 48000, 160, 1);
  
      float prob = rnnoise_process(pRnnoise, frameOut, input);
      if (isOutput) {

carlfm01

import librosa
import librosa.filters
import numpy as np
import tensorflow as tf
from scipy import signal
from scipy.io import wavfile

def save_wavenet_wav(wav, path, sr, inv_preemphasize, k):
	# wav = inv_preemphasis(wav, k, inv_preemphasize)
	wav *= 32767 / max(0.01, np.max(np.abs(wav)))

carlfm01

using DeepSpeechClient;
using DeepSpeechClient.Interfaces;
using DeepSpeechClient.Models;
using NAudio.Wave;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;

carlfm01

import spacy
from spacy import displacy
from spacy.lang.es.examples import sentences


f = open("result.txt", mode="w+", encoding="utf-8")
with open('data.txt', mode="r", encoding="utf-8") as file:
    data = file.read().replace('\n', '')
    nlp = spacy.load('es_core_news_sm', disable=['ner', 'textcat'])
    doc = nlp(data)