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import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoModel, WhisperConfig, WhisperPreTrainedModel
import whisper
from transformers.models.whisper.modeling_whisper import WhisperEncoder
class SpectralConvergengeLoss(torch.nn.Module):
"""Spectral convergence loss module."""
def __init__(self):
"""Initilize spectral convergence loss module."""
super(SpectralConvergengeLoss, self).__init__()
def forward(self, x_mag, y_mag):
"""Calculate forward propagation.
Args:
x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
Returns:
Tensor: Spectral convergence loss value.
"""
return torch.norm(y_mag - x_mag, p=1) / torch.norm(y_mag, p=1)
class STFTLoss(torch.nn.Module):
"""STFT loss module."""
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window=torch.hann_window):
"""Initialize STFT loss module."""
super(STFTLoss, self).__init__()
self.fft_size = fft_size
self.shift_size = shift_size
self.win_length = win_length
self.to_mel = torchaudio.transforms.MelSpectrogram(sample_rate=24000, n_fft=fft_size, win_length=win_length, hop_length=shift_size, window_fn=window)
self.spectral_convergenge_loss = SpectralConvergengeLoss()
def forward(self, x, y):
"""Calculate forward propagation.
Args:
x (Tensor): Predicted signal (B, T).
y (Tensor): Groundtruth signal (B, T).
Returns:
Tensor: Spectral convergence loss value.
Tensor: Log STFT magnitude loss value.
"""
x_mag = self.to_mel(x)
mean, std = -4, 4
x_mag = (torch.log(1e-5 + x_mag) - mean) / std
y_mag = self.to_mel(y)
mean, std = -4, 4
y_mag = (torch.log(1e-5 + y_mag) - mean) / std
sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
return sc_loss
class MultiResolutionSTFTLoss(torch.nn.Module):
"""Multi resolution STFT loss module."""
def __init__(self,
fft_sizes=[1024, 2048, 512],
hop_sizes=[120, 240, 50],
win_lengths=[600, 1200, 240],
window=torch.hann_window):
"""Initialize Multi resolution STFT loss module.
Args:
fft_sizes (list): List of FFT sizes.
hop_sizes (list): List of hop sizes.
win_lengths (list): List of window lengths.
window (str): Window function type.
"""
super(MultiResolutionSTFTLoss, self).__init__()
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
self.stft_losses = torch.nn.ModuleList()
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
self.stft_losses += [STFTLoss(fs, ss, wl, window)]
def forward(self, x, y):
"""Calculate forward propagation.
Args:
x (Tensor): Predicted signal (B, T).
y (Tensor): Groundtruth signal (B, T).
Returns:
Tensor: Multi resolution spectral convergence loss value.
Tensor: Multi resolution log STFT magnitude loss value.
"""
sc_loss = 0.0
for f in self.stft_losses:
sc_l = f(x, y)
sc_loss += sc_l
sc_loss /= len(self.stft_losses)
return sc_loss
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
loss += torch.mean(torch.abs(rl - gl))
return loss*2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
r_loss = torch.mean((1-dr)**2)
g_loss = torch.mean(dg**2)
loss += (r_loss + g_loss)
r_losses.append(r_loss.item())
g_losses.append(g_loss.item())
return loss, r_losses, g_losses
def generator_loss(disc_outputs):
loss = 0
gen_losses = []
for dg in disc_outputs:
l = torch.mean((1-dg)**2)
gen_losses.append(l)
loss += l
return loss, gen_losses
""" https://dl.acm.org/doi/abs/10.1145/3573834.3574506 """
def discriminator_TPRLS_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
tau = 0.04
m_DG = torch.median((dr-dg))
L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG])
loss += tau - F.relu(tau - L_rel)
return loss
def generator_TPRLS_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
for dg, dr in zip(disc_real_outputs, disc_generated_outputs):
tau = 0.04
m_DG = torch.median((dr-dg))
L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG])
loss += tau - F.relu(tau - L_rel)
