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test_reduction.py
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test_reduction.py
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from scipy.io import wavfile
import noisereduce as nr
from noisereduce.generate_noise import band_limited_noise
def test_reduce_generated_noise_stationary_with_noise_clip():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
# add noise
noise_len = 2 # seconds
noise = band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
noise_clip = noise[: rate * noise_len]
audio_clip_band_limited = data + noise
return nr.reduce_noise(
y=audio_clip_band_limited, sr=rate, y_noise=noise_clip, stationary=True
)
def test_reduce_generated_noise_stationary_without_noise_clip():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
# add noise
noise = band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
audio_clip_band_limited = data + noise
return nr.reduce_noise(
y=audio_clip_band_limited, sr=rate, stationary=True
)
def test_reduce_generated_noise_nonstationary():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
# add noise
noise = band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
audio_clip_band_limited = data + noise
return nr.reduce_noise(
y=audio_clip_band_limited, sr=rate, stationary=False
)
def test_reduce_generated_noise_batches():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
# add noise
noise = band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
audio_clip_band_limited = data + noise
return nr.reduce_noise(
y=audio_clip_band_limited, sr=rate, stationary=False, chunk_size=30000
)
def test_reduce_torch_cpu_stationary():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
# add noise
noise = band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
audio_clip_band_limited = data + noise
return nr.reduce_noise(
y=audio_clip_band_limited, sr=rate, stationary=True, chunk_size=30000, use_torch=True, device='cpu'
)
# def test_reduce_torch_cpu_stationary_cuda():
# # load data
# wav_loc = "assets/fish.wav"
# rate, data = wavfile.read(wav_loc)
#
# # add noise
# noise = band_limited_noise(
# min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
# audio_clip_band_limited = data + noise
# return nr.reduce_noise(
# y=audio_clip_band_limited, sr=rate, stationary=True, chunk_size=30000, use_torch=True, device='cuda'
# )
def test_reduce_torch_cpu_non_stationary():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
# add noise
noise = band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
audio_clip_band_limited = data + noise
return nr.reduce_noise(
y=audio_clip_band_limited, sr=rate, stationary=False, chunk_size=30000, use_torch=True, device='cpu'
)
# def test_reduce_torch_cpu_non_stationary_cuda():
# # load data
# wav_loc = "assets/fish.wav"
# rate, data = wavfile.read(wav_loc)
#
# # add noise
# noise = band_limited_noise(
# min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) * 10
# audio_clip_band_limited = data + noise
# return nr.reduce_noise(
# y=audio_clip_band_limited, sr=rate, stationary=False, use_torch=True, device='cuda'
# )