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