liangfu

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:

liangfu

apt update
apt install -y emacs-nox aptitude screen xcscope-el gdb nano || echo 0 && echo "Success"
python3 -m pip install git-remote-codecommit opennmt-tf==1.25.1

cat << EOF > ~/.gdbinit
set print thread-events off
define hook-run
  catch throw
end
define hook-quit

liangfu

import numpy as np
import time
import math

def burn_cpu():
    a = np.random.rand(1024, 1024)
    b = np.random.rand(1024, 1024)
    tic = time.time()
    c = np.dot(a, b)
    toc = time.time()

liangfu

import boto3

def recursive_copy(src_bucket, src_prefix, dst_bucket, dst_prefix):
    def get_partition():
        if boto3.session.Session().region_name in ["cn-northwest-1", "cn-north-1"]:
            return "aws-cn"
        else:
            return "aws"
    partition = get_partition()
    region_name = boto3.session.Session().region_name

liangfu

#!/bin/sh

ProgName=$(basename $0)
instance_ids=i-05806fb95d65fffff
region=us-west-2

sub_help(){
    echo "Usage: $ProgName <subcommand> [options]\n"
    echo "Subcommands:"
    echo "    start     Start mldev instances"

liangfu

# Benchmark inference time of pretrained AutoGluon Tabular models
"""
set -e
DEV_BRANCH=reset-thread-2
git checkout master
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py --sample=1
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py
git checkout $DEV_BRANCH
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py --sample=1
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py

liangfu

""" Example script for defining and using custom models in AutoGluon Tabular """

from autogluon.core.utils import infer_problem_type
from autogluon.tabular import TabularDataset, TabularPredictor
from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
from autogluon.core.data import LabelCleaner
from autogluon.core.models import AbstractModel

from skl2onnx import convert_sklearn, get_model_alias
from skl2onnx.common._registration import get_shape_calculator, get_converter

liangfu

#include <stdio.h>
#include <inttypes.h>

static const uint32_t kSingleSignMask = 0x80000000;
static const uint32_t kSingleExpMask = 0x7f800000;
static const uint32_t kSingleMantMask = 0x007fffff;

static const uint32_t kHalfSignMask = 0x8000;
static const uint32_t kHalfExpMask = 0x7c00;
static const uint32_t kHalfMantMask = 0x03ff;

liangfu

# Inspired by https://keon.io/deep-q-learning/

# python3 -m pip install keras==2.3.1 tensorflow-gpu==1.15 gym==0.18.3
# python3 -m pip install 'h5py==2.10.0' --force-reinstall

import random
import gym
import math
import numpy as np
import keras

liangfu

import random
random.seed(111)


def bosonnlp_to_bio2(origfile, trainfile, valfile):
    val_ratio = 0.2

    traindata = []
    valdata = []
    with open(origfile, 'rt') as fp: