#import skvideo
import argparse
from ffmpy import FFmpeg
import os, sys
import pyaudio
import wave
import numpy as np
import matplotlib.pyplot as plt

import wavutil as wu

FFMPEGDIR='c:/Utils/ffmpeg/bin/'
#ffmpeg -i emotions.mp4 -vn -acodec pcm_s16le -ar 44100 -ac 2 emotions.wav

DEMUX=False			#split video and audio into 2 .mp4 files
EXTRACTWAV=False	#extract audio stream into a .wav file
PLAYWAVE=False

# Command-line arguments to the system -- you can extend these if you want, but you shouldn't need to modify any of them
def _parse_args():
    parser = argparse.ArgumentParser(description='trainer.py')
    parser.add_argument('--model', type=str, default='BAD', help='model to run (BAD, CLASSIFIER)')
    parser.add_argument('--train_path', type=str, default='data/HD Training Data', help='path to train set (you should not need to modify)')
    parser.add_argument('--dev_path', type=str, default='data/HD Training Data', help='path to dev set (you should not need to modify)')
    parser.add_argument('--blind_test_path', type=str, default='data/eng.testb.blind', help='path to dev set (you should not need to modify)')
    parser.add_argument('--test_output_path', type=str, default='eng.testb.out', help='output path for test predictions')
    parser.add_argument('--no_run_on_test', dest='run_on_test', default=True, action='store_false', help='skip printing output on the test set')
    args = parser.parse_args()
    return args


if __name__=='__main__':
	args = _parse_args()
	print(args)

	inputvid = 'test.mp4'
	basename = os.path.basename(inputvid)
	base, ext = os.path.splitext(basename)
	
	if DEMUX:
		ff = FFmpeg(
			inputs={inputvid: None},
			outputs={
				base + '-video.mp4': ['-map', '0:0', '-c:a', 'copy', '-f', 'mp4', '-y'],
				base + '-audio.mp4': ['-map', '0:1', '-c:a', 'copy', '-f', 'mp4', '-y']
			}
		)
		print(ff.cmd)
		ff.run()

	wavefile = base + '.wav'

	if EXTRACTWAV:
		ff = FFmpeg(
			inputs={inputvid: None},
			outputs={
				wavefile: ['-vn', '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', '-y']
			}
		)
		print(ff.cmd)
		ff.run()

	if PLAYWAVE:
		wu.playWav(wavefile)

	wavarr = wu.loadWav(wavefile)	#(framerate, np)
	print(wavarr)

	fs = wavarr[0]
	signal = wavarr[1]
	Time=np.linspace(0, len(signal)/fs, num=len(signal))

	plt.figure(1)
	plt.title('Signal Wave...')
	plt.plot(Time, signal)
	plt.savefig("plot1.png")
	plt.show()

9b7773b312c959eaa76bee83f49aea9593d7a7a2
<mask token>


def _parse_args():
    parser = argparse.ArgumentParser(description='trainer.py')
    parser.add_argument('--model', type=str, default='BAD', help=
        'model to run (BAD, CLASSIFIER)')
    parser.add_argument('--train_path', type=str, default=
        'data/HD Training Data', help=
        'path to train set (you should not need to modify)')
    parser.add_argument('--dev_path', type=str, default=
        'data/HD Training Data', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--blind_test_path', type=str, default=
        'data/eng.testb.blind', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--test_output_path', type=str, default=
        'eng.testb.out', help='output path for test predictions')
    parser.add_argument('--no_run_on_test', dest='run_on_test', default=
        True, action='store_false', help='skip printing output on the test set'
        )
    args = parser.parse_args()
    return args


