From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/datasets/audio_datasets/datasets.py | 235 ++++++++++++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 201 insertions(+), 34 deletions(-)
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index 5af33fc..2aafde3 100644
--- a/funasr/datasets/audio_datasets/datasets.py
+++ b/funasr/datasets/audio_datasets/datasets.py
@@ -1,4 +1,5 @@
import torch
+import random
from funasr.register import tables
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
@@ -9,28 +10,33 @@
"""
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,
+ 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)
+ 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 = preprocessor_speech_class(**kwargs.get("preprocessor_speech_conf"))
+ 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
-
+
self.frontend = frontend
self.fs = 16000 if frontend is None else frontend.fs
self.data_type = "sound"
@@ -38,18 +44,18 @@
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;
@@ -57,24 +63,31 @@
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,
- "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():
@@ -83,11 +96,165 @@
outputs[key].append(sample[key])
for key, data_list in outputs.items():
- if data_list[0].dtype == torch.int64:
+ 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)
+ 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
+
+@tables.register("dataset_classes", "AudioDatasetHotword")
+class AudioDatasetHotword(AudioDataset):
+ # for finetuning contextual_paraformer and seaco_paraformer
+ def __init__(
+ self,
+ *args,
+ seaco_id: bool = 0,
+ **kwargs,
+ ):
+ super().__init__(*args, **kwargs)
+ self.seaco_id = seaco_id
+
+ def __getitem__(self, index):
+ item = self.index_ds[index]
+ # import pdb;
+ # pdb.set_trace()
+ 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, 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)
+ 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_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,
+ ):
+ 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
+ ):
+ # 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
+ )
+ # 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),
+ )
+ 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),
+ )
+ 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):
+ outputs = {}
+ hotword_indxs = []
+ seaco_id = samples[0]["seaco_id"]
+ for sample in samples:
+ for key in sample.keys():
+ if key == "seaco_id":
+ continue
+ elif key == "hotword_indx":
+ hotword_indxs.append(sample[key])
+ else:
+ 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:
+ 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
+ )
+
+ hotword_list, hotword_lengths = [], []
+ 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"])
+ ):
+ length = length[0]
+ 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_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]
+ 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(torch.tensor([1]))
+ hotword_lengths.append(1)
+ 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
--
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