From bc723ea200144bd6fa8a5dff4b9a780feda144fc Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 29 六月 2023 18:55:01 +0800
Subject: [PATCH] dcos
---
funasr/models/decoder/sanm_decoder.py | 28 +++++++++++++++-------------
1 files changed, 15 insertions(+), 13 deletions(-)
diff --git a/funasr/models/decoder/sanm_decoder.py b/funasr/models/decoder/sanm_decoder.py
index a36d95e..d83f89f 100644
--- a/funasr/models/decoder/sanm_decoder.py
+++ b/funasr/models/decoder/sanm_decoder.py
@@ -7,7 +7,6 @@
from funasr.modules.streaming_utils import utils as myutils
from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
-from typeguard import check_argument_types
from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.modules.embedding import PositionalEncoding
@@ -94,7 +93,7 @@
if self.self_attn:
if self.normalize_before:
tgt = self.norm2(tgt)
- x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
+ x, _ = self.self_attn(tgt, tgt_mask)
x = residual + self.dropout(x)
if self.src_attn is not None:
@@ -151,7 +150,7 @@
class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
"""
- author: Speech Lab, Alibaba Group, China
+ Author: Speech Lab of DAMO Academy, Alibaba Group
SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
https://arxiv.org/abs/2006.01713
@@ -181,7 +180,6 @@
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
embed_tensor_name_prefix_tf: str = None,
):
- assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -399,7 +397,7 @@
for i in range(self.att_layer_num):
decoder = self.decoders[i]
c = cache[i]
- x, tgt_mask, memory, memory_mask, c_ret = decoder(
+ x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
x, tgt_mask, memory, memory_mask, cache=c
)
new_cache.append(c_ret)
@@ -409,13 +407,13 @@
j = i + self.att_layer_num
decoder = self.decoders2[i]
c = cache[j]
- x, tgt_mask, memory, memory_mask, c_ret = decoder(
+ x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
x, tgt_mask, memory, memory_mask, cache=c
)
new_cache.append(c_ret)
for decoder in self.decoders3:
- x, tgt_mask, memory, memory_mask, _ = decoder(
+ x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
x, tgt_mask, memory, None, cache=None
)
@@ -812,7 +810,7 @@
class ParaformerSANMDecoder(BaseTransformerDecoder):
"""
- author: Speech Lab, Alibaba Group, China
+ Author: Speech Lab of DAMO Academy, Alibaba Group
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
https://arxiv.org/abs/2006.01713
"""
@@ -838,7 +836,6 @@
tf2torch_tensor_name_prefix_torch: str = "decoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
):
- assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -935,6 +932,7 @@
hlens: torch.Tensor,
ys_in_pad: torch.Tensor,
ys_in_lens: torch.Tensor,
+ chunk_mask: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward decoder.
@@ -955,9 +953,13 @@
"""
tgt = ys_in_pad
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
-
+
memory = hs_pad
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
+ if chunk_mask is not None:
+ memory_mask = memory_mask * chunk_mask
+ if tgt_mask.size(1) != memory_mask.size(1):
+ memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)
x = tgt
x, tgt_mask, memory, memory_mask, _ = self.decoders(
@@ -1076,7 +1078,7 @@
for i in range(self.att_layer_num):
decoder = self.decoders[i]
c = cache[i]
- x, tgt_mask, memory, memory_mask, c_ret = decoder(
+ x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
x, tgt_mask, memory, None, cache=c
)
new_cache.append(c_ret)
@@ -1086,14 +1088,14 @@
j = i + self.att_layer_num
decoder = self.decoders2[i]
c = cache[j]
- x, tgt_mask, memory, memory_mask, c_ret = decoder(
+ x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
x, tgt_mask, memory, None, cache=c
)
new_cache.append(c_ret)
for decoder in self.decoders3:
- x, tgt_mask, memory, memory_mask, _ = decoder(
+ x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
x, tgt_mask, memory, None, cache=None
)
--
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