From 9b4e9cc8a0311e5243d69b73ed073e7ea441982e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 三月 2024 16:05:29 +0800
Subject: [PATCH] train update
---
funasr/datasets/large_datasets/utils/padding.py | 33 +++++++++++++++------------------
1 files changed, 15 insertions(+), 18 deletions(-)
diff --git a/funasr/datasets/large_datasets/utils/padding.py b/funasr/datasets/large_datasets/utils/padding.py
index b317482..26c6e84 100644
--- a/funasr/datasets/large_datasets/utils/padding.py
+++ b/funasr/datasets/large_datasets/utils/padding.py
@@ -13,15 +13,16 @@
batch = {}
data_names = data[0].keys()
for data_name in data_names:
- if data_name == "key" or data_name == "sampling_rate" or data_name == 'hotword_indxs':
+ if data_name == "key" or data_name == "sampling_rate":
continue
else:
- if data[0][data_name].dtype.kind == "i":
- pad_value = int_pad_value
- tensor_type = torch.int64
- else:
- pad_value = float_pad_value
- tensor_type = torch.float32
+ if data_name != 'hotword_indxs':
+ if data[0][data_name].dtype.kind == "i":
+ pad_value = int_pad_value
+ tensor_type = torch.int64
+ else:
+ pad_value = float_pad_value
+ tensor_type = torch.float32
tensor_list = [torch.tensor(np.copy(d[data_name]), dtype=tensor_type) for d in data]
tensor_lengths = torch.tensor([len(d[data_name]) for d in data], dtype=torch.int32)
@@ -31,7 +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
@@ -40,28 +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)
@@ -70,8 +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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