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audioTests.py
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audioTests.py
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import matplotlib.pyplot as plt
import torch
import torchaudio
import time
def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
#waveform = waveform.numpy()
num_channels, num_frames = waveform.shape
time_axis = torch.arange(0, num_frames) / sample_rate
figure, axes = plt.subplots(num_channels, 1)
if num_channels == 1:
axes = [axes]
for c in range(num_channels):
s = time.time()
axes[c].specgram(waveform[c], Fs=sample_rate)
print(time.time()-s)
if num_channels > 1:
axes[c].set_ylabel(f'Channel {c+1}')
if xlim:
axes[c].set_xlim(xlim)
figure.suptitle(title)
plt.show(block=False)
samples,sample_rate = torchaudio.load("./noise_files/TrafficSamples/test.wav")
transform = torchaudio.transforms.Resample(orig_freq=sample_rate,new_freq=16000)
samples_2 = transform(samples).numpy()[:16000]
print(samples.shape)
plot_specgram(samples,16000,title="ok")
input("waiting")