import numpy as np
import torch
from torch import nn
import math
from typing import Any, Callable, Optional, Tuple, Union
from torch.cuda.amp import autocast, GradScaler
from .vits_config import VitsConfig,VitsPreTrainedModel
from .flow import VitsResidualCouplingBlock
from .duration_predictor import VitsDurationPredictor, VitsStochasticDurationPredictor
from .encoder import VitsTextEncoder
from .decoder import VitsHifiGan
from .posterior_encoder import VitsPosteriorEncoder
from .discriminator import VitsDiscriminator
from .vits_output import VitsModelOutput, VitsTrainingOutput
from .dataset_features_collector import FeaturesCollectionDataset
from .feature_extraction import VitsFeatureExtractor
import os
import sys
from typing import Optional
import tempfile
from torch.cuda.amp import autocast, GradScaler
from IPython.display import clear_output
from transformers import set_seed
import wandb
import logging
import copy
Lst=['input_ids',
'attention_mask',
'waveform',
'labels',
'labels_attention_mask',
'mel_scaled_input_features']
def covert_cuda_batch(d):
#return d
for key in Lst:
d[key]=d[key].cuda(non_blocking=True)
# for key in d['text_encoder_output']:
# d['text_encoder_output'][key]=d['text_encoder_output'][key].cuda(non_blocking=True)
for key in d['posterior_encode_output']:
d['posterior_encode_output'][key]=d['posterior_encode_output'][key].cuda(non_blocking=True)
return d
def generator_loss(disc_outputs):
total_loss = 0
gen_losses = []
for disc_output in disc_outputs:
disc_output = disc_output
loss = torch.mean((1 - disc_output) ** 2)
gen_losses.append(loss)
total_loss += loss
return total_loss, gen_losses
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
real_losses = 0
generated_losses = 0
for disc_real, disc_generated in zip(disc_real_outputs, disc_generated_outputs):
real_loss = torch.mean((1 - disc_real) ** 2)
generated_loss = torch.mean(disc_generated**2)
loss += real_loss + generated_loss
real_losses += real_loss
generated_losses += generated_loss
return loss, real_losses, generated_losses
def feature_loss(feature_maps_real, feature_maps_generated):
loss = 0
for feature_map_real, feature_map_generated in zip(feature_maps_real, feature_maps_generated):
for real, generated in zip(feature_map_real, feature_map_generated):
real = real.detach()
loss += torch.mean(torch.abs(real - generated))
return loss * 2
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
"""
z_p, logs_q: [b, h, t_t]
m_p, logs_p: [b, h, t_t]
"""
z_p = z_p.float()
logs_q = logs_q.float()
m_p = m_p.float()
logs_p = logs_p.float()
z_mask = z_mask.float()
kl = logs_p - logs_q - 0.5
kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
kl = torch.sum(kl * z_mask)
l = kl / torch.sum(z_mask)
return l
#.............................................
# def kl_loss(prior_latents, posterior_log_variance, prior_means, prior_log_variance, labels_mask):
# kl = prior_log_variance - posterior_log_variance - 0.5
# kl += 0.5 * ((prior_latents - prior_means) ** 2) * torch.exp(-2.0 * prior_log_variance)
# kl = torch.sum(kl * labels_mask)
# loss = kl / torch.sum(labels_mask)
# return loss
def get_state_grad_loss(k1=True,
mel=True,
duration=True,
generator=True,
discriminator=True):
return {'k1':k1,'mel':mel,'duration':duration,'generator':generator,'discriminator':discriminator}
def clip_grad_value_(parameters, clip_value, norm_type=2):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
norm_type = float(norm_type)
if clip_value is not None:
clip_value = float(clip_value)
total_norm = 0
for p in parameters:
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item() ** norm_type
if clip_value is not None:
p.grad.data.clamp_(min=-clip_value, max=clip_value)
total_norm = total_norm ** (1. / norm_type)
return total_norm
class VitsModel(VitsPreTrainedModel):
def __init__(self, config: VitsConfig):
super().__init__(config)
self.config = config
self.text_encoder = VitsTextEncoder(config)
self.flow = VitsResidualCouplingBlock(config)
self.decoder = VitsHifiGan(config)
if config.use_stochastic_duration_prediction:
self.duration_predictor = VitsStochasticDurationPredictor(config)
else:
self.duration_predictor = VitsDurationPredictor(config)
if config.num_speakers > 1:
self.embed_speaker = nn.Embedding(config.num_speakers, config.speaker_embedding_size)
# This is used only for training.
self.posterior_encoder = VitsPosteriorEncoder(config)
self.discriminator = VitsDiscriminator(config)
# These parameters control the synthesised speech properties
self.speaking_rate = config.speaking_rate
self.noise_scale = config.noise_scale
self.noise_scale_duration = config.noise_scale_duration
self.segment_size = self.config.segment_size // self.config.hop_length
# Initialize weights and apply final processing
self.post_init()
self.monotonic_alignment_function=self.monotonic_align_max_path
#....................................
def setMfA(self,fn):
self.monotonic_alignment_function=fn
def monotonic_align_max_path(self,log_likelihoods, mask):
# used for training - awfully slow
# an alternative is proposed in examples/pytorch/text-to-speech/run_vits_finetuning.py
path = torch.zeros_like(log_likelihoods)
text_length_maxs = mask.sum(1)[:, 0]
latent_length_maxs = mask.sum(2)[:, 0]
indexes = latent_length_maxs - 1
max_neg_val = -1e9
for batch_id in range(len(path)):
index = int(indexes[batch_id].item())
text_length_max = int(text_length_maxs[batch_id].item())
latent_length_max = int(latent_length_maxs[batch_id].item())
for y in range(text_length_max):
for x in range(max(0, latent_length_max + y - text_length_max), min(latent_length_max, y + 1)):
if x == y:
v_cur = max_neg_val
else:
v_cur = log_likelihoods[batch_id, y - 1, x]
if x == 0:
if y == 0:
v_prev = 0.0
else:
v_prev = max_neg_val
else:
v_prev = log_likelihoods[batch_id, y - 1, x - 1]
log_likelihoods[batch_id, y, x] += max(v_prev, v_cur)
for y in range(text_length_max - 1, -1, -1):
path[batch_id, y, index] = 1
if index != 0 and (
index == y or log_likelihoods[batch_id, y - 1, index] < log_likelihoods[batch_id, y - 1, index - 1]
):
index = index - 1
return path
#....................................
def slice_segments(self,hidden_states, ids_str, segment_size=4):
batch_size, channels, _ = hidden_states.shape
# 1d tensor containing the indices to keep
indices = torch.arange(segment_size).to(ids_str.device)
# extend the indices to match the shape of hidden_states
indices = indices.view(1, 1, -1).expand(batch_size, channels, -1)
# offset indices with ids_str
indices = indices + ids_str.view(-1, 1, 1)
# gather indices
output = torch.gather(hidden_states, dim=2, index=indices)
return output
#....................................
def rand_slice_segments(self,hidden_states, sample_lengths=None, segment_size=4):
batch_size, _, seq_len = hidden_states.size()
if sample_lengths is None:
sample_lengths = seq_len
ids_str_max = sample_lengths - segment_size + 1
ids_str = (torch.rand([batch_size]).to(device=hidden_states.device) * ids_str_max).to(dtype=torch.long)
ret = self.slice_segments(hidden_states, ids_str, segment_size)
return ret, ids_str
#....................................
def resize_speaker_embeddings(
self,
new_num_speakers: int,
speaker_embedding_size: Optional[int] = None,
pad_to_multiple_of: Optional[int] = 2,
):
if pad_to_multiple_of is not None:
new_num_speakers = ((new_num_speakers + pad_to_multiple_of - 1) // pad_to_multiple_of) * pad_to_multiple_of
# first, take care of embed_speaker
if self.config.num_speakers <= 1:
if speaker_embedding_size is None:
raise ValueError(
"The current model had no previous speaker embedding, but `speaker_embedding_size` is not specified. Pass `speaker_embedding_size` to this method."
