当前Convention未适配3.10([T]泛型注解导致的问题)
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281
cosyvoice/flow/flow.py
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281
cosyvoice/flow/flow.py
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import random
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from typing import Dict, Optional
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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from omegaconf import DictConfig
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from cosyvoice.utils.mask import make_pad_mask
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class MaskedDiffWithXvec(torch.nn.Module):
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def __init__(self,
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input_size: int = 512,
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output_size: int = 80,
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spk_embed_dim: int = 192,
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output_type: str = "mel",
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vocab_size: int = 4096,
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input_frame_rate: int = 50,
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only_mask_loss: bool = True,
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encoder: torch.nn.Module = None,
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length_regulator: torch.nn.Module = None,
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decoder: torch.nn.Module = None,
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decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
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'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
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'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
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'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
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'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
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mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
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'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
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super().__init__()
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self.input_size = input_size
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self.output_size = output_size
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self.decoder_conf = decoder_conf
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self.mel_feat_conf = mel_feat_conf
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self.vocab_size = vocab_size
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self.output_type = output_type
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self.input_frame_rate = input_frame_rate
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logging.info(f"input frame rate={self.input_frame_rate}")
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self.input_embedding = nn.Embedding(vocab_size, input_size)
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self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
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self.encoder = encoder
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self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
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self.decoder = decoder
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self.length_regulator = length_regulator
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self.only_mask_loss = only_mask_loss
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def forward(
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self,
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batch: dict,
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device: torch.device,
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) -> Dict[str, Optional[torch.Tensor]]:
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token = batch['speech_token'].to(device)
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token_len = batch['speech_token_len'].to(device)
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feat = batch['speech_feat'].to(device)
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feat_len = batch['speech_feat_len'].to(device)
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embedding = batch['embedding'].to(device)
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# xvec projection
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embedding = F.normalize(embedding, dim=1)
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embedding = self.spk_embed_affine_layer(embedding)
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# concat text and prompt_text
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mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
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token = self.input_embedding(torch.clamp(token, min=0)) * mask
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# text encode
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h, h_lengths = self.encoder(token, token_len)
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h = self.encoder_proj(h)
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h, h_lengths = self.length_regulator(h, feat_len)
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# get conditions
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conds = torch.zeros(feat.shape, device=token.device)
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for i, j in enumerate(feat_len):
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if random.random() < 0.5:
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continue
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index = random.randint(0, int(0.3 * j))
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conds[i, :index] = feat[i, :index]
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conds = conds.transpose(1, 2)
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mask = (~make_pad_mask(feat_len)).to(h)
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# NOTE this is unnecessary, feat/h already same shape
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loss, _ = self.decoder.compute_loss(
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feat.transpose(1, 2).contiguous(),
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mask.unsqueeze(1),
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h.transpose(1, 2).contiguous(),
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embedding,
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cond=conds
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)
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return {'loss': loss}
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@torch.inference_mode()
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def inference(self,
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token,
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token_len,
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prompt_token,
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prompt_token_len,
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prompt_feat,
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prompt_feat_len,
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embedding,
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flow_cache):
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assert token.shape[0] == 1
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# xvec projection
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embedding = F.normalize(embedding, dim=1)
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embedding = self.spk_embed_affine_layer(embedding)
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# concat speech token and prompt speech token
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token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
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token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
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mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
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token = self.input_embedding(torch.clamp(token, min=0)) * mask
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# text encode
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h, h_lengths = self.encoder(token, token_len)
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h = self.encoder_proj(h)
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mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
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h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)
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# get conditions
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conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
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conds[:, :mel_len1] = prompt_feat
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conds = conds.transpose(1, 2)
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mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
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feat, flow_cache = self.decoder(
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mu=h.transpose(1, 2).contiguous(),
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mask=mask.unsqueeze(1),
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spks=embedding,
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cond=conds,
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n_timesteps=10,
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prompt_len=mel_len1,
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cache=flow_cache
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)
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feat = feat[:, :, mel_len1:]
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assert feat.shape[2] == mel_len2
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return feat.float(), flow_cache