return loss
class GeneratorLoss(torch.nn.Module):
def __init__(self, mpd, msd):
super(GeneratorLoss, self).__init__()
self.mpd = mpd
self.msd = msd
def forward(self, y, y_hat):
y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = self.mpd(y, y_hat)
y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = self.msd(y, y_hat)
loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
loss_rel = generator_TPRLS_loss(y_df_hat_r, y_df_hat_g) + generator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)
loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_rel
return loss_gen_all.mean()
class DiscriminatorLoss(torch.nn.Module):
def __init__(self, mpd, msd):
super(DiscriminatorLoss, self).__init__()
self.mpd = mpd
self.msd = msd
def forward(self, y, y_hat):
# MPD
y_df_hat_r, y_df_hat_g, _, _ = self.mpd(y, y_hat)
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
# MSD
y_ds_hat_r, y_ds_hat_g, _, _ = self.msd(y, y_hat)
loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
loss_rel = discriminator_TPRLS_loss(y_df_hat_r, y_df_hat_g) + discriminator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)
d_loss = loss_disc_s + loss_disc_f + loss_rel
return d_loss.mean()
# #####################
# MIXED PRECISION
class WhisperEncoderOnly(WhisperPreTrainedModel):
def __init__(self, config: WhisperConfig):
super().__init__(config)
self.encoder = WhisperEncoder(config)
def forward(self, input_features, attention_mask=None):
return self.encoder(input_features, attention_mask)
class WavLMLoss(torch.nn.Module):
def __init__(self, model, wd, model_sr, slm_sr=16000):
super(WavLMLoss, self).__init__()
config = WhisperConfig.from_pretrained("Respair/Whisper_Large_v2_Encoder_Block")
# this will load the full model and keep only the encoder
full_model = WhisperEncoderOnly.from_pretrained("openai/whisper-large-v2", config=config, device_map='auto',torch_dtype=torch.bfloat16)
model = WhisperEncoderOnly(config)
model.encoder.load_state_dict(full_model.encoder.state_dict())
del full_model
self.wavlm = model.to(torch.bfloat16)
self.wd = wd
self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)
def forward(self, wav, y_rec, generator=False, discriminator=False, discriminator_forward=False):
if generator:
y_rec = y_rec.squeeze(1)
y_rec = whisper.pad_or_trim(y_rec)
y_rec = whisper.log_mel_spectrogram(y_rec)
with torch.no_grad():
y_rec_embeddings = self.wavlm.encoder(y_rec.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_df_hat_g = self.wd(y_rec_embeddings.to(torch.float32))
loss_gen = torch.mean((1-y_df_hat_g)**2)
return loss_gen.to(torch.float32)
elif discriminator:
wav = wav.squeeze(1)
y_rec = y_rec.squeeze(1)
wav = whisper.pad_or_trim(wav)
wav = whisper.log_mel_spectrogram(wav)
y_rec = whisper.pad_or_trim(y_rec)
y_rec = whisper.log_mel_spectrogram(y_rec)
with torch.no_grad():
wav_embeddings = self.wavlm.encoder(wav.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_rec_embeddings = self.wavlm.encoder(y_rec.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_d_rs = self.wd(y_embeddings.to(torch.float32))
y_d_gs = self.wd(y_rec_embeddings.to(torch.float32))
y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs
r_loss = torch.mean((1-y_df_hat_r)**2)
g_loss = torch.mean((y_df_hat_g)**2)
loss_disc_f = r_loss + g_loss
return loss_disc_f.mean().to(torch.float32)
elif discriminator_forward:
# Squeeze the channel dimension if it's unnecessary
wav = wav.squeeze(1) # Adjust this line if the channel dimension is not at dim=1
with torch.no_grad():
wav_16 = self.resample(wav)
wav_16 = whisper.pad_or_trim(wav_16)
wav_16 = whisper.log_mel_spectrogram(wav_16)
wav_embeddings = self.wavlm.encoder(wav_16.to(torch.bfloat16) , output_hidden_states=True).hidden_states
y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_d_rs = self.wd(y_embeddings.to(torch.float32))
