请帮我用Bert和pytorch写一个用于音频文件的Encoder神经网络代码

我可以回答这个问题。以下是一个使用Bert和pytorch编写的音频文件编码器神经网络的示例代码：

import torch
from transformers import BertModel, BertTokenizer

class AudioEncoder(torch.nn.Module):
    def __init__(self):
        super(AudioEncoder, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

    def forward(self, audio_file):
        # Convert audio file to text using speech-to-text API
        text = convert_audio_to_text(audio_file)

        # Tokenize text using BERT tokenizer
        input_ids = torch.tensor([self.tokenizer.encode(text, add_special_tokens=True)])

        # Pass input_ids through BERT model to get embeddings
        outputs = self.bert(input_ids)
        embeddings = outputs[0]

        # Average embeddings to get final audio encoding
        encoding = torch.mean(embeddings, dim=1)

        return encoding

请注意，这只是一个示例代码，您需要根据您的具体需求进行修改和调整。