)
# create new embedding layer
new_embeddings = nn.Embedding(
new_num_speakers,
speaker_embedding_size,
device=self.device,
)
# initialize all new embeddings
self._init_weights(new_embeddings)
else:
new_embeddings = self._get_resized_embeddings(self.embed_speaker, new_num_speakers)
self.embed_speaker = new_embeddings
# then take care of sub-models
self.flow.resize_speaker_embeddings(speaker_embedding_size)
for flow in self.flow.flows:
self._init_weights(flow.wavenet.cond_layer)
self.decoder.resize_speaker_embedding(speaker_embedding_size)
self._init_weights(self.decoder.cond)
self.duration_predictor.resize_speaker_embeddings(speaker_embedding_size)
self._init_weights(self.duration_predictor.cond)
self.posterior_encoder.resize_speaker_embeddings(speaker_embedding_size)
self._init_weights(self.posterior_encoder.wavenet.cond_layer)
self.config.num_speakers = new_num_speakers
self.config.speaker_embedding_size = speaker_embedding_size
#....................................
def get_input_embeddings(self):
return self.text_encoder.get_input_embeddings()
#....................................
def set_input_embeddings(self, value):
self.text_encoder.set_input_embeddings(value)
#....................................
def apply_weight_norm(self):
self.decoder.apply_weight_norm()
self.flow.apply_weight_norm()
self.posterior_encoder.apply_weight_norm()
#....................................
def remove_weight_norm(self):
self.decoder.remove_weight_norm()
self.flow.remove_weight_norm()
self.posterior_encoder.remove_weight_norm()
#....................................
def discriminate(self, hidden_states):
return self.discriminator(hidden_states)
#....................................
def get_encoder(self):
return self.text_encoder
#....................................
def _inference_forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
speaker_embeddings: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
padding_mask: Optional[torch.Tensor] = None,
):
text_encoder_output = self.text_encoder(
input_ids=input_ids,
padding_mask=padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
hidden_states = hidden_states.transpose(1, 2)
input_padding_mask = padding_mask.transpose(1, 2)
prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
if self.config.use_stochastic_duration_prediction:
log_duration = self.duration_predictor(
hidden_states,
input_padding_mask,
speaker_embeddings,
reverse=True,
noise_scale=self.noise_scale_duration,
)
else:
log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
length_scale = 1.0 / self.speaking_rate
duration = torch.ceil(torch.exp(log_duration) * input_padding_mask * length_scale)
predicted_lengths = torch.clamp_min(torch.sum(duration, [1, 2]), 1).long()
# Create a padding mask for the output lengths of shape (batch, 1, max_output_length)
indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device)
output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1)
output_padding_mask = output_padding_mask.unsqueeze(1).to(input_padding_mask.dtype)
# Reconstruct an attention tensor of shape (batch, 1, out_length, in_length)
attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1)
batch_size, _, output_length, input_length = attn_mask.shape
cum_duration = torch.cumsum(duration, -1).view(batch_size * input_length, 1)
indices = torch.arange(output_length, dtype=duration.dtype, device=duration.device)
valid_indices = indices.unsqueeze(0) < cum_duration
valid_indices = valid_indices.to(attn_mask.dtype).view(batch_size, input_length, output_length)
padded_indices = valid_indices - nn.functional.pad(valid_indices, [0, 0, 1, 0, 0, 0])[:, :-1]
attn = padded_indices.unsqueeze(1).transpose(2, 3) * attn_mask
# Expand prior distribution
prior_means = torch.matmul(attn.squeeze(1), prior_means).transpose(1, 2)
prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances).transpose(1, 2)
prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale
latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True)
spectrogram = latents * output_padding_mask
waveform = self.decoder(spectrogram, speaker_embeddings)
waveform = waveform.squeeze(1)
sequence_lengths = predicted_lengths * np.prod(self.config.upsample_rates)
if not return_dict:
outputs = (waveform, sequence_lengths, spectrogram) + text_encoder_output[3:]
return outputs
return VitsModelOutput(
waveform=waveform,
sequence_lengths=sequence_lengths,
spectrogram=spectrogram,
hidden_states=text_encoder_output.hidden_states,
attentions=text_encoder_output.attentions,
)
#....................................
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
speaker_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.FloatTensor] = None,
labels_attention_mask: Optional[torch.Tensor] = None,
monotonic_alignment_function: Optional[Callable] = None,
) -> Union[Tuple[Any], VitsModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
monotonic_alignment_function = (
self.monotonic_align_max_path if monotonic_alignment_function is None else monotonic_alignment_function
)
if attention_mask is not None:
input_padding_mask = attention_mask.unsqueeze(-1).float()
else:
input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
if self.config.num_speakers > 1 and speaker_id is not None:
if isinstance(speaker_id, int):
speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device)
elif isinstance(speaker_id, (list, tuple, np.ndarray)):
speaker_id = torch.tensor(speaker_id, device=self.device)
if not ((0 <= speaker_id).all() and (speaker_id < self.config.num_speakers).all()).item():
raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}.")
if not (len(speaker_id) == 1 or len(speaker_id == len(input_ids))):
raise ValueError(
f"You passed {len(speaker_id)} `speaker_id` but you should either pass one speaker id or `batch_size` `speaker_id`."
