From 08114ae27d85949106aeab03b3fa5d764d100b33 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 14 六月 2024 15:16:40 +0800
Subject: [PATCH] decoding
---
funasr/datasets/openai_datasets/datasets.py | 24 ++++++++++++++++++------
1 files changed, 18 insertions(+), 6 deletions(-)
diff --git a/funasr/datasets/openai_datasets/datasets.py b/funasr/datasets/openai_datasets/datasets.py
index 6307930..3c2a957 100644
--- a/funasr/datasets/openai_datasets/datasets.py
+++ b/funasr/datasets/openai_datasets/datasets.py
@@ -64,6 +64,8 @@
self.max_token_length = kwargs.get("max_token_length", 1024)
self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5)
self.batch_size_token_max = kwargs.get("batch_size_token_max", 2500)
+ self.audio_adaptor_downsample_rate = kwargs.get("audio_adaptor_downsample_rate", 2)
+ self.audio_encoder_downsample_rate = kwargs.get("audio_encoder_downsample_rate", 4)
def get_source_len(self, index):
item = self.index_ds[index]
@@ -136,10 +138,13 @@
speech = speech.permute(0, 2, 1)
# if speech_lengths > self.batch_size:
# continue
+ if self.audio_encoder_downsample_rate == 4:
+ olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
+ olens = 1 + (olens - 3 + 2 * 1) // 2
+ elif self.audio_encoder_downsample_rate == 1:
+ olens = speech_lengths[0].item()
- olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
- olens = 1 + (olens - 3 + 2 * 1) // 2
- sub_token_len = (olens - 1) // 2 + 1
+ sub_token_len = (olens - 1) // self.audio_adaptor_downsample_rate + 1
sub_token = [0] * sub_token_len
fbank_beg_i = [len(source_ids)]
source_ids += sub_token
@@ -329,6 +334,7 @@
splits = self.pattern.split(source_input)
source_ids = []
+ fbank_i = []
fbank_mask_i = []
fbank_beg_i = []
fbank_lens_i = []
@@ -376,8 +382,11 @@
target_ids = self.tokenizer.encode(target_out)
input_ids += source_ids + target_ids
labels += source_mask + target_ids
+ fbank.append(speech[0, :, :])
fbank_mask += fbank_mask_i
- fbank_beg.append(fbank_beg_i)
+ if len(fbank_beg_i) < 1:
+ fbank_beg_i = [-1]
+ fbank_beg += fbank_beg_i
if len(input_ids) > self.max_token_length:
logging.info(
@@ -390,7 +399,7 @@
attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
- fbank = speech[0, :, :]
+ # fbank = speech[0, :, :]
fbank_lens = speech_lengths
fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
@@ -420,7 +429,10 @@
for key in sample.keys():
if key not in outputs:
outputs[key] = []
- outputs[key].append(sample[key])
+ if isinstance(sample[key], (list, tuple)):
+ outputs[key].extend(sample[key])
+ else:
+ outputs[key].append(sample[key])
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
--
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