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import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
import torch
import librosa
from torch.nn import functional as F
def save_curve_plot(pred, midi, gt, savepath):
plt.style.use('default')
fig, ax = plt.subplots(figsize=(12, 3))
pred[pred == 0] = np.nan
midi[midi == 0] = np.nan
gt[gt == 0] = np.nan
# im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none')
ax.plot(range(len(pred)), pred, color='tab:green', label='pred')
ax.plot(range(len(midi)), midi, color='tab:blue', label='midi')
ax.plot(range(len(gt)), gt, color='grey', label='gt')
# plt.colorbar(im, ax=ax)
plt.tight_layout()
fig.canvas.draw()
plt.legend()
plt.savefig(savepath)
plt.close()
#
#
# def save_audio(file_path, sampling_rate, audio):
# audio = np.clip(audio.detach().cpu().squeeze().numpy(), -0.999, 0.999)
# wavfile.write(file_path, sampling_rate, (audio * 32767).astype("int16"))
def minmax_norm_diff(tensor: torch.Tensor, vmax: float = librosa.note_to_hz('C6'),
vmin: float = 0) -> torch.Tensor:
tensor = torch.clip(tensor, vmin, vmax)
tensor = 2 * (tensor - vmin) / (vmax - vmin) - 1
return tensor
def reverse_minmax_norm_diff(tensor: torch.Tensor, vmax: float = librosa.note_to_hz('C6'),
vmin: float = 0) -> torch.Tensor:
tensor = torch.clip(tensor, -1.0, 1.0)
tensor = (tensor + 1) / 2
tensor = tensor * (vmax - vmin) + vmin
return tensor |