From b9837bfc73c14f74cbb4c351bb51b35f1d354ac6 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 07 四月 2023 14:37:30 +0800
Subject: [PATCH] onnx
---
funasr/export/models/target_delay_transformer.py | 97 ++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 88 insertions(+), 9 deletions(-)
diff --git a/funasr/export/models/target_delay_transformer.py b/funasr/export/models/target_delay_transformer.py
index bfe3ec4..2780d82 100644
--- a/funasr/export/models/target_delay_transformer.py
+++ b/funasr/export/models/target_delay_transformer.py
@@ -3,7 +3,12 @@
import torch
import torch.nn as nn
-class TargetDelayTransformer(nn.Module):
+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):
def __init__(
self,
@@ -23,16 +28,12 @@
self.num_embeddings = self.embed.num_embeddings
self.model_name = model_name
- # from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
- from funasr.models.encoder.sanm_encoder import SANMEncoder
- from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
-
if isinstance(model.encoder, SANMEncoder):
self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
else:
assert False, "Only support samn encode."
- def forward(self, input: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
+ def forward(self, inputs: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
"""Compute loss value from buffer sequences.
Args:
@@ -40,7 +41,7 @@
hidden (torch.Tensor): Target ids. (batch, len)
"""
- x = self.embed(input)
+ x = self.embed(inputs)
# mask = self._target_mask(input)
h, _ = self.encoder(x, text_lengths)
y = self.decoder(h)
@@ -53,14 +54,14 @@
return (text_indexes, text_lengths)
def get_input_names(self):
- return ['input', 'text_lengths']
+ return ['inputs', 'text_lengths']
def get_output_names(self):
return ['logits']
def get_dynamic_axes(self):
return {
- 'input': {
+ 'inputs': {
0: 'batch_size',
1: 'feats_length'
},
@@ -73,3 +74,81 @@
},
}
+
+class CT_Transformer_VadRealtime(nn.Module):
+
+ 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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