From a49f2c6411637d696e787605ec611f05667e8935 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期四, 28 九月 2023 15:52:14 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
funasr/models/encoder/sanm_encoder.py | 72 ++++++++++++++++++++++++-----------
1 files changed, 49 insertions(+), 23 deletions(-)
diff --git a/funasr/models/encoder/sanm_encoder.py b/funasr/models/encoder/sanm_encoder.py
index 9e27d4a..c15343e 100644
--- a/funasr/models/encoder/sanm_encoder.py
+++ b/funasr/models/encoder/sanm_encoder.py
@@ -114,8 +114,44 @@
if not self.normalize_before:
x = self.norm2(x)
-
return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
+
+ def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
+ """Compute encoded features.
+
+ Args:
+ x_input (torch.Tensor): Input tensor (#batch, time, size).
+ mask (torch.Tensor): Mask tensor for the input (#batch, time).
+ cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time).
+
+ """
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm1(x)
+
+ if self.in_size == self.size:
+ attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
+ x = residual + attn
+ else:
+ x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
+
+ if not self.normalize_before:
+ x = self.norm1(x)
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm2(x)
+ x = residual + self.feed_forward(x)
+ if not self.normalize_before:
+ x = self.norm2(x)
+
+ return x, cache
+
class SANMEncoder(AbsEncoder):
"""
@@ -841,7 +877,6 @@
xs_pad: torch.Tensor,
ilens: torch.Tensor,
cache: dict = None,
- ctc: CTC = None,
):
xs_pad *= self.output_size() ** 0.5
if self.embed is None:
@@ -852,34 +887,25 @@
xs_pad = to_device(cache["feats"], device=xs_pad.device)
else:
xs_pad = self._add_overlap_chunk(xs_pad, cache)
- encoder_outs = self.encoders0(xs_pad, None, None, None, None)
- xs_pad, masks = encoder_outs[0], encoder_outs[1]
- intermediate_outs = []
- if len(self.interctc_layer_idx) == 0:
- encoder_outs = self.encoders(xs_pad, None, None, None, None)
- xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ if cache["opt"] is None:
+ cache_layer_num = len(self.encoders0) + len(self.encoders)
+ new_cache = [None] * cache_layer_num
else:
- for layer_idx, encoder_layer in enumerate(self.encoders):
- encoder_outs = encoder_layer(xs_pad, None, None, None, None)
- xs_pad, masks = encoder_outs[0], encoder_outs[1]
- if layer_idx + 1 in self.interctc_layer_idx:
- encoder_out = xs_pad
+ new_cache = cache["opt"]
- # intermediate outputs are also normalized
- if self.normalize_before:
- encoder_out = self.after_norm(encoder_out)
+ for layer_idx, encoder_layer in enumerate(self.encoders0):
+ encoder_outs = encoder_layer.forward_chunk(xs_pad, new_cache[layer_idx], cache["chunk_size"], cache["encoder_chunk_look_back"])
+ xs_pad, new_cache[0] = encoder_outs[0], encoder_outs[1]
- intermediate_outs.append((layer_idx + 1, encoder_out))
-
- if self.interctc_use_conditioning:
- ctc_out = ctc.softmax(encoder_out)
- xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ encoder_outs = encoder_layer.forward_chunk(xs_pad, new_cache[layer_idx+len(self.encoders0)], cache["chunk_size"], cache["encoder_chunk_look_back"])
+ xs_pad, new_cache[layer_idx+len(self.encoders0)] = encoder_outs[0], encoder_outs[1]
if self.normalize_before:
xs_pad = self.after_norm(xs_pad)
+ if cache["encoder_chunk_look_back"] > 0 or cache["encoder_chunk_look_back"] == -1:
+ cache["opt"] = new_cache
- if len(intermediate_outs) > 0:
- return (xs_pad, intermediate_outs), None, None
return xs_pad, ilens, None
def gen_tf2torch_map_dict(self):
--
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