)
speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1)
else:
speaker_embeddings = None
# if inference, return inference forward of VitsModel
if labels is None:
return self._inference_forward(
input_ids,
attention_mask,
speaker_embeddings,
output_attentions,
output_hidden_states,
return_dict,
input_padding_mask,
)
if labels_attention_mask is not None:
labels_padding_mask = labels_attention_mask.unsqueeze(1).float()
else:
labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device)
labels_padding_mask = labels_attention_mask.unsqueeze(1)
text_encoder_output = self.text_encoder(
input_ids=input_ids,
padding_mask=input_padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
hidden_states = hidden_states.transpose(1, 2)
input_padding_mask = input_padding_mask.transpose(1, 2)
prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
latents, posterior_means, posterior_log_variances = self.posterior_encoder(
labels, labels_padding_mask, speaker_embeddings
)
prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False)
prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2)
with torch.no_grad():
# negative cross-entropy
# [batch_size, d, latent_length]
prior_variances = torch.exp(-2 * prior_log_variances)
# [batch_size, 1, latent_length]
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True)
# [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances)
# [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances))
# [batch_size, 1, latent_length]
neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True)
# [batch_size, text_length, latent_length]
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1)
attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
durations = attn.sum(2)
if self.config.use_stochastic_duration_prediction:
log_duration = self.duration_predictor(
hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False
)
log_duration = log_duration / torch.sum(input_padding_mask)
else:
log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask
log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask)
# expand priors
prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2)
prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2)
label_lengths = labels_attention_mask.sum(dim=1)
latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size)
waveform = self.decoder(latents_slice, speaker_embeddings)
if not return_dict:
outputs = (
waveform,
log_duration,
attn,
ids_slice,
input_padding_mask,
labels_padding_mask,
latents,
prior_latents,
prior_means,
prior_log_variances,
posterior_means,
posterior_log_variances,
)
return outputs
return VitsTrainingOutput(
waveform=waveform,
log_duration=log_duration,
attn=attn,
ids_slice=ids_slice,
input_padding_mask=input_padding_mask,
labels_padding_mask=labels_padding_mask,
latents=latents,
prior_latents=prior_latents,
prior_means=prior_means,
prior_log_variances=prior_log_variances,
posterior_means=posterior_means,
posterior_log_variances=posterior_log_variances,
)
def slice_segments(self,hidden_states, ids_str, segment_size=4):
batch_size, channels, _ = hidden_states.shape
# 1d tensor containing the indices to keep
indices = torch.arange(segment_size).to(ids_str.device)
# extend the indices to match the shape of hidden_states
indices = indices.view(1, 1, -1).expand(batch_size, channels, -1)
# offset indices with ids_str
indices = indices + ids_str.view(-1, 1, 1)
# gather indices
output = torch.gather(hidden_states, dim=2, index=indices)
return output
#....................................
def rand_slice_segments(self,hidden_states, sample_lengths=None, segment_size=4):
batch_size, _, seq_len = hidden_states.size()
if sample_lengths is None:
sample_lengths = seq_len
ids_str_max = sample_lengths - segment_size + 1
ids_str = (torch.rand([batch_size]).to(device=hidden_states.device) * ids_str_max).to(dtype=torch.long)
ret = self.slice_segments(hidden_states, ids_str, segment_size)
return ret, ids_str
def forward_k(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
speaker_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.FloatTensor] = None,
labels_attention_mask: Optional[torch.Tensor] = None,
text_encoder_output=None,
posterior_encode_output=None,
monotonic_alignment_function: Optional[Callable] = None,
) -> Union[Tuple[Any], VitsModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
monotonic_alignment_function = (
self.monotonic_align_max_path if monotonic_alignment_function is None else monotonic_alignment_function
)
if attention_mask is not None:
input_padding_mask = attention_mask.unsqueeze(-1).float()
else:
input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
if self.config.num_speakers > 1 and speaker_id is not None:
if isinstance(speaker_id, int):
speaker_id = torch.full(size=(1,), fill_value=speaker_id, device=self.device)
elif isinstance(speaker_id, (list, tuple, np.ndarray)):
speaker_id = torch.tensor(speaker_id, device=self.device)
if not ((0 <= speaker_id).all() and (speaker_id < self.config.num_speakers).all()).item():
raise ValueError(f"Set `speaker_id` in the range 0-{self.config.num_speakers - 1}.")
if not (len(speaker_id) == 1 or len(speaker_id == len(input_ids))):
raise ValueError(
f"You passed {len(speaker_id)} `speaker_id` but you should either pass one speaker id or `batch_size` `speaker_id`."
)
speaker_embeddings = self.embed_speaker(speaker_id).unsqueeze(-1)
else:
speaker_embeddings = None
# if inference, return inference forward of VitsModel
if labels is None:
return self._inference_forward(
input_ids,
attention_mask,
speaker_embeddings,
output_attentions,
output_hidden_states,
return_dict,
input_padding_mask,
)
if labels_attention_mask is not None:
labels_padding_mask = labels_attention_mask.unsqueeze(1).float()
else:
labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device)
labels_padding_mask = labels_attention_mask.unsqueeze(1)
if text_encoder_output is None:
text_encoder_output = self.text_encoder(
input_ids=input_ids,
padding_mask=input_padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
hidden_states = hidden_states.transpose(1, 2)
input_padding_mask = input_padding_mask.transpose(1, 2)
prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
if posterior_encode_output is None:
latents, posterior_means, posterior_log_variances = self.posterior_encoder(
labels, labels_padding_mask, speaker_embeddings
)
else:
latents=posterior_encode_output['posterior_latents']
posterior_means=posterior_encode_output['posterior_means']
posterior_log_variances=posterior_encode_output['posterior_log_variances']
prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False)
prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2)
with torch.no_grad():
# negative cross-entropy
# [batch_size, d, latent_length]
prior_variances = torch.exp(-2 * prior_log_variances)
# [batch_size, 1, latent_length]
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True)
# [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances)
# [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances))
# [batch_size, 1, latent_length]
neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True)
# [batch_size, text_length, latent_length]
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1)
attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
durations = attn.sum(2)
if self.config.use_stochastic_duration_prediction:
log_duration = self.duration_predictor(
hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False
)
log_duration = log_duration / torch.sum(input_padding_mask)
else:
log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask
log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask)
# expand priors
prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2)
prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2)
label_lengths = labels_attention_mask.sum(dim=1)
latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size)
waveform = self.decoder(latents_slice, speaker_embeddings)
if not return_dict:
outputs = (
waveform,
log_duration,
attn,
ids_slice,
input_padding_mask,
labels_padding_mask,
latents,
prior_latents,
prior_means,
prior_log_variances,
posterior_means,
posterior_log_variances,
)
return outputs
return VitsTrainingOutput(
waveform=waveform,
log_duration=log_duration,
attn=attn,
ids_slice=ids_slice,
input_padding_mask=input_padding_mask,
labels_padding_mask=labels_padding_mask,
latents=latents,
prior_latents=prior_latents,
prior_means=prior_means,
prior_log_variances=prior_log_variances,
posterior_means=posterior_means,
posterior_log_variances=posterior_log_variances,
)
def trainer(self,
train_dataset_dir = None,
eval_dataset_dir = None,
full_generation_dir = None,
feature_extractor = VitsFeatureExtractor(),
training_args = None,
full_generation_sample_index= 0,
project_name = "Posterior_Decoder_Finetuning",
wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
is_used_text_encoder=True,
is_used_posterior_encode=True,
dict_state_grad_loss=None,
nk=1,
path_save_model='./',
maf=None
):
os.makedirs(training_args.output_dir,exist_ok=True)
logger = logging.getLogger(f"{__name__} Training")
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
wandb.login(key= wandbKey)
wandb.init(project= project_name,config = training_args.to_dict())
if dict_state_grad_loss is None:
dict_state_grad_loss=get_state_grad_loss()
set_seed(training_args.seed)
# Apply Weight Norm Decoder
# self.apply_weight_norm()
# Save Config
self.config.save_pretrained(training_args.output_dir)
train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
device = self.device
)
eval_dataset = None
if training_args.do_eval:
eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
device = self.device
)
full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
device = self.device
)
self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
# init optimizer, lr_scheduler
optimizer = torch.optim.AdamW(
self.parameters(),
training_args.learning_rate,
betas=[training_args.adam_beta1, training_args.adam_beta2],
eps=training_args.adam_epsilon,
)
# hack to be able to train on multiple device
# disc_optimizer = torch.optim.AdamW(
# self.discriminator.parameters(),
# training_args.learning_rate,
# betas=[training_args.adam_beta1, training_args.adam_beta2],
# eps=training_args.adam_epsilon,
# )
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, gamma=training_args.lr_decay, last_epoch=-1
)
# disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
# disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
#.......................loop training............................
