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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from scipy.io.wavfile import write | |
| audios = [ | |
| ["Book Example", "speaker"], | |
| ["Swoosh", "swoosh"], | |
| ["Knocking", "knocking"], | |
| ["Forest", "forest"], | |
| ["Evil Laugh", "evil-laugh"], | |
| ["Morning", "morning"], | |
| ["Cinematic", "cinematic"], | |
| ] | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Waveforms"): | |
| gr.Markdown("""## Waveforms | |
| In this section, we'll look into the waveforms of multiple audios. | |
| """) | |
| for title, path in audios: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown(f"### {title}") | |
| with gr.Column(scale=5): | |
| waveform = gr.Image(value=f"{path}/waveform.png") | |
| with gr.Column(scale=5): | |
| video = gr.Video(value=f"{path}/waveform_video.mp4") | |
| with gr.Tab("Understanding Frequencies"): | |
| gr.Markdown("""## Understanding Frequencies | |
| """) | |
| freq = gr.Slider(0, 300, step=20, value=40, label="Frequency") | |
| freq2 = gr.Slider(0, 30, step=5, value=0, label="Second Frequency") | |
| amplitude = gr.Slider(0.05, 1, step=0.05, value=1, label="Amplitude") | |
| audio = gr.Audio() | |
| with gr.Row(): | |
| plots = gr.Plot(label="Results") | |
| with gr.Row(): | |
| button = gr.Button(label="Create") | |
| # https://github.com/gradio-app/gradio/issues/5469 | |
| def plot_sine(freq, freq2, a): | |
| sr = 1000 # samples per second | |
| ts = 1.0/sr # sampling interval | |
| t = np.arange(0, 1, ts) # time vector | |
| data = a * np.sin(2 * np.pi * freq * t) + a * np.sin(2 * np.pi * freq2 * t) | |
| write("test.wav", sr, data) | |
| fig, axes = plt.subplots(nrows=2, ncols=1, sharex=False) | |
| ax_waveform = axes[0] | |
| ax_spectrum = axes[1] | |
| ax_waveform.plot(t, data) | |
| ax_waveform.set_title(f'Sine wave with frequency {freq} and amplitude {a}') | |
| ax_waveform.set_xlabel('Time )s)') | |
| ax_waveform.set_ylabel('Amplitude') | |
| ax_waveform.set_title("Time domain of the signal") | |
| X = np.fft.fft(data) | |
| N = len(X) | |
| n = np.arange(N) | |
| T = N/sr | |
| freq = n/T | |
| ax_spectrum.set_xlim((0,300)) | |
| ax_spectrum.stem(freq, np.abs(X), 'r', \ | |
| markerfmt=" ", basefmt="-b") | |
| ax_spectrum.set_xlabel("Frequency (Hz)") | |
| ax_spectrum.set_title("Frequency domain of the signal") | |
| fig.tight_layout() | |
| fig.savefig('foo.png') | |
| return "test.wav", fig | |
| button.click(plot_sine, inputs=[freq, freq2, amplitude], outputs=[audio, plots]) | |
| with gr.Tab("Spectrograms and Mel Spectrograms"): | |
| gr.Markdown("""## Waveforms | |
| In this section, we'll look into the waveforms of multiple audios. | |
| """) | |
| for title, path in audios: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown(f"### {title}") | |
| with gr.Column(scale=10): | |
| gr.Image(value=f"{path}/waveform.png") | |
| with gr.Column(scale=10): | |
| gr.Image(value=f"{path}/fft.png") | |
| with gr.Column(scale=10): | |
| video = gr.Video(value=f"{path}/waveform_video.mp4") | |
| with gr.Row(): | |
| with gr.Column(scale=5): | |
| gr.Image(value=f"{path}/spectrogram.png") | |
| with gr.Column(scale=5): | |
| gr.Image(value=f"{path}/mel_spectrogram.png") | |
| if __name__ == '__main__': | |
| demo.launch(debug=True) |