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class CausalMaskedDiffWithXvec(torch.nn.Module):
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def __init__(self,
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input_size: int = 512,
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output_size: int = 80,
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spk_embed_dim: int = 192,
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output_type: str = "mel",
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vocab_size: int = 4096,
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input_frame_rate: int = 50,
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only_mask_loss: bool = True,
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token_mel_ratio: int = 2,
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pre_lookahead_len: int = 3,
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encoder: torch.nn.Module = None,
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decoder: torch.nn.Module = None,
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decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
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'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
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'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
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'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
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'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
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mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
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'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
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super().__init__()
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self.input_size = input_size
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self.output_size = output_size
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self.decoder_conf = decoder_conf
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self.mel_feat_conf = mel_feat_conf
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self.vocab_size = vocab_size
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self.output_type = output_type
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self.input_frame_rate = input_frame_rate
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logging.info(f"input frame rate={self.input_frame_rate}")
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self.input_embedding = nn.Embedding(vocab_size, input_size)
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self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
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self.encoder = encoder
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self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
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self.decoder = decoder
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self.only_mask_loss = only_mask_loss
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self.token_mel_ratio = token_mel_ratio
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self.pre_lookahead_len = pre_lookahead_len
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def forward(
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self,
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batch: dict,
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device: torch.device,
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) -> Dict[str, Optional[torch.Tensor]]:
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token = batch['speech_token'].to(device)
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token_len = batch['speech_token_len'].to(device)
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feat = batch['speech_feat'].to(device)
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feat_len = batch['speech_feat_len'].to(device)
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embedding = batch['embedding'].to(device)
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# NOTE unified training, static_chunk_size > 0 or = 0
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streaming = True if random.random() < 0.5 else False
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# xvec projection
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embedding = F.normalize(embedding, dim=1)
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embedding = self.spk_embed_affine_layer(embedding)
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# concat text and prompt_text
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mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
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token = self.input_embedding(torch.clamp(token, min=0)) * mask
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# text encode
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h, h_lengths = self.encoder(token, token_len, streaming=streaming)
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h = self.encoder_proj(h)
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# get conditions
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conds = torch.zeros(feat.shape, device=token.device)
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for i, j in enumerate(feat_len):
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if random.random() < 0.5:
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continue
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index = random.randint(0, int(0.3 * j))
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conds[i, :index] = feat[i, :index]
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conds = conds.transpose(1, 2)
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mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
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loss, _ = self.decoder.compute_loss(
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feat.transpose(1, 2).contiguous(),
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mask.unsqueeze(1),
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h.transpose(1, 2).contiguous(),
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embedding,
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cond=conds,
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streaming=streaming,
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)
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return {'loss': loss}
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@torch.inference_mode()
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def inference(self,
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token,
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token_len,
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prompt_token,
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prompt_token_len,
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prompt_feat,
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prompt_feat_len,
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embedding,
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streaming,
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finalize):
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assert token.shape[0] == 1
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# xvec projection
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embedding = F.normalize(embedding, dim=1)
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embedding = self.spk_embed_affine_layer(embedding)
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# concat text and prompt_text
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token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
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mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
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token = self.input_embedding(torch.clamp(token, min=0)) * mask
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# text encode
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if finalize is True:
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h, h_lengths = self.encoder(token, token_len, streaming=streaming)
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else:
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token, context = token[:, :-self.pre_lookahead_len], token[:, -self.pre_lookahead_len:]
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h, h_lengths = self.encoder(token, token_len, context=context, streaming=streaming)
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mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
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h = self.encoder_proj(h)
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# get conditions
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conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
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conds[:, :mel_len1] = prompt_feat
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conds = conds.transpose(1, 2)
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mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
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feat, _ = self.decoder(
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mu=h.transpose(1, 2).contiguous(),
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mask=mask.unsqueeze(1),
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spks=embedding,
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cond=conds,
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n_timesteps=10,
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streaming=streaming
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)
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feat = feat[:, :, mel_len1:]
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assert feat.shape[2] == mel_len2
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return feat.float(), None
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