From e5528b586d70e0ba51a4de3a9c7cb717f6b79847 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期二, 21 十一月 2023 10:59:12 +0800
Subject: [PATCH] update dataloader
---
funasr/datasets/large_datasets/utils/padding.py | 18 +++++++-----------
1 files changed, 7 insertions(+), 11 deletions(-)
diff --git a/funasr/datasets/large_datasets/utils/padding.py b/funasr/datasets/large_datasets/utils/padding.py
index 20ba7a3..26c6e84 100644
--- a/funasr/datasets/large_datasets/utils/padding.py
+++ b/funasr/datasets/large_datasets/utils/padding.py
@@ -32,7 +32,7 @@
batch[data_name] = tensor_pad
batch[data_name + "_lengths"] = tensor_lengths
- # DHA, EAHC NOT INCLUDED
+ # SAC LABEL INCLUDE
if "hotword_indxs" in batch:
# if hotword indxs in batch
# use it to slice hotwords out
@@ -41,28 +41,25 @@
text = batch['text']
text_lengths = batch['text_lengths']
hotword_indxs = batch['hotword_indxs']
- num_hw = sum([int(i) for i in batch['hotword_indxs_lengths'] if i != 1]) // 2
- B, t1 = text.shape
+ dha_pad = torch.ones_like(text) * -1
+ _, t1 = text.shape
t1 += 1 # TODO: as parameter which is same as predictor_bias
- ideal_attn = torch.zeros(B, t1, num_hw+1)
nth_hw = 0
for b, (hotword_indx, one_text, length) in enumerate(zip(hotword_indxs, text, text_lengths)):
- ideal_attn[b][:,-1] = 1
+ dha_pad[b][:length] = 8405
if hotword_indx[0] != -1:
start, end = int(hotword_indx[0]), int(hotword_indx[1])
hotword = one_text[start: end+1]
hotword_list.append(hotword)
hotword_lengths.append(end-start+1)
- ideal_attn[b][start:end+1, nth_hw] = 1
- ideal_attn[b][start:end+1, -1] = 0
+ dha_pad[b][start: end+1] = one_text[start: end+1]
nth_hw += 1
if len(hotword_indx) == 4 and hotword_indx[2] != -1:
# the second hotword if exist
start, end = int(hotword_indx[2]), int(hotword_indx[3])
hotword_list.append(one_text[start: end+1])
hotword_lengths.append(end-start+1)
- ideal_attn[b][start:end+1, nth_hw-1] = 1
- ideal_attn[b][start:end+1, -1] = 0
+ dha_pad[b][start: end+1] = one_text[start: end+1]
nth_hw += 1
hotword_list.append(torch.tensor([1]))
hotword_lengths.append(1)
@@ -71,8 +68,7 @@
padding_value=0)
batch["hotword_pad"] = hotword_pad
batch["hotword_lengths"] = torch.tensor(hotword_lengths, dtype=torch.int32)
- batch['ideal_attn'] = ideal_attn
+ batch['dha_pad'] = dha_pad
del batch['hotword_indxs']
del batch['hotword_indxs_lengths']
-
return keys, batch
--
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