215 lines
7.9 KiB
Python
215 lines
7.9 KiB
Python
import torch
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import torchaudio
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import torch.nn.functional as F
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from ..infer_pack.commons import median_pool1d
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def compute_temporal_envelope(log_magnitude: torch.Tensor, kernel_size=31):
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# Apply median pooling to the generated audio
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pooled_stft = median_pool1d(log_magnitude, kernel_size, stride=kernel_size//2+1)
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# Compute differences on the median-pooled audio
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diff = F.normalize(torch.diff(pooled_stft, dim=-1, n=1),dim=-1)
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# Return the mean of the differences as the loss
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return diff.sum(dim=-2).nan_to_num(0)
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def compute_cepstrals(log_magnitude: torch.Tensor, kernel_size=31):
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pooled_magnitude = median_pool1d(log_magnitude, kernel_size, stride=kernel_size//2+1)
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# Compute the cepstrum
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cepstral_coefficients = torch.fft.ifft(pooled_magnitude, dim=-2).real
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return cepstral_coefficients.sum(dim=-2).nan_to_num(0)
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def compute_sts_loss(gen_stft: torch.Tensor, org_stft: torch.Tensor, kernel_size=31):
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# B?N?T
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org_mag = torch.log1p(org_stft.abs())
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gen_mag = torch.log1p(gen_stft.abs())
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gen_temp = compute_temporal_envelope(gen_mag, kernel_size=kernel_size)
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org_temp = compute_temporal_envelope(org_mag, kernel_size=kernel_size)
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temporal_loss = 1-F.cosine_similarity(gen_temp, org_temp, dim=-1).mean()
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gen_spec = compute_cepstrals(org_mag,kernel_size=kernel_size)
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org_spec = compute_cepstrals(gen_mag,kernel_size=kernel_size)
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spectral_loss = F.smooth_l1_loss(gen_spec, org_spec)
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org_phase = org_stft.angle()
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gen_phase = gen_stft.angle()
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# temporal invariant phase
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org_phase = median_pool1d(org_stft.angle(),3,stride=3).mean(-1)
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gen_phase = median_pool1d(gen_stft.angle(),3,stride=3).mean(-1)
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phase_loss = F.smooth_l1_loss(gen_phase,org_phase)+F.smooth_l1_loss(1-torch.cos(gen_phase-org_phase),torch.zeros_like(gen_phase))
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return temporal_loss, spectral_loss, phase_loss.nan_to_num(0)
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def compute_harmonics(stft_matrix: torch.Tensor, kernel_size=31):
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# Calculate log-magnitude spectrograms
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mag = torch.log1p(stft_matrix.abs())
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# Apply median filtering to separate harmonics and percussives
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harmonic = median_pool1d(mag, kernel_size, stride=kernel_size//2+1)
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percussive = median_pool1d(mag.transpose(-2,-1), kernel_size, stride=kernel_size//2+1).transpose(-2,-1)
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return harmonic.nan_to_num(0), percussive.nan_to_num(0)
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def combined_aux_loss(
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original_audio: torch.Tensor, generated_audio: torch.Tensor, sample_rate: int,
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c_mfcc=1., c_lfcc=1., c_hd=1., c_sts=1.,
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n_fft=1024, hop_length=320, win_length=1024,
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n_mfcc=13, n_lfcc=13, n_filter=80,
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f_min=0., f_max=None):
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kernel_size = n_fft//hop_length+1
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if kernel_size % 2 == 0: kernel_size+=1
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# Compute STFT once
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if c_hd+c_sts>0:
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hann_window = torch.hann_window(win_length).to(
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dtype=original_audio.dtype, device=original_audio.device
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)
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generated_stft = torch.stft(
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generated_audio.view(-1,generated_audio.size(-1)),
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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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window=hann_window,
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return_complex=True,
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center=False)
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original_stft = torch.stft(
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original_audio.view(-1,original_audio.size(-1)),
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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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window=hann_window,
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return_complex=True,
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center=False)
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# Harmonic Loss
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if c_hd>0:
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original_harmonics, original_percussives = compute_harmonics(original_stft,kernel_size)
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generated_harmonics, generated_percussives = compute_harmonics(generated_stft,kernel_size)
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# Define loss terms
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harmonic_loss = F.smooth_l1_loss(generated_harmonics, original_harmonics)
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harmonic_loss += F.smooth_l1_loss(generated_percussives, original_percussives)
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harmonic_loss *= c_hd
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harmonic_loss
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else: harmonic_loss = 0
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# Spectral Temporal Smoothness Loss
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if c_sts>0:
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temporal_loss, spectral_loss, phase_loss = compute_sts_loss(generated_stft,original_stft,kernel_size)
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temporal_loss *= c_sts
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spectral_loss *= c_sts
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phase_loss *= c_sts
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else: spectral_loss = temporal_loss = phase_loss = 0