global_step = 0
for epoch in range(training_args.num_train_epochs):
train_losses_sum = 0
lr_scheduler.step()
# disc_lr_scheduler.step()
print(f" Num Epochs = {epoch}")
if epoch%nk==0:
print('Save checkpoints Model :',int(epoch/nk))
self.save_pretrained(path_save_model)
for step, batch in enumerate(train_dataset):
# forward through model
# outputs = self.forward(
# labels=batch["labels"],
# labels_attention_mask=batch["labels_attention_mask"],
# speaker_id=batch["speaker_id"]
# )
#if step==10:break
model_outputs = self.forward_k(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
text_encoder_output =None if is_used_text_encoder else batch['text_encoder_output'],
posterior_encode_output=None if is_used_posterior_encode else batch['posterior_encode_output'],
return_dict=True,
monotonic_alignment_function=maf,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
target_waveform = batch["waveform"].transpose(1, 2)
target_waveform = self.slice_segments(
target_waveform,
model_outputs.ids_slice * feature_extractor.hop_length,
self.config.segment_size
)
optimizer.zero_grad()
displayloss={}
# backpropagate
if dict_state_grad_loss['k1']:
loss_kl = kl_loss(
model_outputs.prior_latents,
model_outputs.posterior_log_variances,
model_outputs.prior_means,
model_outputs.prior_log_variances,
model_outputs.labels_padding_mask,
)
loss_kl=loss_kl*training_args.weight_kl
displayloss['loss_kl']=loss_kl.detach().item()
#if displayloss['loss_kl']>=0:
# loss_kl.backward()
if dict_state_grad_loss['mel']:
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
displayloss['loss_mel'] = loss_mel.detach().item()
train_losses_sum = train_losses_sum + displayloss['loss_mel']
# if displayloss['loss_mel']>=0:
# loss_mel.backward()
if dict_state_grad_loss['duration']:
loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
displayloss['loss_duration'] = loss_duration.detach().item()
# if displayloss['loss_duration']>=0:
# loss_duration.backward()
discriminator_target, fmaps_target = self.discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
if dict_state_grad_loss['discriminator']:
loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
discriminator_target, discriminator_candidate
)
dk={"step_loss_disc": loss_disc.detach().item(),
"step_loss_real_disc": loss_real_disc.detach().item(),
"step_loss_fake_disc": loss_fake_disc.detach().item()}
displayloss['dict_loss_discriminator']=dk
loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
loss_dd.backward()
discriminator_target, fmaps_target = self.discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
if dict_state_grad_loss['generator']:
loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
loss_gen, losses_gen = generator_loss(discriminator_candidate)
loss_gen=loss_gen * training_args.weight_gen
displayloss['loss_gen'] = loss_gen.detach().item()
# loss_gen.backward(retain_graph=True)
loss_fmaps=loss_fmaps * training_args.weight_fmaps
displayloss['loss_fmaps'] = loss_fmaps.detach().item()
# loss_fmaps.backward(retain_graph=True)
total_generator_loss = (
loss_duration
+ loss_mel
+ loss_kl
+ loss_fmaps
+ loss_gen
)
total_generator_loss.backward()
optimizer.step()
print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
print(f"display loss function enable :{displayloss}")
global_step +=1
# validation
do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
if do_eval:
logger.info("Running validation... ")
eval_losses_sum = 0
cc=0;
for step, batch in enumerate(eval_dataset):
break
if cc>2: break
cc+=1
with torch.no_grad():
model_outputs = self.forward(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
return_dict=True,
monotonic_alignment_function=None,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
loss = loss_mel.detach().item()
eval_losses_sum +=loss
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation =self.forward(
input_ids =full_generation_sample["input_ids"],
attention_mask=full_generation_sample["attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({
"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
for w in full_generation_waveform],})
wandb.log({"train_losses":train_losses_sum})
# add weight norms
# self.remove_weight_norm()
try:
torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
except:pass
logger.info("Running final full generations samples... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation = self.forward(
input_ids=full_generation_sample["labels"],
attention_mask=full_generation_sample["labels_attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
sample_rate=16000) for w in full_generation_waveform],
})
logger.info("***** Training / Inference Done *****")
#....................................
def trainer_to_cuda(self,
train_dataset_dir = None,
eval_dataset_dir = None,
full_generation_dir = None,
feature_extractor = VitsFeatureExtractor(),
training_args = None,
full_generation_sample_index= 0,
project_name = "Posterior_Decoder_Finetuning",
wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
is_used_text_encoder=True,
is_used_posterior_encode=True,
dict_state_grad_loss=None,
nk=1,
path_save_model='./',
maf=None
):
os.makedirs(training_args.output_dir,exist_ok=True)
logger = logging.getLogger(f"{__name__} Training")
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
wandb.login(key= wandbKey)
wandb.init(project= project_name,config = training_args.to_dict())
if dict_state_grad_loss is None:
dict_state_grad_loss=get_state_grad_loss()
set_seed(training_args.seed)
scaler = GradScaler(enabled=training_args.fp16)
# Apply Weight Norm Decoder
# self.apply_weight_norm()
# Save Config
self.config.save_pretrained(training_args.output_dir)
train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
device = self.device
)
eval_dataset = None
if training_args.do_eval:
eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
device = self.device
)
full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
device = self.device
)
self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
# init optimizer, lr_scheduler
discriminator=self.discriminator
self.discriminator=None
optimizer = torch.optim.AdamW(
self.parameters(),
training_args.learning_rate,
betas=[training_args.adam_beta1, training_args.adam_beta2],
eps=training_args.adam_epsilon,
)
# hack to be able to train on multiple device
disc_optimizer = torch.optim.AdamW(
discriminator.parameters(),
training_args.d_learning_rate,
betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
eps=training_args.adam_epsilon,
)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, gamma=training_args.lr_decay, last_epoch=-1
)
disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
#.......................loop training............................
