Muhammad Irfan mirfan899

ffmpeg

Converting Audio into Different Formats / Sample Rates

Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma

You can get the list of supported formats with:
ffmpeg -formats

Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and sample rate to 22050 Hz:

Ubuntu 20.04 on a 15" Retina MacBook Pro (Mid-2014)

These are notes from my efforts to get Ubuntu 20.04 installed on my older MacBook Pro. I'm making this gist public in the hopes that it's helpful to others.

I did a Minimal install, but selected the option to install additional 3rd-party drivers.

Wifi doesn't work during the install (because it requires a 3rd-party driver), so you won't be able to choose to download updates while installing. No big deal, run a software update after the install.

The installer takes about 25 minutes to complete. Post-install, most things work. The only driver I had to manually install was for the FaceTime camera. More on that below.

	<?php

	namespace App\BotMan\Middleware;

	use Mpociot\BotMan\Message;
	use Mpociot\BotMan\Http\Curl;
	use Mpociot\BotMan\Interfaces\HttpInterface;
	use Mpociot\BotMan\Interfaces\DriverInterface;
	use Mpociot\BotMan\Interfaces\MiddlewareInterface;

	# -- coding: utf-8 --
	import os
	import uuid

	from flask import Flask, render_template, redirect, url_for, request
	from flask_uploads import UploadSet, configure_uploads, IMAGES, patch_request_class
	from flask_wtf import FlaskForm
	from flask_wtf.file import FileField, FileRequired, FileAllowed
	from wtforms import SubmitField

	import cv2 # opencv
	import numpy as np

	font_scale = 1.5
	font = cv2.FONT_HERSHEY_PLAIN

	# set the rectangle background to white
	rectangle_bgr = (255, 255, 255)
	# make a black image
	img = np.zeros((500, 500))

	import math
	import torch
	from transformers import BertTokenizer, BertModel, BertForMaskedLM, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer


	# Load pre-trained model (weights)
	model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
	model.eval()
	# Load pre-trained model tokenizer (vocabulary)
	tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')

	function import_extract_first {
	echo "name:ID(Concept)" > concepts.txt
	cat data-concept-instance-relations.txt \| cut -d $'\t' -f 1 \| sort \| uniq >> concepts.txt

	echo "name:ID(Instance)" > instances.txt
	cat data-concept-instance-relations.txt \| cut -d $'\t' -f 2 \| sort \| uniq >> instances.txt

	echo $':END_ID(Concept)\t:START_ID(Instance) relations:int' > is_a.hdr

	$NEO4J_HOME/bin/neo4j-import --into concepts.db --id-type string --delimiter TAB --bad-tolerance 13000000 --skip-duplicate-nodes true --skip-bad-relationships true \

	wget --no-check-certificate --content-disposition https://github.com/joyent/node/tarball/v0.7.1
	# --no-check-cerftificate was necessary for me to have wget not puke about https
	curl -LJO https://github.com/joyent/node/tarball/v0.7.1

	import os

	nltkdata_wn = '/path/to/nltk_data/corpora/wordnet/'
	wn31 = "http://wordnetcode.princeton.edu/wn3.1.dict.tar.gz"
	if not os.path.exists(nltkdata_wn+'wn3.0'):
	os.mkdir(nltkdata_wn+'wn3.0')
	os.system('mv '+nltkdata_wn+"* "+nltkdata_wn+"wn3.0/")
	if not os.path.exists('wn3.1.dict.tar.gz'):
	os.system('wget '+wn31)
	os.system("tar zxf wn3.1.dict.tar.gz -C "+nltkdata_wn)