From 4ba1011b42e041ee1d71448eefd7ef2e7bd61bb6 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 31 三月 2023 15:31:26 +0800
Subject: [PATCH] export
---
funasr/export/models/vad_realtime_transformer.py | 45 ++++++++++++++++++++++++++-------------------
1 files changed, 26 insertions(+), 19 deletions(-)
diff --git a/funasr/export/models/vad_realtime_transformer.py b/funasr/export/models/vad_realtime_transformer.py
index 44583d8..693b9c8 100644
--- a/funasr/export/models/vad_realtime_transformer.py
+++ b/funasr/export/models/vad_realtime_transformer.py
@@ -1,17 +1,12 @@
-from typing import Any
-from typing import List
from typing import Tuple
import torch
import torch.nn as nn
-from funasr.modules.embedding import SinusoidalPositionEncoder
-from funasr.punctuation.sanm_encoder import SANMVadEncoder as Encoder
-from funasr.punctuation.abs_model import AbsPunctuation
-from funasr.punctuation.sanm_encoder import SANMVadEncoder
+from funasr.models.encoder.sanm_encoder import SANMVadEncoder
from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
-class VadRealtimeTransformer(AbsPunctuation):
+class VadRealtimeTransformer(nn.Module):
def __init__(
self,
@@ -21,7 +16,9 @@
**kwargs,
):
super().__init__()
-
+ onnx = False
+ if "onnx" in kwargs:
+ onnx = kwargs["onnx"]
self.embed = model.embed
if isinstance(model.encoder, SANMVadEncoder):
@@ -30,11 +27,15 @@
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) -> Tuple[torch.Tensor, None]:
+ 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:
@@ -44,7 +45,7 @@
"""
x = self.embed(input)
# mask = self._target_mask(input)
- h, _, _ = self.encoder(x, text_lengths, vad_indexes)
+ h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
y = self.decoder(h)
return y
@@ -53,12 +54,15 @@
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)
+ 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 ['input', 'text_lengths']
+ return ['input', 'text_lengths', 'vad_mask', 'sub_masks']
def get_output_names(self):
return ['logits']
@@ -66,14 +70,17 @@
def get_dynamic_axes(self):
return {
'input': {
- 0: 'batch_size',
1: 'feats_length'
},
- 'text_lengths': {
- 0: 'batch_size',
+ 'vad_mask': {
+ 2: 'feats_length1',
+ 3: 'feats_length2'
+ },
+ 'sub_masks': {
+ 2: 'feats_length1',
+ 3: 'feats_length2'
},
'logits': {
- 0: 'batch_size',
1: 'logits_length'
},
}
--
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