def test_reduce_cafe_noise():
# load data
wav_loc = "assets/coffe-1_2020-03-08_170854361.wav"
rate, data = wavfile.read(wav_loc)
data = int16_to_float32(data)
noise_loc = "assets/coffe-1x_2020-03-08_170932616.wav"
noise_rate, noise_data = wavfile.read(noise_loc)
noise_data = int16_to_float32(noise_data)
# add noise
snr = 2 # signal to noise ratio
noise_clip = noise_data / snr
audio_clip_cafe = data
# reduce noise
reduced_noise = nr.reduce_noise(audio_clip=audio_clip_cafe,
noise_clip=noise_clip,
verbose=True)
return float32_to_int16(reduced_noise)
def test_reduce_cafe_noise():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
data = int16_to_float32(data)
noise_loc = "assets/cafe_short.wav"
noise_rate, noise_data = wavfile.read(noise_loc)
noise_data = int16_to_float32(noise_data)
# add noise
snr = 2 # signal to noise ratio
noise_clip = noise_data / snr
audio_clip_cafe = data + noise_clip
# reduce noise
reduced_noise = nrv1.reduce_noise(audio_clip=audio_clip_cafe,
noise_clip=noise_clip,
verbose=True)
return float32_to_int16(reduced_noise)
for i in data:
if i > 0:
break
else:
lag = lag + 1
cnt = 0
for i in data:
cnt = cnt + 1
if abs(i) > 0:
if cnt < 100000:
noise_list.append(i)
else:
break
#picking up noise clip data from lag+3000 to lag+15000 and creating noise profile from noise clip
noise_clip = data[lag + 3000:lag + 15000]
video_len = len(data) / rate
reduced = float32_to_int16(
nr.reduce_noise(audio_clip=data, noise_clip=noise_clip, prop_decrease=0.8))
from scipy.io.wavfile import write
write("v1_ss8.wav", rate, reduced)
cmd = 'ffmpeg -y -i v1_ss8.wav -r 30 -i v1_ss_V_8.h264 -filter:a aresample=async=1 -c:a flac -c:v copy v1_ss_V_8.mkv'
subprocess.call(cmd, shell=True) # "Muxing Done
print('Done')