From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/datasets/audio_datasets/datasets.py | 206 +++++++++++++++++++++++++++++++--------------------
1 files changed, 124 insertions(+), 82 deletions(-)
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index 260236c..68b2d3c 100644
--- a/funasr/datasets/audio_datasets/datasets.py
+++ b/funasr/datasets/audio_datasets/datasets.py
@@ -1,6 +1,7 @@
import torch
import random
+
from funasr.register import tables
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
@@ -10,28 +11,39 @@
"""
AudioDataset
"""
- def __init__(self,
- path,
- index_ds: str = None,
- frontend=None,
- tokenizer=None,
- int_pad_value: int = -1,
- float_pad_value: float = 0.0,
- **kwargs):
+
+ def __init__(
+ self,
+ path,
+ index_ds: str = None,
+ frontend=None,
+ tokenizer=None,
+ is_training: bool = True,
+ int_pad_value: int = -1,
+ float_pad_value: float = 0.0,
+ **kwargs,
+ ):
super().__init__()
index_ds_class = tables.index_ds_classes.get(index_ds)
self.index_ds = index_ds_class(path, **kwargs)
- preprocessor_speech = kwargs.get("preprocessor_speech", None)
- if 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_classes.get(preprocessor_text)
- preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
- self.preprocessor_text = preprocessor_text
-
+
+ self.preprocessor_speech = None
+ self.preprocessor_text = None
+
+ if is_training:
+ preprocessor_speech = kwargs.get("preprocessor_speech", None)
+ if 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_classes.get(preprocessor_text)
+ preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
+ self.preprocessor_text = preprocessor_text
+
self.frontend = frontend
self.fs = 16000 if frontend is None else frontend.fs
self.data_type = "sound"
@@ -39,18 +51,18 @@
self.int_pad_value = int_pad_value
self.float_pad_value = float_pad_value
-
+
def get_source_len(self, index):
item = self.index_ds[index]
return self.index_ds.get_source_len(item)
-
+
def get_target_len(self, index):
item = self.index_ds[index]
return self.index_ds.get_target_len(item)
-
+
def __len__(self):
return len(self.index_ds)
-
+
def __getitem__(self, index):
item = self.index_ds[index]
# import pdb;
@@ -59,11 +71,15 @@
data_src = load_audio_text_image_video(source, fs=self.fs)
if self.preprocessor_speech:
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]
+
+ 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)
+
if self.tokenizer:
ids = self.tokenizer.encode(target)
text = torch.tensor(ids, dtype=torch.int64)
@@ -73,14 +89,14 @@
ids_lengths = len(ids)
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
- return {"speech": speech[0, :, :],
- "speech_lengths": speech_lengths,
- "text": text,
- "text_lengths": text_lengths,
- }
-
-
- def collator(self, samples: list=None):
+ return {
+ "speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ }
+
+ def collator(self, samples: list = None):
outputs = {}
for sample in samples:
for key in sample.keys():
@@ -90,13 +106,15 @@
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
- if data_list[0].dtype == torch.int64:
-
+ 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)
+
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(
+ data_list, batch_first=True, padding_value=pad_value
+ )
return outputs
@@ -111,7 +129,7 @@
):
super().__init__(*args, **kwargs)
self.seaco_id = seaco_id
-
+
def __getitem__(self, index):
item = self.index_ds[index]
# import pdb;
@@ -120,7 +138,9 @@
data_src = load_audio_text_image_video(source, fs=self.fs)
if self.preprocessor_speech:
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]
+ 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:
@@ -133,57 +153,71 @@
text = ids
ids_lengths = len(ids)
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
-
- def generate_index(length,
- hotword_min_length=2,
- hotword_max_length=8,
- sample_rate=0.75,
- double_rate=0.1,
- pre_prob=0.0,
- pre_index=None,
- pre_hwlist=None):
+
+ def generate_index(
+ length,
+ hotword_min_length=2,
+ hotword_max_length=8,
+ sample_rate=0.75,
+ double_rate=0.1,
+ pre_prob=0.0,
+ pre_index=None,
+ pre_hwlist=None,
+ ):
if length < hotword_min_length:
return [-1]
if random.random() < sample_rate:
if pre_prob > 0 and random.random() < pre_prob and pre_index is not None:
return pre_index
if length == hotword_min_length:
- return [0, length-1]
