From fc9595625855be5b63f86a38ac785e49c142c0ae Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期二, 21 三月 2023 14:10:03 +0800
Subject: [PATCH] embed debug
---
funasr/models_transducer/encoder/encoder.py | 12 +++++++-----
funasr/models_transducer/espnet_transducer_model_unified.py | 3 +--
funasr/models_transducer/encoder/blocks/conv_input.py | 15 ++++++++-------
3 files changed, 16 insertions(+), 14 deletions(-)
diff --git a/funasr/models_transducer/encoder/blocks/conv_input.py b/funasr/models_transducer/encoder/blocks/conv_input.py
index c68c73b..ffec93e 100644
--- a/funasr/models_transducer/encoder/blocks/conv_input.py
+++ b/funasr/models_transducer/encoder/blocks/conv_input.py
@@ -146,30 +146,31 @@
if mask is not None:
mask = self.create_new_mask(mask)
olens = max(mask.eq(0).sum(1))
-
- b, t_input, f = x.size()
+
+ b, t, f = x.size()
x = x.unsqueeze(1) # (b. 1. t. f)
+
if chunk_size is not None:
max_input_length = int(
- chunk_size * self.subsampling_factor * (math.ceil(float(t_input) / (chunk_size * self.subsampling_factor) ))
+ chunk_size * self.subsampling_factor * (math.ceil(float(t) / (chunk_size * self.subsampling_factor) ))
)
x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x)
x = list(x)
x = torch.stack(x, dim=0)
N_chunks = max_input_length // ( chunk_size * self.subsampling_factor)
x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f)
+
x = self.conv(x)
- _, c, t, f = x.size()
-
+ _, c, _, f = x.size()
if chunk_size is not None:
x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:,:olens,:]
else:
- x = x.transpose(1, 2).contiguous().view(b, t, c * f)
+ x = x.transpose(1, 2).contiguous().view(b, -1, c * f)
if self.output is not None:
x = self.output(x)
-
+
return x, mask[:,:olens][:,:x.size(1)]
def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor:
diff --git a/funasr/models_transducer/encoder/encoder.py b/funasr/models_transducer/encoder/encoder.py
index 45c99c1..b486a11 100644
--- a/funasr/models_transducer/encoder/encoder.py
+++ b/funasr/models_transducer/encoder/encoder.py
@@ -134,14 +134,11 @@
)
mask = make_source_mask(x_len)
- if self.unified_model_training:
- x, mask = self.embed(x, mask, self.default_chunk_size)
- else:
- x, mask = self.embed(x, mask)
- pos_enc = self.pos_enc(x)
if self.unified_model_training:
chunk_size = self.default_chunk_size + torch.randint(-self.jitter_range, self.jitter_range+1, (1,)).item()
+ x, mask = self.embed(x, mask, chunk_size)
+ pos_enc = self.pos_enc(x)
chunk_mask = make_chunk_mask(
x.size(1),
chunk_size,
@@ -178,6 +175,9 @@
else:
chunk_size = (chunk_size % self.short_chunk_size) + 1
+ x, mask = self.embed(x, mask, chunk_size)
+ pos_enc = self.pos_enc(x)
+
chunk_mask = make_chunk_mask(
x.size(1),
chunk_size,
@@ -185,6 +185,8 @@
device=x.device,
)
else:
+ x, mask = self.embed(x, mask, None)
+ pos_enc = self.pos_enc(x)
chunk_mask = None
x = self.encoders(
x,
diff --git a/funasr/models_transducer/espnet_transducer_model_unified.py b/funasr/models_transducer/espnet_transducer_model_unified.py
index 6df86f8..be61e83 100644
--- a/funasr/models_transducer/espnet_transducer_model_unified.py
+++ b/funasr/models_transducer/espnet_transducer_model_unified.py
@@ -455,8 +455,7 @@
gather=True,
)
- #if not self.training and (self.report_cer or self.report_wer):
- if self.report_cer or self.report_wer:
+ if not self.training and (self.report_cer or self.report_wer):
if self.error_calculator is None:
self.error_calculator = ErrorCalculator(
self.decoder,
--
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