From c408a8847d4257f7a45cac54b512a791f450b3f4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 23 十一月 2023 23:24:57 +0800
Subject: [PATCH] merge
---
funasr/datasets/dataset_jsonl.py | 35 ++++++++++++++++++++++++++++++++++-
1 files changed, 34 insertions(+), 1 deletions(-)
diff --git a/funasr/datasets/dataset_jsonl.py b/funasr/datasets/dataset_jsonl.py
index 9e4ee6f..1b20c95 100644
--- a/funasr/datasets/dataset_jsonl.py
+++ b/funasr/datasets/dataset_jsonl.py
@@ -22,10 +22,14 @@
def extract_features(data, date_type: str="sound", frontend=None):
if date_type == "sound":
+<<<<<<< HEAD
if isinstance(data, np.ndarray):
data = torch.from_numpy(data).to(torch.float32)
data_len = torch.tensor([data.shape[0]]).to(torch.int32)
feat, feats_lens = frontend(data[None, :], data_len)
+=======
+ feat, feats_lens = frontend(data, len(data))
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
feat = feat[0, :, :]
else:
feat, feats_lens = torch.from_numpy(data).to(torch.float32), torch.tensor([data.shape[0]]).to(torch.int32)
@@ -77,14 +81,21 @@
class AudioDataset(torch.utils.data.Dataset):
+<<<<<<< HEAD
def __init__(self, path, frontend=None, tokenizer=None, token_id_converter=None):
+=======
+ def __init__(self, path, frontend=None, tokenizer=None):
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
super().__init__()
self.indexed_dataset = IndexedDatasetJsonl(path)
self.frontend = frontend.forward
self.fs = 16000 if frontend is None else frontend.fs
self.data_type = "sound"
self.tokenizer = tokenizer
+<<<<<<< HEAD
self.token_id_converter = token_id_converter
+=======
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
self.int_pad_value = -1
self.float_pad_value = 0.0
@@ -96,15 +107,24 @@
def __getitem__(self, index):
item = self.indexed_dataset[index]
+<<<<<<< HEAD
# return item
+=======
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
source = item["source"]
data_src = load_audio(source, fs=self.fs)
speech, speech_lengths = extract_features(data_src, self.data_type, self.frontend)
target = item["target"]
+<<<<<<< HEAD
text = self.tokenizer.text2tokens(target)
ids = self.token_id_converter.tokens2ids(text)
ids_lengths = len(ids)
text, text_lengths = torch.tensor(ids, dtype=torch.int64), torch.tensor([ids_lengths], dtype=torch.int32)
+=======
+ text = self.tokenizer.encode(target)
+ text_lengths = len(text)
+ text, text_lengths = torch.tensor(text, dtype=torch.int64), torch.tensor([text_lengths], dtype=torch.int32)
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
return {"speech": speech,
"speech_lengths": speech_lengths,
"text": text,
@@ -114,19 +134,32 @@
def collator(self, samples: list=None):
+<<<<<<< HEAD
# return samples
+=======
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
outputs = {}
for sample in samples:
for key in sample.keys():
if key not in outputs:
outputs[key] = []
outputs[key].append(sample[key])
+<<<<<<< HEAD
for key, data_list in outputs.items():
if data_list[0].dtype == torch.int64:
+=======
+
+ for key, data_list in outputs.items():
+ if data_list[0].dtype.kind == "i":
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
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
\ No newline at end of file
+<<<<<<< HEAD
+ return outputs
+=======
+ return samples
+>>>>>>> 911fb3421b9867a0b27f57dfc0912f33d9e779e8
--
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