From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/datasets/audio_datasets/datasets.py | 33 ++++++++++++++++++++-------------
1 files changed, 20 insertions(+), 13 deletions(-)
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index 5af33fc..ab08fb0 100644
--- a/funasr/datasets/audio_datasets/datasets.py
+++ b/funasr/datasets/audio_datasets/datasets.py
@@ -19,15 +19,15 @@
**kwargs):
super().__init__()
index_ds_class = tables.index_ds_classes.get(index_ds)
- self.index_ds = index_ds_class(path)
+ self.index_ds = index_ds_class(path, **kwargs)
preprocessor_speech = kwargs.get("preprocessor_speech", None)
if preprocessor_speech:
- preprocessor_speech_class = tables.preprocessor_speech_classes.get(preprocessor_speech)
+ preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
preprocessor_speech = preprocessor_speech_class(**kwargs.get("preprocessor_speech_conf"))
self.preprocessor_speech = preprocessor_speech
preprocessor_text = kwargs.get("preprocessor_text", None)
if preprocessor_text:
- preprocessor_text_class = tables.preprocessor_text_classes.get(preprocessor_text)
+ preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
self.preprocessor_text = preprocessor_text
@@ -57,15 +57,20 @@
source = item["source"]
data_src = load_audio_text_image_video(source, fs=self.fs)
if self.preprocessor_speech:
- data_src = self.preprocessor_speech(data_src)
- speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend) # speech: [b, T, d]
+ data_src = self.preprocessor_speech(data_src, fs=self.fs)
+ speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d]
target = item["target"]
if self.preprocessor_text:
target = self.preprocessor_text(target)
- ids = self.tokenizer.encode(target)
+ if self.tokenizer:
+ ids = self.tokenizer.encode(target)
+ text = torch.tensor(ids, dtype=torch.int64)
+ else:
+ ids = target
+ text = ids
ids_lengths = len(ids)
- text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
+ text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
return {"speech": speech[0, :, :],
"speech_lengths": speech_lengths,
@@ -83,11 +88,13 @@
outputs[key].append(sample[key])
for key, data_list in outputs.items():
- if data_list[0].dtype == torch.int64:
-
- 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 isinstance(data_list[0], torch.Tensor):
+ if data_list[0].dtype == torch.int64:
+
+ 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)
return outputs
--
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