return y_d_rs
else:
wav = wav.squeeze(1)
y_rec = y_rec.squeeze(1)
wav = whisper.pad_or_trim(wav)
wav = whisper.log_mel_spectrogram(wav)
y_rec = whisper.pad_or_trim(y_rec)
y_rec = whisper.log_mel_spectrogram(y_rec)
with torch.no_grad():
wav_embeddings = self.wavlm.encoder(wav.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_rec_embeddings = self.wavlm.encoder(y_rec.to(torch.bfloat16), output_hidden_states=True).hidden_states
floss = 0
for er, eg in zip([e.to(torch.float32) for e in wav_embeddings], [e.to(torch.float32) for e in y_rec_embeddings]):
floss += torch.mean(torch.abs(er - eg))
return floss.mean()
def generator(self, y_rec):
y_rec = y_rec.squeeze(1)
y_rec = whisper.pad_or_trim(y_rec)
y_rec = whisper.log_mel_spectrogram(y_rec)
with torch.no_grad():
y_rec_embeddings = self.wavlm.encoder(y_rec.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_df_hat_g = self.wd(y_rec_embeddings.to(torch.float32))
loss_gen = torch.mean((1-y_df_hat_g)**2)
return loss_gen.to(torch.float32)
def discriminator(self, wav, y_rec):
wav = wav.squeeze(1)
y_rec = y_rec.squeeze(1)
wav = whisper.pad_or_trim(wav)
wav = whisper.log_mel_spectrogram(wav)
y_rec = whisper.pad_or_trim(y_rec)
y_rec = whisper.log_mel_spectrogram(y_rec)
with torch.no_grad():
wav_embeddings = self.wavlm.encoder(wav.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_rec_embeddings = self.wavlm.encoder(y_rec.to(torch.bfloat16), output_hidden_states=True).hidden_states
y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_d_rs = self.wd(y_embeddings.to(torch.float32))
y_d_gs = self.wd(y_rec_embeddings.to(torch.float32))
y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs
r_loss = torch.mean((1-y_df_hat_r)**2)
g_loss = torch.mean((y_df_hat_g)**2)
loss_disc_f = r_loss + g_loss
return loss_disc_f.mean().to(torch.float32)
def discriminator_forward(self, wav):
# Squeeze the channel dimension if it's unnecessary
wav = wav.squeeze(1) # Adjust this line if the channel dimension is not at dim=1
with torch.no_grad():
wav_16 = self.resample(wav)
wav_16 = whisper.pad_or_trim(wav_16)
wav_16 = whisper.log_mel_spectrogram(wav_16)
wav_embeddings = self.wavlm.encoder(wav_16.to(torch.bfloat16) , output_hidden_states=True).hidden_states
y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
y_d_rs = self.wd(y_embeddings.to(torch.float32))
return y_d_rs
################### Whisper - No Mixed Precision!
# class WhisperEncoderOnly(WhisperPreTrainedModel):
# def __init__(self, config: WhisperConfig):
# super().__init__(config)
# self.encoder = WhisperEncoder(config)
# def forward(self, input_features, attention_mask=None):
# return self.encoder(input_features, attention_mask)
# class WavLMLoss(torch.nn.Module):
# def __init__(self, model, wd, model_sr, slm_sr=16000):
# super(WavLMLoss, self).__init__()
# config = WhisperConfig.from_pretrained("Respair/Whisper_Large_v2_Encoder_Block")
# # this will load the full model and keep only the encoder
# full_model = WhisperEncoderOnly.from_pretrained("openai/whisper-large-v2", config=config, device_map='auto')
# model = WhisperEncoderOnly(config)
# model.encoder.load_state_dict(full_model.encoder.state_dict())
# del full_model
# self.wavlm = model
# self.wd = wd
# self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)
# def forward(self, wav, y_rec):
# wav = wav.squeeze(1)
# y_rec = y_rec.squeeze(1)
# wav = whisper.pad_or_trim(wav)
# wav = whisper.log_mel_spectrogram(wav)
# y_rec = whisper.pad_or_trim(y_rec)
# y_rec = whisper.log_mel_spectrogram(y_rec)
# with torch.no_grad():
# wav_embeddings = self.wavlm.encoder(wav, output_hidden_states=True).hidden_states
# y_rec_embeddings = self.wavlm.encoder(y_rec, output_hidden_states=True).hidden_states
# floss = 0
# for er, eg in