- elif random.random() < double_rate and length > hotword_max_length + hotword_min_length + 2:
+ return [0, length - 1]
+ elif (
+ random.random() < double_rate
+ and length > hotword_max_length + hotword_min_length + 2
+ ):
# sample two hotwords in a sentence
_max_hw_length = min(hotword_max_length, length // 2)
# first hotword
start1 = random.randint(0, length // 3)
- end1 = random.randint(start1 + hotword_min_length - 1, start1 + _max_hw_length - 1)
+ end1 = random.randint(
+ start1 + hotword_min_length - 1, start1 + _max_hw_length - 1
+ )
# second hotword
start2 = random.randint(end1 + 1, length - hotword_min_length)
- end2 = random.randint(min(length-1, start2+hotword_min_length-1), min(length-1, start2+hotword_max_length-1))
+ end2 = random.randint(
+ min(length - 1, start2 + hotword_min_length - 1),
+ min(length - 1, start2 + hotword_max_length - 1),
+ )
return [start1, end1, start2, end2]
else: # single hotword
start = random.randint(0, length - hotword_min_length)
- end = random.randint(min(length-1, start+hotword_min_length-1), min(length-1, start+hotword_max_length-1))
+ end = random.randint(
+ min(length - 1, start + hotword_min_length - 1),
+ min(length - 1, start + hotword_max_length - 1),
+ )
return [start, end]
else:
return [-1]
-
+
hotword_indx = generate_index(text_lengths[0])
- return {"speech": speech[0, :, :],
- "speech_lengths": speech_lengths,
- "text": text,
- "text_lengths": text_lengths,
- "hotword_indx": hotword_indx,
- "seaco_id": self.seaco_id,
- }
-
- def collator(self, samples: list=None):
+ return {
+ "speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ "hotword_indx": hotword_indx,
+ "seaco_id": self.seaco_id,
+ }
+
+ def collator(self, samples: list = None):
outputs = {}
hotword_indxs = []
- seaco_id = samples[0]['seaco_id']
+ seaco_id = samples[0]["seaco_id"]
for sample in samples:
for key in sample.keys():
- if key == 'seaco_id':
+ if key == "seaco_id":
continue
- elif key == 'hotword_indx':
+ elif key == "hotword_indx":
hotword_indxs.append(sample[key])
else:
if key not in outputs:
@@ -192,36 +226,44 @@
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
- if data_list[0].dtype == torch.int64:
+ 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)
-
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(
+ data_list, batch_first=True, padding_value=pad_value
+ )
+
hotword_list, hotword_lengths = [], []
- text = outputs['text']
+ text = outputs["text"]
seaco_label_pad = torch.ones_like(text) * -1 if seaco_id else None
- for b, (hotword_indx, one_text, length) in enumerate(zip(hotword_indxs, text, outputs['text_lengths'])):
+ for b, (hotword_indx, one_text, length) in enumerate(
+ zip(hotword_indxs, text, outputs["text_lengths"])
+ ):
length = length[0]
- if seaco_label_pad is not None: seaco_label_pad[b][:length] = seaco_id
+ if seaco_label_pad is not None:
+ seaco_label_pad[b][:length] = seaco_id
if hotword_indx[0] != -1:
start, end = int(hotword_indx[0]), int(hotword_indx[1])
- hotword = one_text[start: end+1]
+ hotword = one_text[start : end + 1]
hotword_list.append(hotword)
- hotword_lengths.append(end-start+1)
- if seaco_label_pad is not None: seaco_label_pad[b][start: end+1] = one_text[start: end+1]
+ hotword_lengths.append(end - start + 1)
+ if seaco_label_pad is not None:
+ seaco_label_pad[b][start : end + 1] = one_text[start : end + 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)
- if seaco_label_pad is not None: seaco_label_pad[b][start: end+1] = one_text[start: end+1]
+ hotword_list.append(one_text[start : end + 1])
+ hotword_lengths.append(end - start + 1)
+ if seaco_label_pad is not None:
+ seaco_label_pad[b][start : end + 1] = one_text[start : end + 1]
hotword_list.append(torch.tensor([1]))
hotword_lengths.append(1)
- hotword_pad = torch.nn.utils.rnn.pad_sequence(hotword_list,
- batch_first=True,
- padding_value=0)
+ hotword_pad = torch.nn.utils.rnn.pad_sequence(
+ hotword_list, batch_first=True, padding_value=0
+ )
outputs["hotword_pad"] = hotword_pad
outputs["hotword_lengths"] = torch.tensor(hotword_lengths, dtype=torch.int32)
- if seaco_label_pad is not None: outputs['seaco_label_pad'] = seaco_label_pad
- return outputs
\ No newline at end of file
+ if seaco_label_pad is not None:
+ outputs["seaco_label_pad"] = seaco_label_pad
+ return outputs
--
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