From 316d16bc5576932e2d88dbeab22b3405bfb49eb9 Mon Sep 17 00:00:00 2001
From: 九耳 <mengzhe.cmz@alibaba-inc.com>
Date: 星期五, 07 四月 2023 14:43:50 +0800
Subject: [PATCH] Merge branch 'dev_cmz2' of github.com:alibaba-damo-academy/FunASR into dev_cmz2
---
/dev/null | 96 ------------------------
funasr/export/test/test_onnx_punc.py | 2
funasr/export/models/target_delay_transformer.py | 97 ++++++++++++++++++++++--
funasr/export/test/test_onnx_punc_vadrealtime.py | 4
funasr/export/models/__init__.py | 8 +-
5 files changed, 95 insertions(+), 112 deletions(-)
diff --git a/funasr/export/models/__init__.py b/funasr/export/models/__init__.py
index 4ac0456..f81ff64 100644
--- a/funasr/export/models/__init__.py
+++ b/funasr/export/models/__init__.py
@@ -4,10 +4,10 @@
from funasr.models.e2e_vad import E2EVadModel
from funasr.export.models.e2e_vad import E2EVadModel as E2EVadModel_export
from funasr.models.target_delay_transformer import TargetDelayTransformer
-from funasr.export.models.target_delay_transformer import TargetDelayTransformer as TargetDelayTransformer_export
+from funasr.export.models.target_delay_transformer import CT_Transformer as CT_Transformer_export
from funasr.train.abs_model import PunctuationModel
from funasr.models.vad_realtime_transformer import VadRealtimeTransformer
-from funasr.export.models.vad_realtime_transformer import VadRealtimeTransformer as VadRealtimeTransformer_export
+from funasr.export.models.target_delay_transformer import CT_Transformer_VadRealtime as CT_Transformer_VadRealtime_export
def get_model(model, export_config=None):
if isinstance(model, BiCifParaformer):
@@ -18,8 +18,8 @@
return E2EVadModel_export(model, **export_config)
elif isinstance(model, PunctuationModel):
if isinstance(model.punc_model, TargetDelayTransformer):
- return TargetDelayTransformer_export(model.punc_model, **export_config)
+ return CT_Transformer_export(model.punc_model, **export_config)
elif isinstance(model.punc_model, VadRealtimeTransformer):
- return VadRealtimeTransformer_export(model.punc_model, **export_config)
+ return CT_Transformer_VadRealtime_export(model.punc_model, **export_config)
else:
raise "Funasr does not support the given model type currently."
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'
+ },
+ }
diff --git a/funasr/export/models/vad_realtime_transformer.py b/funasr/export/models/vad_realtime_transformer.py
deleted file mode 100644
index 24a8e72..0000000
--- a/funasr/export/models/vad_realtime_transformer.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from typing import Tuple
-
-import torch
-import torch.nn as nn
-
-from funasr.models.encoder.sanm_encoder import SANMVadEncoder
-from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
-
-class VadRealtimeTransformer(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.encoder = model.encoder
- self.decoder = model.decoder
- self.model_name = model_name
-
-
-
- def forward(self, input: 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(input)
- # 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_dummy_inputs(self, txt_dir=None):
- from funasr.modules.mask import vad_mask
- length = 10
- text_indexes = torch.tensor([[266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757]], dtype=torch.int32)
- text_lengths = torch.tensor([length], dtype=torch.int32)
- vad_masks = vad_mask(10, 2, 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_masks, sub_masks[None, None, :, :])
-
- def get_input_names(self):
- return ['input', 'text_lengths', 'vad_masks', 'sub_masks']
-
- def get_output_names(self):
- return ['logits']
-
- def get_dynamic_axes(self):
- return {
- 'input': {
- 1: 'feats_length'
- },
- 'vad_masks': {
- 2: 'feats_length1',
- 3: 'feats_length2'
- },
- 'sub_masks': {
- 2: 'feats_length1',
- 3: 'feats_length2'
- },
- 'logits': {
- 1: 'logits_length'
- },
- }
diff --git a/funasr/export/test/test_onnx_punc.py b/funasr/export/test/test_onnx_punc.py
index 62689a9..39f85f4 100644
--- a/funasr/export/test/test_onnx_punc.py
+++ b/funasr/export/test/test_onnx_punc.py
@@ -9,7 +9,7 @@
output_name = [nd.name for nd in sess.get_outputs()]
def _get_feed_dict(text_length):
- return {'input': np.ones((1, text_length), dtype=np.int64), 'text_lengths': np.array([text_length,], dtype=np.int32)}
+ return {'inputs': np.ones((1, text_length), dtype=np.int64), 'text_lengths': np.array([text_length,], dtype=np.int32)}
def _run(feed_dict):
output = sess.run(output_name, input_feed=feed_dict)
diff --git a/funasr/export/test/test_onnx_punc_vadrealtime.py b/funasr/export/test/test_onnx_punc_vadrealtime.py
index 54f85f1..86be026 100644
--- a/funasr/export/test/test_onnx_punc_vadrealtime.py
+++ b/funasr/export/test/test_onnx_punc_vadrealtime.py
@@ -9,9 +9,9 @@
output_name = [nd.name for nd in sess.get_outputs()]
def _get_feed_dict(text_length):
- return {'input': np.ones((1, text_length), dtype=np.int64),
+ return {'inputs': np.ones((1, text_length), dtype=np.int64),
'text_lengths': np.array([text_length,], dtype=np.int32),
- 'vad_mask': np.ones((1, 1, text_length, text_length), dtype=np.float32),
+ 'vad_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32),
'sub_masks': np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
}
--
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