WhisperX: 带时间戳的自动语音识别及说话人分离

import torch
import torch.nn as nn
from transformers import Wav2Vec2Model, Wav2Vec2Processor
 
# 定义一个带时间戳的自注意力模块
class TimestampAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout):
        super(TimestampAttention, self).__init__()
        self.attention = nn.MultiheadAttention(hidden_size, num_heads, dropout)
        self.linear_timestamp = nn.Linear(hidden_size, hidden_size)
    
    def forward(self, input, timestamp):
        # 将时间戳线性变换并重复到与输入特征相同的维度
        timestamp_proj = self.linear_timestamp(timestamp).repeat(1, 1, 1)
        # 将时间戳特征与输入特征进行拼接
        attention_input = torch.cat((input, timestamp_proj), dim=-1)
        # 执行自注意力操作
        outputs = self.attention(attention_input, attention_input, attention_input)[0]
        return outputs
 
# 示例：使用TimestampAttention模块
model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base")
 
# 假设input_features是一个已处理的特征张量，timestamps是对应的时间戳张量
input_features = torch.randn(10, 512)  # 示例特征形状：(batch_size, sequence_length, feature_dim)
timestamps = torch.randn(10, 32)  # 示例时间戳形状：(batch_size, timestamp_dim)
 
timestamp_attn_layer = TimestampAttention(hidden_size=model.config.hidden_size, num_heads=model.config.num_attention_heads, dropout=model.config.attention_dropout)
 
# 应用带时间戳的自注意力
encoded_input = timestamp_attn_layer(input_features, timestamps)
 
# 使用Wav2Vec2模型的其余部分进行编码
encoded_output = model(inputs=encoded_input.transpose(1, 2)).last_hidden_state

这个代码示例展示了如何定义一个带时间戳的自注意力模块，并将其应用于Wav2Vec2模型的特征编码过程。这里的TimestampAttention类接收输入特征和时间戳作为输入，并返回带有时间戳信息的编码特征。这种方法可以增强说话人识别和语音识别的性能。