global_step = 0
for epoch in range(training_args.num_train_epochs):
train_losses_sum = 0
lr_scheduler.step()
disc_lr_scheduler.step()
print(f" Num Epochs = {epoch}")
if (epoch+1)%nk==0:
clear_output()
print('Save checkpoints Model :',int(epoch/nk))
self.discriminator=discriminator
self.save_pretrained(path_save_model)
self.discriminator=None
for step, batch in enumerate(train_dataset):
# forward through model
# outputs = self.forward(
# labels=batch["labels"],
# labels_attention_mask=batch["labels_attention_mask"],
# speaker_id=batch["speaker_id"]
# )
#if step==10:break
batch=covert_cuda_batch(batch)
with autocast(enabled=training_args.fp16):
model_outputs = self.forward_k(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
text_encoder_output =None if is_used_text_encoder else batch['text_encoder_output'],
posterior_encode_output=None if is_used_posterior_encode else batch['posterior_encode_output'],
return_dict=True,
monotonic_alignment_function= maf,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
target_waveform = batch["waveform"].transpose(1, 2)
target_waveform = self.slice_segments(
target_waveform,
model_outputs.ids_slice * feature_extractor.hop_length,
self.config.segment_size
)
discriminator_target, fmaps_target = discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
#with autocast(enabled=False):
if dict_state_grad_loss['discriminator']:
loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
discriminator_target, discriminator_candidate
)
dk={"step_loss_disc": loss_disc.detach().item(),
"step_loss_real_disc": loss_real_disc.detach().item(),
"step_loss_fake_disc": loss_fake_disc.detach().item()}
displayloss['dict_loss_discriminator']=dk
loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
# loss_dd.backward()
disc_optimizer.zero_grad()
scaler.scale(loss_dd).backward()
scaler.unscale_(disc_optimizer )
grad_norm_d = clip_grad_value_(discriminator.parameters(), None)
scaler.step(disc_optimizer)
with autocast(enabled=training_args.fp16):
displayloss={}
# backpropagate
if dict_state_grad_loss['k1']:
loss_kl = kl_loss(
model_outputs.prior_latents,
model_outputs.posterior_log_variances,
model_outputs.prior_means,
model_outputs.prior_log_variances,
model_outputs.labels_padding_mask,
)
loss_kl=loss_kl*training_args.weight_kl
displayloss['loss_kl']=loss_kl.detach().item()
#if displayloss['loss_kl']>=0:
# loss_kl.backward()
if dict_state_grad_loss['mel']:
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
displayloss['loss_mel'] = loss_mel.detach().item()
train_losses_sum = train_losses_sum + displayloss['loss_mel']
# if displayloss['loss_mel']>=0:
# loss_mel.backward()
if dict_state_grad_loss['duration']:
loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
displayloss['loss_duration'] = loss_duration.detach().item()
# if displayloss['loss_duration']>=0:
# loss_duration.backward()
discriminator_target, fmaps_target = discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
if dict_state_grad_loss['generator']:
loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
loss_gen, losses_gen = generator_loss(discriminator_candidate)
loss_gen=loss_gen * training_args.weight_gen
displayloss['loss_gen'] = loss_gen.detach().item()
# loss_gen.backward(retain_graph=True)
loss_fmaps=loss_fmaps * training_args.weight_fmaps
displayloss['loss_fmaps'] = loss_fmaps.detach().item()
# loss_fmaps.backward(retain_graph=True)
total_generator_loss = (
loss_duration
+ loss_mel
+ loss_kl
+ loss_fmaps
+ loss_gen
)
# total_generator_loss.backward()
optimizer.zero_grad()
scaler.scale(total_generator_loss).backward()
scaler.unscale_(optimizer)
grad_norm_g = clip_grad_value_(self.parameters(), None)
scaler.step(optimizer)
scaler.update()
# optimizer.step()
print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
print(f"display loss function enable :{displayloss}")
global_step +=1
# validation
do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
if do_eval:
logger.info("Running validation... ")
eval_losses_sum = 0
cc=0;
for step, batch in enumerate(eval_dataset):
break
if cc>2: break
cc+=1
with torch.no_grad():
model_outputs = self.forward(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
return_dict=True,
monotonic_alignment_function=None,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
loss = loss_mel.detach().item()
eval_losses_sum +=loss
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation =self.forward(
input_ids =full_generation_sample["input_ids"],
attention_mask=full_generation_sample["attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({
"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
for w in full_generation_waveform],})
wandb.log({"train_losses":train_losses_sum})
# add weight norms
# self.remove_weight_norm()
try:
torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
except:pass
logger.info("Running final full generations samples... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation = self.forward(
input_ids=full_generation_sample["labels"],
attention_mask=full_generation_sample["labels_attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
sample_rate=16000) for w in full_generation_waveform],
})
logger.info("***** Training / Inference Done *****")
#....................................
def trainer_to_cuda(self,
train_dataset_dir = None,
eval_dataset_dir = None,
full_generation_dir = None,
feature_extractor = VitsFeatureExtractor(),
training_args = None,
full_generation_sample_index= 0,
project_name = "Posterior_Decoder_Finetuning",
wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
is_used_text_encoder=True,
is_used_posterior_encode=True,
dict_state_grad_loss=None,
nk=1,
path_save_model='./',
maf=None
):
os.makedirs(training_args.output_dir,exist_ok=True)
logger = logging.getLogger(f"{__name__} Training")
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
wandb.login(key= wandbKey)
wandb.init(project= project_name,config = training_args.to_dict())
if dict_state_grad_loss is None:
dict_state_grad_loss=get_state_grad_loss()
set_seed(training_args.seed)
scaler = GradScaler(enabled=training_args.fp16)
# Apply Weight Norm Decoder
# self.apply_weight_norm()
# Save Config
self.config.save_pretrained(training_args.output_dir)
train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
device = self.device
)
eval_dataset = None
if training_args.do_eval:
eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
device = self.device
)
full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
device = self.device
)
self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
# init optimizer, lr_scheduler
optimizer = torch.optim.AdamW(
self.parameters(),
training_args.learning_rate,
betas=[training_args.adam_beta1, training_args.adam_beta2],
eps=training_args.adam_epsilon,
)
# hack to be able to train on multiple device
# disc_optimizer = torch.optim.AdamW(
# self.discriminator.parameters(),
# training_args.d_learning_rate,
# betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
# eps=training_args.adam_epsilon,
# )
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, gamma=training_args.lr_decay, last_epoch=-1
)
# disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
# disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
#.......................loop training............................