<mask token>

<mask token>


def _parse_args():
    parser = argparse.ArgumentParser(description='trainer.py')
    parser.add_argument('--model', type=str, default='BAD', help=
        'model to run (BAD, CLASSIFIER)')
    parser.add_argument('--train_path', type=str, default=
        'data/HD Training Data', help=
        'path to train set (you should not need to modify)')
    parser.add_argument('--dev_path', type=str, default=
        'data/HD Training Data', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--blind_test_path', type=str, default=
        'data/eng.testb.blind', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--test_output_path', type=str, default=
        'eng.testb.out', help='output path for test predictions')
    parser.add_argument('--no_run_on_test', dest='run_on_test', default=
        True, action='store_false', help='skip printing output on the test set'
        )
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = _parse_args()
    print(args)
    inputvid = 'test.mp4'
    basename = os.path.basename(inputvid)
    base, ext = os.path.splitext(basename)
    if DEMUX:
        ff = FFmpeg(inputs={inputvid: None}, outputs={(base + '-video.mp4'):
            ['-map', '0:0', '-c:a', 'copy', '-f', 'mp4', '-y'], (base +
            '-audio.mp4'): ['-map', '0:1', '-c:a', 'copy', '-f', 'mp4', '-y']})
        print(ff.cmd)
        ff.run()
    wavefile = base + '.wav'
    if EXTRACTWAV:
        ff = FFmpeg(inputs={inputvid: None}, outputs={wavefile: ['-vn',
            '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', '-y']})
        print(ff.cmd)
        ff.run()
    if PLAYWAVE:
        wu.playWav(wavefile)
    wavarr = wu.loadWav(wavefile)
    print(wavarr)
    fs = wavarr[0]
    signal = wavarr[1]
    Time = np.linspace(0, len(signal) / fs, num=len(signal))
    plt.figure(1)
    plt.title('Signal Wave...')
    plt.plot(Time, signal)
    plt.savefig('plot1.png')
    plt.show()

<mask token>
FFMPEGDIR = 'c:/Utils/ffmpeg/bin/'
DEMUX = False
EXTRACTWAV = False
PLAYWAVE = False


def _parse_args():
    parser = argparse.ArgumentParser(description='trainer.py')
    parser.add_argument('--model', type=str, default='BAD', help=
        'model to run (BAD, CLASSIFIER)')
    parser.add_argument('--train_path', type=str, default=
        'data/HD Training Data', help=
        'path to train set (you should not need to modify)')
    parser.add_argument('--dev_path', type=str, default=
        'data/HD Training Data', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--blind_test_path', type=str, default=
        'data/eng.testb.blind', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--test_output_path', type=str, default=
        'eng.testb.out', help='output path for test predictions')
    parser.add_argument('--no_run_on_test', dest='run_on_test', default=
        True, action='store_false', help='skip printing output on the test set'
        )
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = _parse_args()
    print(args)
    inputvid = 'test.mp4'
    basename = os.path.basename(inputvid)
    base, ext = os.path.splitext(basename)
    if DEMUX:
        ff = FFmpeg(inputs={inputvid: None}, outputs={(base + '-video.mp4'):
            ['-map', '0:0', '-c:a', 'copy', '-f', 'mp4', '-y'], (base +
            '-audio.mp4'): ['-map', '0:1', '-c:a', 'copy', '-f', 'mp4', '-y']})
        print(ff.cmd)
        ff.run()
    wavefile = base + '.wav'
    if EXTRACTWAV:
        ff = FFmpeg(inputs={inputvid: None}, outputs={wavefile: ['-vn',
            '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', '-y']})
        print(ff.cmd)
        ff.run()
    if PLAYWAVE:
        wu.playWav(wavefile)
    wavarr = wu.loadWav(wavefile)
    print(wavarr)
    fs = wavarr[0]
    signal = wavarr[1]
    Time = np.linspace(0, len(signal) / fs, num=len(signal))
    plt.figure(1)
    plt.title('Signal Wave...')
    plt.plot(Time, signal)
    plt.savefig('plot1.png')
    plt.show()

import argparse
from ffmpy import FFmpeg
import os, sys
import pyaudio
import wave
import numpy as np
import matplotlib.pyplot as plt
import wavutil as wu
FFMPEGDIR = 'c:/Utils/ffmpeg/bin/'
DEMUX = False
EXTRACTWAV = False
PLAYWAVE = False


