Azalea (on HyDEV-Daisy)
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import librosa
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import librosa.display
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import matplotlib.pyplot as plt
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import numpy as np
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import parselmouth
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import sgs
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if __name__ == '__main__':
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librosa.filters.mel(sr=16000, n_fft=1024, htk=True)
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f = 'Z:/EECS 6414/voice_cnn/test.wav'
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y, sr = librosa.load(f, sr=16000)
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# Plot waveform
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# plt.plot(y)
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# plt.title('Signal')
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# plt.xlabel('Time (samples)')
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# plt.ylabel('Amplitude')
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# plt.show()
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# plt.clf()
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# Plot frequency domain graph at a single time
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n_fft = 2048
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ft = np.abs(librosa.stft(y[:n_fft], hop_length=n_fft + 1))
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# plt.plot(ft)
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# plt.title('Spectrum')
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# plt.xlabel('Frequency Bin')
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# plt.ylabel('Amplitude')
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# plt.show()
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# plt.clf()
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# Plot spectrogram
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spec = np.abs(librosa.stft(y, n_fft=1024, hop_length=512))
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# spec = librosa.amplitude_to_db(spec, ref=np.max)
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# librosa.display.specshow(spec, sr=sr, x_axis='time', y_axis='log')
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# plt.colorbar(format='%+2.0f dB')
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# plt.title('Spectrogram')
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# plt.show()
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# plt.clf()
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# Mel transform
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mel_spect = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=512, htk=True)
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mel_spect = librosa.power_to_db(mel_spect, ref=np.max)
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print(len(mel_spect))
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librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time', n_fft=1024, hop_length=512)
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result, freq_array = sgs.api.calculate_feature_classification(parselmouth.Sound(f))
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pitch_array = freq_array[:, 0]
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# x_len = len(pitch_array) / len(mel_spect)
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# x = np.arange(len(mel_spect))
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# y = []
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# for x in range(len(mel_spect) // 2):
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# y.append(float(np.mean(pitch_array[int(x_len * x):int(x_len * (x + 1))])))
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# print(len(y))
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x = np.linspace(0, 4.1)
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print(x)
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x_len = len(pitch_array) / len(x)
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y = []
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for a in range(len(x)):
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y.append(np.mean(pitch_array[int(x_len * a):int(x_len * (a + 1))]))
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plt.plot(x, y, color='#7bff4f')
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plt.plot(x, [100] * len(x), color='#7bff4f')
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plt.yticks([0,100,200,300,400,500,600,700,800,900,1000,1200,1400,1600])
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plt.title('Mel Spectrogram')
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plt.colorbar(format='%+2.0f dB')
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plt.show()
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plt.clf() |