global_step = 0
for epoch in range(training_args.num_train_epochs):
train_losses_sum = 0
lr_scheduler.step()
# disc_lr_scheduler.step()
print(f" Num Epochs = {epoch}")
if (epoch+1)%nk==0:
clear_output()
print('Save checkpoints Model :',int(epoch/nk))
self.save_pretrained(path_save_model)
for step, batch in enumerate(train_dataset):
# forward through model
# outputs = self.forward(
# labels=batch["labels"],
# labels_attention_mask=batch["labels_attention_mask"],
# speaker_id=batch["speaker_id"]
# )
#if step==10:break
batch=covert_cuda_batch(batch)
displayloss={}
with autocast(enabled=training_args.fp16):
model_outputs = self.forward_k(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
text_encoder_output =None if is_used_text_encoder else batch['text_encoder_output'],
posterior_encode_output=None if is_used_posterior_encode else batch['posterior_encode_output'],
return_dict=True,
monotonic_alignment_function=maf,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
target_waveform = batch["waveform"].transpose(1, 2)
target_waveform = self.slice_segments(
target_waveform,
model_outputs.ids_slice * feature_extractor.hop_length,
self.config.segment_size
)
discriminator_target, fmaps_target = self.discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
with autocast(enabled=False):
if dict_state_grad_loss['discriminator']:
# disc_optimizer.zero_grad()
loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
discriminator_target, discriminator_candidate
)
dk={"step_loss_disc": loss_disc.detach().item(),
"step_loss_real_disc": loss_real_disc.detach().item(),
"step_loss_fake_disc": loss_fake_disc.detach().item()}
displayloss['dict_loss_discriminator']=dk
loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
# loss_dd.backward()
optimizer.zero_grad()
# disc_optimizer.zero_grad()
scaler.scale(loss_dd).backward()
# scaler.unscale_(disc_optimizer)
#grad_norm_d = clip_grad_value_(self.discriminator.parameters(), None)
# scaler.step(disc_optimizer)
with autocast(enabled=training_args.fp16):
# backpropagate
discriminator_target, fmaps_target = self.discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = self.discriminator(model_outputs.waveform.detach())
with autocast(enabled=False):
if dict_state_grad_loss['k1']:
loss_kl = kl_loss(
model_outputs.prior_latents,
model_outputs.posterior_log_variances,
model_outputs.prior_means,
model_outputs.prior_log_variances,
model_outputs.labels_padding_mask,
)
loss_kl=loss_kl*training_args.weight_kl
displayloss['loss_kl']=loss_kl.detach().item()
#if displayloss['loss_kl']>=0:
# loss_kl.backward()
if dict_state_grad_loss['mel']:
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
displayloss['loss_mel'] = loss_mel.detach().item()
train_losses_sum = train_losses_sum + displayloss['loss_mel']
# if displayloss['loss_mel']>=0:
# loss_mel.backward()
if dict_state_grad_loss['duration']:
loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
displayloss['loss_duration'] = loss_duration.detach().item()
# if displayloss['loss_duration']>=0:
# loss_duration.backward()
if dict_state_grad_loss['generator']:
loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
loss_gen, losses_gen = generator_loss(discriminator_candidate)
loss_gen=loss_gen * training_args.weight_gen
displayloss['loss_gen'] = loss_gen.detach().item()
# loss_gen.backward(retain_graph=True)
loss_fmaps=loss_fmaps * training_args.weight_fmaps
displayloss['loss_fmaps'] = loss_fmaps.detach().item()
# loss_fmaps.backward(retain_graph=True)
total_generator_loss = (
loss_duration
+ loss_mel
+ loss_kl
+ loss_fmaps
+ loss_gen
)
# total_generator_loss.backward()
scaler.scale(total_generator_loss).backward()
scaler.unscale_(optimizer)
grad_norm_g = clip_grad_value_(self.parameters(), None)
scaler.step(optimizer)
scaler.update()
# optimizer.step()
print(f"TRAINIG - batch {step},Grad G{grad_norm_g}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
print(f"display loss function enable :{displayloss}")
global_step +=1
# validation
do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
if do_eval:
logger.info("Running validation... ")
eval_losses_sum = 0
cc=0;
for step, batch in enumerate(eval_dataset):
break
if cc>2: break
cc+=1
with torch.no_grad():
model_outputs = self.forward(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
return_dict=True,
monotonic_alignment_function=None,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
loss = loss_mel.detach().item()
eval_losses_sum +=loss
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation =self.forward(
input_ids =full_generation_sample["input_ids"],
attention_mask=full_generation_sample["attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({
"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
for w in full_generation_waveform],})
wandb.log({"train_losses":train_losses_sum})
# add weight norms
# self.remove_weight_norm()
try:
torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
except:pass
logger.info("Running final full generations samples... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation = self.forward(
input_ids=full_generation_sample["labels"],
attention_mask=full_generation_sample["labels_attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
sample_rate=16000) for w in full_generation_waveform],
})
logger.info("***** Training / Inference Done *****")
#....................................
def trainer_to(self,
train_dataset_dir = None,
eval_dataset_dir = None,
full_generation_dir = None,
feature_extractor = VitsFeatureExtractor(),
training_args = None,
full_generation_sample_index= 0,
project_name = "Posterior_Decoder_Finetuning",
wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
is_used_text_encoder=True,
is_used_posterior_encode=True,
dict_state_grad_loss=None,
nk=1,
path_save_model='./',
maf=None
):
os.makedirs(training_args.output_dir,exist_ok=True)
logger = logging.getLogger(f"{__name__} Training")
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
wandb.login(key= wandbKey)
wandb.init(project= project_name,config = training_args.to_dict())
if dict_state_grad_loss is None:
dict_state_grad_loss=get_state_grad_loss()
set_seed(training_args.seed)
# Apply Weight Norm Decoder
# self.apply_weight_norm()
# Save Config
self.config.save_pretrained(training_args.output_dir)
train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
device = self.device
)
eval_dataset = None
if training_args.do_eval:
eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
device = self.device
)
full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
device = self.device
)
self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
# init optimizer, lr_scheduler
optimizer = torch.optim.AdamW(
self.parameters(),
training_args.learning_rate,
betas=[training_args.adam_beta1, training_args.adam_beta2],
eps=training_args.adam_epsilon,
)
# hack to be able to train on multiple device
disc_optimizer = torch.optim.AdamW(
self.discriminator.parameters(),
training_args.d_learning_rate,
betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
eps=training_args.adam_epsilon,
)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, gamma=training_args.lr_decay, last_epoch=-1
)
disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
#.......................loop training............................
global_step = 0
for epoch in range(training_args.num_train_epochs):
train_losses_sum = 0
lr_scheduler.step()
disc_lr_scheduler.step()
print(f" Num Epochs = {epoch}")
if epoch%nk==0:
clear_output()
print('')
print('Save checkpoints Model :',int(epoch/nk))
self.save_pretrained(path_save_model)
for step, batch in enumerate(train_dataset):
# forward through model
# outputs = self.forward(
# labels=batch["labels"],
# labels_attention_mask=batch["labels_attention_mask"],
# speaker_id=batch["speaker_id"]
# )
#if step==10:break
batch=covert_cuda_batch(batch)
waveform,ids_slice,log_duration,prior_latents,posterior_log_variances,prior_means,prior_log_variances,labels_padding_mask=self.forward_train(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
text_encoder_output =None , #if is_used_text_encoder else batch['text_encoder_output'],
posterior_encode_output=batch['posterior_encode_output'] ,# if is_used_posterior_encode else ,
return_dict=True,
monotonic_alignment_function= maf,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(waveform.squeeze(1))[1]
target_waveform = batch["waveform"].transpose(1, 2)
target_waveform = self.slice_segments(
target_waveform,
ids_slice * feature_extractor.hop_length,
self.config.segment_size
)
displayloss={}
# backpropagate
#if dict_state_grad_loss['k1']:
loss_kl = kl_loss(
prior_latents,
posterior_log_variances,
prior_means,
prior_log_variances,
labels_padding_mask,
)
loss_kl=loss_kl*training_args.weight_kl
displayloss['loss_kl']=loss_kl.detach().item()
#if displayloss['loss_kl']>=0:
# loss_kl.backward()
# if dict_state_grad_loss['mel']:
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
displayloss['loss_mel'] = loss_mel.detach().item()
train_losses_sum = train_losses_sum + displayloss['loss_mel']
# if displayloss['loss_mel']>=0:
# loss_mel.backward()
#if dict_state_grad_loss['duration']:
loss_duration=torch.sum(log_duration)*training_args.weight_duration
displayloss['loss_duration'] = loss_duration.detach().item()
# if displayloss['loss_duration']>=0:
# loss_duration.backward()
discriminator_target, fmaps_target = self.discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = self.discriminator(waveform.detach())
#if dict_state_grad_loss['discriminator']:
disc_optimizer.zero_grad()
loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
discriminator_target, discriminator_candidate
)
dk={"step_loss_disc": loss_disc.detach().item(),
"step_loss_real_disc": loss_real_disc.detach().item(),
"step_loss_fake_disc": loss_fake_disc.detach().item()}
displayloss['dict_loss_discriminator']=dk
loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
loss_dd.backward()
disc_optimizer.step()
discriminator_target, fmaps_target = self.discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = self.discriminator(waveform.detach())
optimizer.zero_grad()
# if dict_state_grad_loss['generator']:
loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
loss_gen, losses_gen = generator_loss(discriminator_candidate)
loss_gen=loss_gen * training_args.weight_gen
displayloss['loss_gen'] = loss_gen.detach().item()
# loss_gen.backward(retain_graph=True)
loss_fmaps=loss_fmaps * training_args.weight_fmaps
displayloss['loss_fmaps'] = loss_fmaps.detach().item()
# loss_fmaps.backward(retain_graph=True)
total_generator_loss = (
loss_duration
+ loss_mel
+ loss_kl
+ loss_fmaps
+ loss_gen
)
total_generator_loss.backward()
optimizer.step()
print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
print(f"display loss function enable :{displayloss}")
global_step +=1
# validation
do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
if do_eval:
logger.info("Running validation... ")
eval_losses_sum = 0
cc=0;
for step, batch in enumerate(eval_dataset):
break
if cc>2: break
cc+=1
with torch.no_grad():
model_outputs = self.forward(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
return_dict=True,
monotonic_alignment_function=None,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
loss = loss_mel.detach().item()
eval_losses_sum +=loss
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation =self.forward(
input_ids =full_generation_sample["input_ids"],
attention_mask=full_generation_sample["attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({
"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
for w in full_generation_waveform],})
wandb.log({"train_losses":train_losses_sum})
# add weight norms
# self.remove_weight_norm()
try:
torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
except:pass
logger.info("Running final full generations samples... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation = self.forward(
input_ids=full_generation_sample["labels"],
attention_mask=full_generation_sample["labels_attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
sample_rate=16000) for w in full_generation_waveform],
})
logger.info("***** Training / Inference Done *****")
#....................................
