From 172e7ac986f299ad545cbd91a8cecc3ef967af36 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 十二月 2023 10:17:22 +0800
Subject: [PATCH] Revert "Dev gzf funasr2" (#1164)
---
funasr/models/e2e_asr_paraformer.py | 7 +------
1 files changed, 1 insertions(+), 6 deletions(-)
diff --git a/funasr/models/e2e_asr_paraformer.py b/funasr/models/e2e_asr_paraformer.py
index 6b1d824..0e0b95b 100644
--- a/funasr/models/e2e_asr_paraformer.py
+++ b/funasr/models/e2e_asr_paraformer.py
@@ -256,7 +256,6 @@
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + self.predictor_bias).sum())
-
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -869,7 +868,6 @@
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + self.predictor_bias).sum())
-
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -1497,7 +1495,6 @@
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + self.predictor_bias).sum())
-
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -1769,7 +1766,6 @@
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + self.predictor_bias).sum())
-
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -1972,7 +1968,6 @@
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + self.predictor_bias).sum())
-
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
@@ -2267,4 +2262,4 @@
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
var_dict_tf[name_tf].shape))
- return var_dict_torch_update
\ No newline at end of file
+ return var_dict_torch_update
--
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