zip(wav_embeddings, y_rec_embeddings):
# floss += torch.mean(torch.abs(er - eg))
# return floss.mean()
# def generator(self, y_rec):
# y_rec = y_rec.squeeze(1)
# y_rec = whisper.pad_or_trim(y_rec)
# y_rec = whisper.log_mel_spectrogram(y_rec)
# with torch.no_grad():
# y_rec_embeddings = self.wavlm.encoder(y_rec, output_hidden_states=True).hidden_states
# y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_df_hat_g = self.wd(y_rec_embeddings)
# loss_gen = torch.mean((1-y_df_hat_g)**2)
# return loss_gen
# def discriminator(self, wav, y_rec):
# wav = wav.squeeze(1)
# y_rec = y_rec.squeeze(1)
# wav = whisper.pad_or_trim(wav)
# wav = whisper.log_mel_spectrogram(wav)
# y_rec = whisper.pad_or_trim(y_rec)
# y_rec = whisper.log_mel_spectrogram(y_rec)
# with torch.no_grad():
# wav_embeddings = self.wavlm.encoder(wav, output_hidden_states=True).hidden_states
# y_rec_embeddings = self.wavlm.encoder(y_rec, output_hidden_states=True).hidden_states
# y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_d_rs = self.wd(y_embeddings)
# y_d_gs = self.wd(y_rec_embeddings)
# y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs
# r_loss = torch.mean((1-y_df_hat_r)**2)
# g_loss = torch.mean((y_df_hat_g)**2)
# loss_disc_f = r_loss + g_loss
# return loss_disc_f.mean()
# def discriminator_forward(self, wav):
# # Squeeze the channel dimension if it's unnecessary
# wav = wav.squeeze(1) # Adjust this line if the channel dimension is not at dim=1
# with torch.no_grad():
# wav_16 = self.resample(wav)
# wav_16 = whisper.pad_or_trim(wav_16)
# wav_16 = whisper.log_mel_spectrogram(wav_16)
# wav_embeddings = self.wavlm.encoder(wav_16, output_hidden_states=True).hidden_states
# y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_d_rs = self.wd(y_embeddings)
# return y_d_rs
###### ORIGINAL
# class WavLMLoss(torch.nn.Module):
# def __init__(self, model, wd, model_sr, slm_sr=16000):
# super(WavLMLoss, self).__init__()
# self.wavlm = AutoModel.from_pretrained(model)
# self.wd = wd
# self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)
# def forward(self, wav, y_rec):
# with torch.no_grad():
# wav_16 = self.resample(wav)
# wav_embeddings = self.wavlm(wav_16, output_hidden_states=True).hidden_states
# y_rec_16 = self.resample(y_rec)
# y_rec_embeddings = self.wavlm(y_rec_16.squeeze(), output_hidden_states=True).hidden_states
# floss = 0
# for er, eg in zip(wav_embeddings, y_rec_embeddings):
# floss += torch.mean(torch.abs(er - eg))
# return floss.mean()
# def generator(self, y_rec):
# y_rec_16 = self.resample(y_rec)
# y_rec_embeddings = self.wavlm(y_rec_16, output_hidden_states=True).hidden_states
# y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_df_hat_g = self.wd(y_rec_embeddings)
# loss_gen = torch.mean((1-y_df_hat_g)**2)
# return loss_gen
# def discriminator(self, wav, y_rec):
# with torch.no_grad():
# wav_16 = self.resample(wav)
# wav_embeddings = self.wavlm(wav_16, output_hidden_states=True).hidden_states
# y_rec_16 = self.resample(y_rec)
# y_rec_embeddings = self.wavlm(y_rec_16, output_hidden_states=True).hidden_states
# y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_rec_embeddings = torch.stack(y_rec_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_d_rs = self.wd(y_embeddings)
# y_d_gs = self.wd(y_rec_embeddings)
# y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs
# r_loss = torch.mean((1-y_df_hat_r)**2)
# g_loss = torch.mean((y_df_hat_g)**2)
# loss_disc_f = r_loss + g_loss
# return loss_disc_f.mean()
# def discriminator_forward(self, wav):
# with torch.no_grad():
# wav_16 = self.resample(wav)
# wav_embeddings = self.wavlm(wav_16, output_hidden_states=True).hidden_states
# y_embeddings = torch.stack(wav_embeddings, dim=1).transpose(-1, -2).flatten(start_dim=1, end_dim=2)
# y_d_rs = self.wd(y_embeddings)
# return y_d_rs |