From 0ba1bdd476c2079f1220904d5f2a217d78bdb64a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 07 六月 2024 03:21:53 +0800
Subject: [PATCH] auto frontend
---
funasr/datasets/openai_datasets/datasets.py | 76 +++++++++++++++++---------------------
1 files changed, 34 insertions(+), 42 deletions(-)
diff --git a/funasr/datasets/openai_datasets/datasets.py b/funasr/datasets/openai_datasets/datasets.py
index f6127b6..8cb0926 100644
--- a/funasr/datasets/openai_datasets/datasets.py
+++ b/funasr/datasets/openai_datasets/datasets.py
@@ -180,51 +180,43 @@
return output
def collator(self, samples: list = None):
- outputs = {}
- for sample in samples:
- if sample is None:
- continue
- for key in sample.keys():
- if key not in outputs:
- outputs[key] = []
- outputs[key].append(sample[key])
- for key, data_list in outputs.items():
- if isinstance(data_list[0], torch.Tensor):
- if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+ for idx in range(self.retry):
+ badcase_flag = False
- pad_value = self.int_pad_value
- else:
- pad_value = self.float_pad_value
+ outputs = {}
+ for sample in samples:
+ if sample is None:
+ continue
+ for key in sample.keys():
+ if key not in outputs:
+ outputs[key] = []
+ outputs[key].append(sample[key])
- outputs[key] = torch.nn.utils.rnn.pad_sequence(
- data_list, batch_first=True, padding_value=pad_value
- )
-
- if self.batch_type != "example":
- for i in range(10):
- outputs = self._filter_badcase(outputs, i=i)
-
- return outputs
-
- def _filter_badcase(self, outputs, i=0):
- b, t = outputs["input_ids"].shape
-
- if b * t > self.batch_size * 2:
- beg = torch.randint(0, 2, ()).item()
- if b < 2:
- beg = 0
- logging.info(
- f"Warning, b * t: {b * t} > {self.batch_size}, b: {b}, t: {t}, drop half data {i}th, beg:{beg}"
- )
for key, data_list in outputs.items():
- outputs[key] = outputs[key][beg : beg + b : 2]
- #
- # speech_lengths_max = outputs["speech_lengths"].max().item()
- # outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
- # text_lengths_max = outputs["text_lengths"].max().item()
- # outputs["text"] = outputs["text"][:, :text_lengths_max]
- # target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
- # outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
+ if isinstance(data_list[0], torch.Tensor):
+ if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+
+ pad_value = self.int_pad_value
+ else:
+ pad_value = self.float_pad_value
+
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(
+ data_list, batch_first=True, padding_value=pad_value
+ )
+
+ if self.batch_type != "example":
+ b, t = outputs["input_ids"].shape
+ if b * t > self.batch_size * 2:
+ beg = torch.randint(0, 2, ()).item()
+ if b < 2:
+ beg = 0
+ logging.info(
+ f"Warning, b * t: {b * t} > {self.batch_size}, b: {b}, t: {t}, drop half data {idx}th, beg:{beg}"
+ )
+ samples = samples[beg : beg + b : 2]
+ continue
+
+ break
return outputs
--
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