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#
import logging
import random
from typing import Dict, Optional
import os
os.environ["TORCH_JIT_DISABLE"] = "1"
import torch
import torch.nn as nn
from torch.nn import functional as F
from omegaconf import DictConfig
from cosyvoice_rodis.utils.mask import make_pad_mask
from cosyvoice_rodis.utils.losses import OrthogonalityLoss
class MaskedDiffWithXvec(torch.nn.Module):
def __init__(self,
input_size: int = 512,
output_size: int = 80,
spk_embed_dim: int = 192,
output_type: str = "mel",
vocab_size: int = 4096,
input_frame_rate: int = 50,
only_mask_loss: bool = True,
encoder: torch.nn.Module = None,
length_regulator: torch.nn.Module = None,
decoder: torch.nn.Module = None,
decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000},
flow_emotion_embedding: bool = False,
flow_orth_loss: bool = False,
cross_orth_loss: bool = False):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.decoder_conf = decoder_conf
self.mel_feat_conf = mel_feat_conf
self.vocab_size = vocab_size
self.output_type = output_type
self.input_frame_rate = input_frame_rate
logging.info(f"input frame rate={self.input_frame_rate}")
self.input_embedding = nn.Embedding(vocab_size, input_size)
self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
self.encoder = encoder
self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
self.decoder = decoder
self.length_regulator = length_regulator
self.only_mask_loss = only_mask_loss
self.flow_emotion_embedding = flow_emotion_embedding
self.flow_orth_loss = flow_orth_loss
self.cross_orth_loss = cross_orth_loss
if self.flow_emotion_embedding:
self.flow_emotion_embedding_proj = torch.nn.Linear(spk_embed_dim, spk_embed_dim)
self.speaker_projector = nn.Linear(spk_embed_dim, spk_embed_dim)
def forward(
self,
batch: dict,
device: torch.device,
) -> Dict[str, Optional[torch.Tensor]]:
token = batch['speech_token'].to(device)
token_len = batch['speech_token_len'].to(device)
feat = batch['speech_feat'].to(device)
feat_len = batch['speech_feat_len'].to(device)
embedding = batch['embedding'].to(device)
if self.flow_emotion_embedding:
flow_emotion_embedding = batch['emotion_embedding'].to(device) #[270,192]
flow_emotion_embedding = F.normalize(flow_emotion_embedding, dim=1)
flow_emotion_embedding = self.flow_emotion_embedding_proj(flow_emotion_embedding)
embedding = self.speaker_projector(embedding)
if self.cross_orth_loss: #false
orth_loss = 0.0
batch_size = embedding.size(0)
if batch_size > 1:
batch_contrastive_orth_loss=0
for i in range(batch_size):
for j in range(i + 1, batch_size):
batch_contrastive_orth_loss =batch_contrastive_orth_loss+ torch.abs(torch.dot(embedding[i], flow_emotion_embedding[j]))
num_pairs = (batch_size * (batch_size - 1)) / 2
batch_contrastive_orth_loss = batch_contrastive_orth_loss / num_pairs
else:
batch_contrastive_orth_loss = 0
orth_loss = OrthogonalityLoss(embedding, flow_emotion_embedding)
else:
orth_loss = OrthogonalityLoss(embedding, flow_emotion_embedding)
embedding = F.normalize(embedding, dim=1)
embedding = self.spk_embed_affine_layer(embedding)
mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
token = self.input_embedding(torch.clamp(token, min=0)) * mask
# text encode
h, h_lengths = self.encoder(token, token_len, flow_emotion_embedding)
h = self.encoder_proj(h)
h, h_lengths = self.length_regulator(h, feat_len)
# get conditions
conds = torch.zeros(feat.shape, device=token.device)
for i, j in enumerate(feat_len):
if random.random() < 0.5:
continue
index = random.randint(0, int(0.3 * j))
conds[i, :index] = feat[i, :index]
conds = conds.transpose(1, 2)
mask = (~make_pad_mask(feat_len)).to(h)
feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
mse_loss, _ = self.decoder.compute_loss(
feat.transpose(1, 2).contiguous(),
mask.unsqueeze(1),
h.transpose(1, 2).contiguous(),
embedding,
cond=conds
)
if self.flow_orth_loss and self.flow_emotion_embedding:
loss =mse_loss+ orth_loss+batch_contrastive_orth_loss
return {'loss': loss, "mse_loss":mse_loss,"orth_loss":orth_loss,"contrastive_loss":batch_contrastive_orth_loss}
@torch.inference_mode()
def inference(self,
token,
token_len,
prompt_token,
prompt_token_len,
prompt_feat,
prompt_feat_len,
embedding,
flow_cache,
flow_emotion_embedding=None):
assert token.shape[0] == 1
if self.flow_emotion_embedding and flow_emotion_embedding is not None:
flow_emotion_embedding = F.normalize(flow_emotion_embedding.unsqueeze(0).to(torch.float16), dim=1)
flow_emotion_embedding = self.flow_emotion_embedding_proj(flow_emotion_embedding)
embedding = self.speaker_projector(embedding)
embedding += flow_emotion_embedding
# xvec projection
embedding = F.normalize(embedding, dim=1)
embedding = self.spk_embed_affine_layer(embedding)
# concat text and prompt_text
token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
token = self.input_embedding(torch.clamp(token, min=0).long()) * mask
# text encode
print("token:", token, token_len, flow_emotion_embedding)
print("token shape:", token.shape(), token_len.shape(), flow_emotion_embedding.shape())
h, h_lengths = self.encoder(token, token_len, flow_emotion_embedding)
h = self.encoder_proj(h) #torch.Size([1, 358, 80])
mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)
# get conditions
conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device)
conds[:, :mel_len1] = prompt_feat
conds = conds.transpose(1, 2)
mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
feat, flow_cache = self.decoder(
mu=h.transpose(1, 2).contiguous(),
mask=mask.unsqueeze(1),
spks=embedding,
cond=conds,
n_timesteps=10,
prompt_len=mel_len1,
flow_cache=flow_cache
)
feat = feat[:, :, mel_len1:]
assert feat.shape[2] == mel_len2
return feat, flow_cache |