From 2b066390187357ac75c61866992495b4180e5a8b Mon Sep 17 00:00:00 2001
From: mengzhe.cmz <mengzhe.cmz@alibaba-inc.com>
Date: 星期四, 13 四月 2023 13:46:33 +0800
Subject: [PATCH] change name
---
funasr/export/models/CT_Transformer.py | 162 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 162 insertions(+), 0 deletions(-)
diff --git a/funasr/export/models/CT_Transformer.py b/funasr/export/models/CT_Transformer.py
new file mode 100644
index 0000000..ea6ff4f
--- /dev/null
+++ b/funasr/export/models/CT_Transformer.py
@@ -0,0 +1,162 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+from funasr.models.encoder.sanm_encoder import SANMEncoder
+from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
+from funasr.models.encoder.sanm_encoder import SANMVadEncoder
+from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
+
+class CT_Transformer(nn.Module):
+ """
+ Author: Speech Lab, Alibaba Group, China
+ CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
+ https://arxiv.org/pdf/2003.01309.pdf
+ """
+ def __init__(
+ self,
+ model,
+ max_seq_len=512,
+ model_name='punc_model',
+ **kwargs,
+ ):
+ super().__init__()
+ onnx = False
+ if "onnx" in kwargs:
+ onnx = kwargs["onnx"]
+ self.embed = model.embed
+ self.decoder = model.decoder
+ # self.model = model
+ self.feats_dim = self.embed.embedding_dim
+ self.num_embeddings = self.embed.num_embeddings
+ self.model_name = model_name
+
+ if isinstance(model.encoder, SANMEncoder):
+ self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
+ else:
+ assert False, "Only support samn encode."
+
+ def forward(self, inputs: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
+ """Compute loss value from buffer sequences.
+
+ Args:
+ input (torch.Tensor): Input ids. (batch, len)
+ hidden (torch.Tensor): Target ids. (batch, len)
+
+ """
+ x = self.embed(inputs)
+ # mask = self._target_mask(input)
+ h, _ = self.encoder(x, text_lengths)
+ y = self.decoder(h)
+ return y
+
+ def get_dummy_inputs(self):
+ length = 120
+ text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length))
+ text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
+ return (text_indexes, text_lengths)
+
+ def get_input_names(self):
+ return ['inputs', 'text_lengths']
+
+ def get_output_names(self):
+ return ['logits']
+
+ def get_dynamic_axes(self):
+ return {
+ 'inputs': {
+ 0: 'batch_size',
+ 1: 'feats_length'
+ },
+ 'text_lengths': {
+ 0: 'batch_size',
+ },
+ 'logits': {
+ 0: 'batch_size',
+ 1: 'logits_length'
+ },
+ }
+
+
+class CT_Transformer_VadRealtime(nn.Module):
+ """
+ Author: Speech Lab, Alibaba Group, China
+ CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
+ https://arxiv.org/pdf/2003.01309.pdf
+ """
+ def __init__(
+ self,
+ model,
+ max_seq_len=512,
+ model_name='punc_model',
+ **kwargs,
+ ):
+ super().__init__()
+ onnx = False
+ if "onnx" in kwargs:
+ onnx = kwargs["onnx"]
+
+ self.embed = model.embed
+ if isinstance(model.encoder, SANMVadEncoder):
+ self.encoder = SANMVadEncoder_export(model.encoder, onnx=onnx)
+ else:
+ assert False, "Only support samn encode."
+ self.decoder = model.decoder
+ self.model_name = model_name
+
+
+
+ def forward(self, inputs: torch.Tensor,
+ text_lengths: torch.Tensor,
+ vad_indexes: torch.Tensor,
+ sub_masks: torch.Tensor,
+ ) -> Tuple[torch.Tensor, None]:
+ """Compute loss value from buffer sequences.
+
+ Args:
+ input (torch.Tensor): Input ids. (batch, len)
+ hidden (torch.Tensor): Target ids. (batch, len)
+
+ """
+ x = self.embed(inputs)
+ # mask = self._target_mask(input)
+ h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
+ y = self.decoder(h)
+ return y
+
+ def with_vad(self):
+ return True
+
+ def get_dummy_inputs(self):
+ length = 120
+ text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length))
+ text_lengths = torch.tensor([length], dtype=torch.int32)
+ vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
+ sub_masks = torch.ones(length, length, dtype=torch.float32)
+ sub_masks = torch.tril(sub_masks).type(torch.float32)
+ return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
+
+ def get_input_names(self):
+ return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']
+
+ def get_output_names(self):
+ return ['logits']
+
+ def get_dynamic_axes(self):
+ return {
+ 'inputs': {
+ 1: 'feats_length'
+ },
+ 'vad_masks': {
+ 2: 'feats_length1',
+ 3: 'feats_length2'
+ },
+ 'sub_masks': {
+ 2: 'feats_length1',
+ 3: 'feats_length2'
+ },
+ 'logits': {
+ 1: 'logits_length'
+ },
+ }
--
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