def trainer_to_cuda1(self,
train_dataset_dir = None,
eval_dataset_dir = None,
full_generation_dir = None,
feature_extractor = VitsFeatureExtractor(),
training_args = None,
full_generation_sample_index= 0,
project_name = "Posterior_Decoder_Finetuning",
wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
is_used_text_encoder=True,
is_used_posterior_encode=True,
dict_state_grad_loss=None,
nk=1,
path_save_model='./',
maf=None
):
os.makedirs(training_args.output_dir,exist_ok=True)
logger = logging.getLogger(f"{__name__} Training")
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
wandb.login(key= wandbKey)
wandb.init(project= project_name,config = training_args.to_dict())
if dict_state_grad_loss is None:
dict_state_grad_loss=get_state_grad_loss()
set_seed(training_args.seed)
scaler = GradScaler(enabled=training_args.fp16)
# Apply Weight Norm Decoder
# self.apply_weight_norm()
# Save Config
self.config.save_pretrained(training_args.output_dir)
train_dataset = FeaturesCollectionDataset(dataset_dir = train_dataset_dir,
device = self.device
)
eval_dataset = None
if training_args.do_eval:
eval_dataset = FeaturesCollectionDataset(dataset_dir = eval_dataset_dir,
device = self.device
)
full_generation_dataset = FeaturesCollectionDataset(dataset_dir = full_generation_dir,
device = self.device
)
self.full_generation_sample = full_generation_dataset[full_generation_sample_index]
# init optimizer, lr_scheduler
discriminator=self.discriminator
self.discriminator=None
optimizer = torch.optim.AdamW(
self.parameters(),
training_args.learning_rate,
betas=[training_args.adam_beta1, training_args.adam_beta2],
eps=training_args.adam_epsilon,
)
# hack to be able to train on multiple device
disc_optimizer = torch.optim.AdamW(
discriminator.parameters(),
training_args.d_learning_rate,
betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
eps=training_args.adam_epsilon,
)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, gamma=training_args.lr_decay, last_epoch=-1
)
disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
logger.info("***** Running training *****")
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
#.......................loop training............................
global_step = 0
for epoch in range(training_args.num_train_epochs):
train_losses_sum = 0
lr_scheduler.step()
disc_lr_scheduler.step()
print(f" Num Epochs = {epoch}")
if epoch%nk==0:
clear_output()
print('Save checkpoints Model :',int(epoch/nk))
self.discriminator=discriminator
self.save_pretrained(path_save_model)
self.discriminator=None
for step, batch in enumerate(train_dataset):
# forward through model
# outputs = self.forward(
# labels=batch["labels"],
# labels_attention_mask=batch["labels_attention_mask"],
# speaker_id=batch["speaker_id"]
# )
#if step==10:break
batch=covert_cuda_batch(batch)
displayloss={}
with autocast(enabled=training_args.fp16):
model_outputs = self.forward_k(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
text_encoder_output =None if is_used_text_encoder else batch['text_encoder_output'],
posterior_encode_output=None if is_used_posterior_encode else batch['posterior_encode_output'],
return_dict=True,
monotonic_alignment_function= maf,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
target_waveform = batch["waveform"].transpose(1, 2)
target_waveform = self.slice_segments(
target_waveform,
model_outputs.ids_slice * feature_extractor.hop_length,
self.config.segment_size
)
discriminator_target, fmaps_target = discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
#with autocast(enabled=False):
if dict_state_grad_loss['discriminator']:
loss_disc, loss_real_disc, loss_fake_disc = discriminator_loss(
discriminator_target, discriminator_candidate
)
dk={"step_loss_disc": loss_disc.detach().item(),
"step_loss_real_disc": loss_real_disc.detach().item(),
"step_loss_fake_disc": loss_fake_disc.detach().item()}
displayloss['dict_loss_discriminator']=dk
loss_dd = loss_disc# + loss_real_disc + loss_fake_disc
disc_optimizer.zero_grad()
loss_dd.backward()
# scaler.scale(loss_dd).backward()
# scaler.unscale_(disc_optimizer )
grad_norm_d = clip_grad_value_(discriminator.parameters(), None)
disc_optimizer.step()
with autocast(enabled=training_args.fp16):
# backpropagate
if dict_state_grad_loss['k1']:
loss_kl = kl_loss(
model_outputs.prior_latents,
model_outputs.posterior_log_variances,
model_outputs.prior_means,
model_outputs.prior_log_variances,
model_outputs.labels_padding_mask,
)
loss_kl=loss_kl*training_args.weight_kl
displayloss['loss_kl']=loss_kl.detach().item()
#if displayloss['loss_kl']>=0:
# loss_kl.backward()
if dict_state_grad_loss['mel']:
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
displayloss['loss_mel'] = loss_mel.detach().item()
train_losses_sum = train_losses_sum + displayloss['loss_mel']
# if displayloss['loss_mel']>=0:
# loss_mel.backward()
if dict_state_grad_loss['duration']:
loss_duration=torch.sum(model_outputs.log_duration)*training_args.weight_duration
displayloss['loss_duration'] = loss_duration.detach().item()
# if displayloss['loss_duration']>=0:
# loss_duration.backward()
discriminator_target, fmaps_target = discriminator(target_waveform)
discriminator_candidate, fmaps_candidate = discriminator(model_outputs.waveform.detach())
if dict_state_grad_loss['generator']:
loss_fmaps = feature_loss(fmaps_target, fmaps_candidate)
loss_gen, losses_gen = generator_loss(discriminator_candidate)
loss_gen=loss_gen * training_args.weight_gen
displayloss['loss_gen'] = loss_gen.detach().item()
# loss_gen.backward(retain_graph=True)
loss_fmaps=loss_fmaps * training_args.weight_fmaps
displayloss['loss_fmaps'] = loss_fmaps.detach().item()
# loss_fmaps.backward(retain_graph=True)
total_generator_loss = (
loss_duration
+ loss_mel
+ loss_kl
+ loss_fmaps
+ loss_gen
)
optimizer.zero_grad()
total_generator_loss.backward()
# scaler.scale(total_generator_loss).backward()
# scaler.unscale_(optimizer)
grad_norm_g = clip_grad_value_(self.parameters(), None)
optimizer.step()
# scaler.update()
# optimizer.step()
print(f"TRAINIG - batch {step}, waveform {(batch['waveform'].shape)}, lr {lr_scheduler.get_last_lr()[0]}... ")
print(f"display loss function enable :{displayloss}")
global_step +=1
# validation
do_eval = training_args.do_eval and (global_step % training_args.eval_steps == 0)
if do_eval:
logger.info("Running validation... ")
