From 0cf5dfec2c8313fc2ed2aab8d10bf3dc4b9c283f Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期四, 14 三月 2024 14:41:49 +0800
Subject: [PATCH] update cmakelist
---
funasr/datasets/audio_datasets/datasets.py | 160 +++++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 147 insertions(+), 13 deletions(-)
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index 5af33fc..260236c 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
@@ -19,15 +20,15 @@
**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 +58,20 @@
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 +89,139 @@
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
+
+@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:
+ 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
\ No newline at end of file
--
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