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# MFCC Loss
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if c_mfcc>0:
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mfcc_transform = torchaudio.transforms.MFCC(
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sample_rate=sample_rate, n_mfcc=n_mfcc,
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melkwargs=dict(n_fft=n_fft, hop_length=hop_length, win_length=win_length, n_mels=n_filter, f_min=f_min, f_max=f_max)
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).to(original_audio.device)
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original_mfcc = mfcc_transform(original_audio)
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generated_mfcc = mfcc_transform(generated_audio)
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mfcc_loss = F.smooth_l1_loss(generated_mfcc, original_mfcc) * c_mfcc
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else: mfcc_loss = 0
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# LFCC Loss
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if c_lfcc>0:
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lfcc_transform = torchaudio.transforms.LFCC(
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sample_rate=sample_rate, n_lfcc=n_lfcc, n_filter=n_filter, f_min=f_min, f_max=f_max,
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speckwargs=dict(n_fft=n_fft, hop_length=hop_length, win_length=win_length)
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).to(original_audio.device)
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original_lfcc = lfcc_transform(original_audio)
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generated_lfcc = lfcc_transform(generated_audio)
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lfcc_loss = F.smooth_l1_loss(generated_lfcc, original_lfcc) * c_lfcc
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else: lfcc_loss = 0
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return harmonic_loss, temporal_loss, spectral_loss, mfcc_loss, lfcc_loss, phase_loss
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def gradient_norm_loss(original_audio: torch.Tensor, generated_audio: torch.Tensor, net_d):
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loss=0
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# Compute the gradient penalty
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# Randomly interpolate between real and generated data
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size = [1]*original_audio.ndim
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size[0] = original_audio.size(0)
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alpha = torch.rand(*size, device=original_audio.device)
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interpolated = alpha * original_audio + (1 - alpha) * generated_audio
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interpolated.requires_grad_(True)
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# Get the discriminator output for the interpolated data
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_, disc_interpolated_output, _, _ = net_d(original_audio, interpolated)
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# Compute gradients of discriminator output w.r.t. interpolated data
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for output in disc_interpolated_output:
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gradient = torch.autograd.grad(
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outputs=output,
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inputs=interpolated,
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grad_outputs=torch.ones(output.size(), device=original_audio.device),
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create_graph=True,
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only_inputs=True
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)[0]
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gradient = gradient.view(gradient.size(0), -1)
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loss += torch.log(gradient.norm(2,dim=-1)).abs().mean() # force gradnorm=1
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return loss/len(disc_interpolated_output)
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def feature_loss(fmap_r, fmap_g):
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loss = 0
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for dr, dg in zip(fmap_r, fmap_g):
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for rl, gl in zip(dr, dg):
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rl = rl.float()
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gl = gl.float()
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loss += torch.mean(torch.abs(rl - gl))
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return loss
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def discriminator_loss(disc_real_outputs, disc_generated_outputs):
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loss = 0
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r_losses = []
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g_losses = []
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for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
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dr = dr.float()
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dg = dg.float()
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r_loss = torch.mean((1 - dr) ** 2)
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g_loss = torch.mean(dg**2)
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loss += r_loss + g_loss
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r_losses.append(r_loss.item())
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g_losses.append(g_loss.item())
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# loss /= len(disc_generated_outputs) #average
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return loss, r_losses, g_losses
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def generator_loss(disc_outputs):
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loss = 0
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gen_losses = []
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for dg in disc_outputs:
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dg = dg.float()
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l = torch.mean((1 - dg) ** 2)
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gen_losses.append(l)
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loss += l
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# loss /= len(disc_outputs) #average
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return loss, gen_losses
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def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
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"""
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z_p, logs_q: [b, h, t_t]
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m_p, logs_p: [b, h, t_t]
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"""
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z_p = z_p.float()
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logs_q = logs_q.float()
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m_p = m_p.float()
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logs_p = logs_p.float()
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z_mask = z_mask.float()
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kl = logs_p - logs_q - 0.5
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kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
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kl = torch.sum(kl * z_mask)
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l = kl / torch.sum(z_mask)
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return l
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