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import gradio as gr |
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""" |
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Audio processing tools to convert between spectrogram images and waveforms. |
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""" |
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import io |
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import typing as T |
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import numpy as np |
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from PIL import Image |
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import pydub |
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from scipy.io import wavfile |
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import torch |
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import torchaudio |
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def wav_bytes_from_spectrogram_image(image: Image.Image) -> T.Tuple[io.BytesIO, float]: |
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""" |
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Reconstruct a WAV audio clip from a spectrogram image. Also returns the duration in seconds. |
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""" |
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max_volume = 50 |
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power_for_image = 0.25 |
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Sxx = spectrogram_from_image(image, max_volume=max_volume, power_for_image=power_for_image) |
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sample_rate = 44100 |
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clip_duration_ms = 5000 |
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bins_per_image = 512 |
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n_mels = 512 |
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window_duration_ms = 100 |
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padded_duration_ms = 400 |
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step_size_ms = 10 |
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num_samples = int(image.width / float(bins_per_image) * clip_duration_ms) * sample_rate |
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n_fft = int(padded_duration_ms / 1000.0 * sample_rate) |
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hop_length = int(step_size_ms / 1000.0 * sample_rate) |
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win_length = int(window_duration_ms / 1000.0 * sample_rate) |
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samples = waveform_from_spectrogram( |
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Sxx=Sxx, |
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n_fft=n_fft, |
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hop_length=hop_length, |
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win_length=win_length, |
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num_samples=num_samples, |
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sample_rate=sample_rate, |
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mel_scale=True, |
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n_mels=n_mels, |
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max_mel_iters=200, |
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num_griffin_lim_iters=32, |
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) |
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wav_bytes = io.BytesIO() |
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wavfile.write(wav_bytes, sample_rate, samples.astype(np.int16)) |
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wav_bytes.seek(0) |
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duration_s = float(len(samples)) / sample_rate |
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return wav_bytes |
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gr.Interface(fn=wav_bytes_from_spectrogram_image, inputs=[gr.Image()], outputs=[gr.Audio()]).launch() |