def _parse_args():
    parser = argparse.ArgumentParser(description='trainer.py')
    parser.add_argument('--model', type=str, default='BAD', help=
        'model to run (BAD, CLASSIFIER)')
    parser.add_argument('--train_path', type=str, default=
        'data/HD Training Data', help=
        'path to train set (you should not need to modify)')
    parser.add_argument('--dev_path', type=str, default=
        'data/HD Training Data', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--blind_test_path', type=str, default=
        'data/eng.testb.blind', help=
        'path to dev set (you should not need to modify)')
    parser.add_argument('--test_output_path', type=str, default=
        'eng.testb.out', help='output path for test predictions')
    parser.add_argument('--no_run_on_test', dest='run_on_test', default=
        True, action='store_false', help='skip printing output on the test set'
        )
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = _parse_args()
    print(args)
    inputvid = 'test.mp4'
    basename = os.path.basename(inputvid)
    base, ext = os.path.splitext(basename)
    if DEMUX:
        ff = FFmpeg(inputs={inputvid: None}, outputs={(base + '-video.mp4'):
            ['-map', '0:0', '-c:a', 'copy', '-f', 'mp4', '-y'], (base +
            '-audio.mp4'): ['-map', '0:1', '-c:a', 'copy', '-f', 'mp4', '-y']})
        print(ff.cmd)
        ff.run()
    wavefile = base + '.wav'
    if EXTRACTWAV:
        ff = FFmpeg(inputs={inputvid: None}, outputs={wavefile: ['-vn',
            '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', '-y']})
        print(ff.cmd)
        ff.run()
    if PLAYWAVE:
        wu.playWav(wavefile)
    wavarr = wu.loadWav(wavefile)
    print(wavarr)
    fs = wavarr[0]
    signal = wavarr[1]
    Time = np.linspace(0, len(signal) / fs, num=len(signal))
    plt.figure(1)
    plt.title('Signal Wave...')
    plt.plot(Time, signal)
    plt.savefig('plot1.png')
    plt.show()

#import skvideo
import argparse
from ffmpy import FFmpeg
import os, sys
import pyaudio
import wave
import numpy as np
import matplotlib.pyplot as plt

import wavutil as wu

FFMPEGDIR='c:/Utils/ffmpeg/bin/'
#ffmpeg -i emotions.mp4 -vn -acodec pcm_s16le -ar 44100 -ac 2 emotions.wav

DEMUX=False			#split video and audio into 2 .mp4 files
EXTRACTWAV=False	#extract audio stream into a .wav file
PLAYWAVE=False

# Command-line arguments to the system -- you can extend these if you want, but you shouldn't need to modify any of them
def _parse_args():
    parser = argparse.ArgumentParser(description='trainer.py')
    parser.add_argument('--model', type=str, default='BAD', help='model to run (BAD, CLASSIFIER)')
    parser.add_argument('--train_path', type=str, default='data/HD Training Data', help='path to train set (you should not need to modify)')
    parser.add_argument('--dev_path', type=str, default='data/HD Training Data', help='path to dev set (you should not need to modify)')
    parser.add_argument('--blind_test_path', type=str, default='data/eng.testb.blind', help='path to dev set (you should not need to modify)')
    parser.add_argument('--test_output_path', type=str, default='eng.testb.out', help='output path for test predictions')
    parser.add_argument('--no_run_on_test', dest='run_on_test', default=True, action='store_false', help='skip printing output on the test set')
    args = parser.parse_args()
    return args


if __name__=='__main__':
	args = _parse_args()
	print(args)

	inputvid = 'test.mp4'
	basename = os.path.basename(inputvid)
	base, ext = os.path.splitext(basename)
	
	if DEMUX:
		ff = FFmpeg(
			inputs={inputvid: None},
			outputs={
				base + '-video.mp4': ['-map', '0:0', '-c:a', 'copy', '-f', 'mp4', '-y'],
				base + '-audio.mp4': ['-map', '0:1', '-c:a', 'copy', '-f', 'mp4', '-y']
			}
		)
		print(ff.cmd)
		ff.run()

	wavefile = base + '.wav'

	if EXTRACTWAV:
		ff = FFmpeg(
			inputs={inputvid: None},
			outputs={
				wavefile: ['-vn', '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', '-y']
			}
		)
		print(ff.cmd)
		ff.run()

	if PLAYWAVE:
		wu.playWav(wavefile)

	wavarr = wu.loadWav(wavefile)	#(framerate, np)
	print(wavarr)

	fs = wavarr[0]
	signal = wavarr[1]
	Time=np.linspace(0, len(signal)/fs, num=len(signal))

	plt.figure(1)
	plt.title('Signal Wave...')
	plt.plot(Time, signal)
	plt.savefig("plot1.png")
	plt.show()
