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import torch
import torch.nn as nn
from transformers import PreTrainedModel
from configuration_bigcodec import BigCodecConfig
# 请确保这些模块路径是正确的
from vq.codec_encoder import CodecEncoder_Transformer
from vq.codec_decoder_vocos import CodecDecoderVocos
from vq.module import SemanticEncoder
from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
class XCodec2Model(PreTrainedModel):
config_class = BigCodecConfig
def __init__(self, config: BigCodecConfig):
super().__init__(config)
# 1) 语义模型
self.semantic_model = Wav2Vec2BertModel.from_pretrained(
"facebook/w2v-bert-2.0",
output_hidden_states=True
)
self.semantic_model.eval()
self.SemanticEncoder_module = SemanticEncoder(
config.semantic_hidden_size,
config.semantic_hidden_size,
config.semantic_hidden_size
)
# 2) Codec Encoder
self.CodecEnc = CodecEncoder_Transformer()
# 3) Codec Decoder
self.generator = CodecDecoderVocos()
# 4) 两个全连接层
self.fc_prior = nn.Linear(2048, 2048)
self.fc_post_a = nn.Linear(2048, 1024)
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
self.feature_extractor = feature_extractor
def forward(self, input_waveform, sample_rate=16000):
"""
这里的 forward 不一定要叫 forward，也可以拆成别的方法；
但是如果想兼容 pipeline，需要在 forward 里给出核心逻辑。
参数：
input_waveform: [batch_size, waveform_length]
sample_rate: 默认 16000
返回：
重构后的语音音频 (Tensor)
"""
# 1) 特征提取
# 如果需要 padding，可以在这里做
input_features = self.feature_extractor(
input_waveform,
sampling_rate=sample_rate,
return_tensors="pt"
).input_features.to(self.device) # [batch, frames, feat_dim]
# 2) 语义层
semantic_output = self.semantic_model(input_features)
semantic_hidden_16 = semantic_output.hidden_states[16] # 取第16层
semantic_hidden_16 = semantic_hidden_16.transpose(1, 2) # [batch, hidden_dim, frames]
semantic_encoded = self.SemanticEncoder_module(semantic_hidden_16)
# 3) codec encoder
wav = input_waveform.unsqueeze(1).to(self.device) # shape: [batch, 1, time]
vq_emb = self.CodecEnc(wav) # [batch, time//down, 1024] 只是示例
vq_emb = vq_emb.transpose(1, 2) # -> [batch, 1024, frames]
# 对齐语义向量的时间帧数，这里只做示例处理
# 真实做法里可能要先对齐维度
if vq_emb.shape[-1] != semantic_encoded.shape[-1]:
# 简单强行截断或补零都行，需要你自己决定
min_len = min(vq_emb.shape[-1], semantic_encoded.shape[-1])
vq_emb = vq_emb[:, :, :min_len]
semantic_encoded = semantic_encoded[:, :, :min_len]
# 4) 拼接
concat_emb = torch.cat([semantic_encoded, vq_emb], dim=1) # [batch, 1024 + 1024, frames]
# 5) fc_prior
concat_emb = self.fc_prior(concat_emb.transpose(1, 2)).transpose(1, 2)
# 6) decoder 的量化部分
_, vq_code, _ = self.generator(concat_emb, vq=True)
vq_post_emb = self.generator.quantizer.get_output_from_indices(vq_code.transpose(1, 2))
vq_post_emb = vq_post_emb.transpose(1, 2)
# 7) fc_post_a
vq_post_emb = self.fc_post_a(vq_post_emb.transpose(1, 2)).transpose(1, 2)
# 8) 最后解码成波形
recon_audio = self.generator(vq_post_emb.transpose(1, 2), vq=False)[0]
# recon_audio: [batch, time]
return recon_audio
def encode_code(self, input_waveform, sample_rate=16000):
"""
将输入的音频编码为代码表示。
参数：
input_waveform: [batch_size, waveform_length]
sample_rate: 默认 16000
返回：
编码后的代码 (Tensor)
"""
with torch.no_grad():
# 1) 特征提取
input_features = self.feature_extractor(
input_waveform,
sampling_rate=sample_rate,
return_tensors="pt"
).input_features.to(self.device) # [batch, frames, feat_dim]
# 2) 语义层
semantic_output = self.semantic_model(input_features)
semantic_hidden_16 = semantic_output.hidden_states[16] # 取第16层
semantic_hidden_16 = semantic_hidden_16.transpose(1, 2) # [batch, hidden_dim, frames]
semantic_encoded = self.SemanticEncoder_module(semantic_hidden_16)
# 3) codec encoder
wav = input_waveform.unsqueeze(1).to(self.device) # shape: [batch, 1, time]
vq_emb = self.CodecEnc(wav) # [batch, time//down, 1024] 只是示例
vq_emb = vq_emb.transpose(1, 2) # -> [batch, 1024, frames]
# 对齐语义向量的时间帧数，这里只做示例处理
if vq_emb.shape[-1] != semantic_encoded.shape[-1]:
min_len = min(vq_emb.shape[-1], semantic_encoded.shape[-1])
vq_emb = vq_emb[:, :, :min_len]
semantic_encoded = semantic_encoded[:, :, :min_len]
# 4) 拼接
concat_emb = torch.cat([semantic_encoded, vq_emb], dim=1) # [batch, 2048, frames]
# 5) fc_prior
concat_emb = self.fc_prior(concat_emb.transpose(1, 2)).transpose(1, 2)
# 6) decoder 的量化部分，获取code
_, vq_code, _ = self.generator(concat_emb, vq=True)
# vq_code: [batch, frames]
return vq_code
def decode_code(self, vq_code):
"""
将编码后的代码解码回音频。
参数：
vq_code: 编码后的代码 (Tensor) [batch, frames]
返回：
解码后的音频 (Tensor) [batch, waveform_length]
"""
with torch.no_grad():
# 获取量化后的嵌入
vq_post_emb = self.generator.quantizer.get_output_from_indices(vq_code.transpose(1, 2))
vq_post_emb = vq_post_emb.transpose(1, 2) # [batch, 1024, frames]
# 7) fc_post_a
vq_post_emb = self.fc_post_a(vq_post_emb.transpose(1, 2)).transpose(1, 2) # [batch, 1024, frames]
# 8) 最后解码成波形
recon_audio = self.generator(vq_post_emb.transpose(1, 2), vq=False)[0] # [batch, time]
return recon_audio
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