eval_losses_sum = 0
cc=0;
for step, batch in enumerate(eval_dataset):
break
if cc>2: break
cc+=1
with torch.no_grad():
model_outputs = self.forward(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"],
labels_attention_mask=batch["labels_attention_mask"],
speaker_id=batch["speaker_id"],
return_dict=True,
monotonic_alignment_function=None,
)
mel_scaled_labels = batch["mel_scaled_input_features"]
mel_scaled_target = self.slice_segments(mel_scaled_labels, model_outputs.ids_slice,self.segment_size)
mel_scaled_generation = feature_extractor._torch_extract_fbank_features(model_outputs.waveform.squeeze(1))[1]
loss = loss_mel.detach().item()
eval_losses_sum +=loss
loss_mel = torch.nn.functional.l1_loss(mel_scaled_target, mel_scaled_generation)
print(f"VALIDATION - batch {step}, waveform {(batch['waveform'].shape)}, step_loss_mel {loss} ... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation =self.forward(
input_ids =full_generation_sample["input_ids"],
attention_mask=full_generation_sample["attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({
"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}", sample_rate=16000)
for w in full_generation_waveform],})
wandb.log({"train_losses":train_losses_sum})
# add weight norms
# self.remove_weight_norm()
try:
torch.save(self.posterior_encoder.state_dict(), os.path.join(training_args.output_dir,"posterior_encoder.pt"))
torch.save(self.decoder.state_dict(), os.path.join(training_args.output_dir,"decoder.pt"))
except:pass
logger.info("Running final full generations samples... ")
with torch.no_grad():
full_generation_sample = self.full_generation_sample
full_generation = self.forward(
input_ids=full_generation_sample["labels"],
attention_mask=full_generation_sample["labels_attention_mask"],
speaker_id=full_generation_sample["speaker_id"]
)
full_generation_waveform = full_generation.waveform.cpu().numpy()
wandb.log({"eval_losses": eval_losses_sum,
"full generations samples": [
wandb.Audio(w.reshape(-1), caption=f"Full generation sample {epoch}",
sample_rate=16000) for w in full_generation_waveform],
})
logger.info("***** Training / Inference Done *****")
def forward_train(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
speaker_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.FloatTensor] = None,
labels_attention_mask: Optional[torch.Tensor] = None,
text_encoder_output=None,
posterior_encode_output=None,
monotonic_alignment_function: Optional[Callable] = None,
speaker_embeddings=None
) -> Union[Tuple[Any], VitsModelOutput]:
#output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states# if output_hidden_states is not None else self.config.output_hidden_states
)
# return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# if attention_mask is not None:
input_padding_mask = attention_mask.unsqueeze(-1).float()
#else:
# input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
# speaker_embeddings=None
# if labels_attention_mask is not None:
labels_padding_mask = labels_attention_mask.unsqueeze(1).float()
# else:
# labels_attention_mask = torch.ones((labels.shape[0], labels.shape[2])).float().to(self.device)
# labels_padding_mask = labels_attention_mask.unsqueeze(1)
if text_encoder_output is None:
text_encoder_output = self.text_encoder(
input_ids=input_ids,
padding_mask=input_padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
#hidden_states = text_encoder_output[0] #if not return_dict else text_encoder_output.last_hidden_state
hidden_states = text_encoder_output[0].transpose(1, 2)
input_padding_mask = input_padding_mask.transpose(1, 2)
prior_means = text_encoder_output[1].transpose(1, 2) #if not return_dict else text_encoder_output.prior_means
prior_log_variances = text_encoder_output[2].transpose(1, 2) #if not return_dict else text_encoder_output.prior_log_variances
if posterior_encode_output is None:
latents, posterior_means, posterior_log_variances = self.posterior_encoder(
labels, labels_padding_mask, speaker_embeddings
)
else:
latents=posterior_encode_output['posterior_latents']
posterior_means=posterior_encode_output['posterior_means']
posterior_log_variances=posterior_encode_output['posterior_log_variances']
prior_latents = self.flow(latents, labels_padding_mask, speaker_embeddings, reverse=False)
# prior_means, prior_log_variances = prior_means.transpose(1, 2), prior_log_variances.transpose(1, 2)
with torch.no_grad():
# negative cross-entropy
# [batch_size, d, latent_length]
prior_variances = torch.exp(-2 * prior_log_variances)
# [batch_size, 1, latent_length]
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - prior_log_variances, [1], keepdim=True)
# [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
neg_cent2 = torch.matmul(-0.5 * (prior_latents**2).transpose(1, 2), prior_variances)
# [batch_size, text_length, d] x [batch_size, d, latent_length] = [batch_size, text_length, latent_length]
neg_cent3 = torch.matmul(prior_latents.transpose(1, 2), (prior_means * prior_variances))
# [batch_size, 1, latent_length]
neg_cent4 = torch.sum(-0.5 * (prior_means**2) * prior_variances, [1], keepdim=True)
# [batch_size, text_length, latent_length]
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(labels_padding_mask, -1)
attn = monotonic_alignment_function(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
durations = attn.sum(2)
#if self.config.use_stochastic_duration_prediction:
log_duration = self.duration_predictor(
hidden_states, input_padding_mask, speaker_embeddings, durations=durations, reverse=False
)
log_duration = log_duration / torch.sum(input_padding_mask)
# else:
# log_duration_padded = torch.log(durations + 1e-6) * input_padding_mask
# log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
# log_duration = torch.sum((log_duration - log_duration_padded) ** 2, [1, 2]) / torch.sum(input_padding_mask)
# expand priors
prior_means = torch.matmul(attn.squeeze(1), prior_means.transpose(1, 2)).transpose(1, 2)
prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances.transpose(1, 2)).transpose(1, 2)
label_lengths = labels_attention_mask.sum(dim=1)
latents_slice, ids_slice = self.rand_slice_segments(latents, label_lengths, segment_size=self.segment_size)
waveform = self.decoder(latents_slice, speaker_embeddings)
return waveform,ids_slice,log_duration,prior_latents,posterior_log_variances,prior_means,prior_log_variances,labels_padding_mask