From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
funasr/models/llm_asr/adaptor.py | 33 +++++++++++++++++++++++++++++++++
1 files changed, 33 insertions(+), 0 deletions(-)
diff --git a/funasr/models/llm_asr/adaptor.py b/funasr/models/llm_asr/adaptor.py
index 0676e7d..2093588 100644
--- a/funasr/models/llm_asr/adaptor.py
+++ b/funasr/models/llm_asr/adaptor.py
@@ -27,3 +27,36 @@
x = self.relu(x)
x = self.linear2(x)
return x
+
+@tables.register("adaptor_classes", "QFormer")
+class EncoderProjectorQFormer(nn.Module):
+ def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
+ super().__init__()
+ self.encoder_dim = encoder_dim
+ self.llm_dim = llm_dim
+ from transformers import Blip2QFormerConfig, Blip2QFormerModel
+ configuration = Blip2QFormerConfig()
+ configuration.encoder_hidden_size = self.encoder_dim
+ configuration.num_hidden_layers = 2
+
+ self.query_len = 64
+ self.query = nn.Parameter(torch.zeros(1, self.query_len, configuration.hidden_size))
+ self.query.data.normal_(mean=0.0, std=1.0)
+ self.qformer = Blip2QFormerModel(configuration)
+
+ self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
+ self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)
+
+ def forward(self, x, atts):
+ query = self.query.expand(x.shape[0], -1, -1)
+
+ query_output = self.qformer(
+ query_embeds=query,
+ encoder_hidden_states=x,
+ encoder_attention_mask=atts,
+ return_dict=True,
+ )
+
+ query_proj = self.norm(self.linear(query_output.last_hidden_state))
+
+ return query_proj
\ No newline at end of file
--
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