import torch
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import random
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from funasr.register import tables
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from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
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@tables.register("dataset_classes", "KwsMTDataset")
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class KwsMTDataset(torch.utils.data.Dataset):
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"""
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KwsMTDataset, support multi tokenizers
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"""
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def __init__(self,
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path,
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index_ds: str = None,
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frontend=None,
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tokenizer=None,
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is_training: bool = True,
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int_pad_value: int = -1,
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float_pad_value: float = 0.0,
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**kwargs,
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):
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super().__init__()
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index_ds_class = tables.index_ds_classes.get(index_ds)
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self.index_ds = index_ds_class(path, **kwargs)
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self.preprocessor_speech = None
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self.preprocessor_text = None
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if is_training:
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preprocessor_speech = kwargs.get("preprocessor_speech", None)
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if preprocessor_speech:
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preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
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preprocessor_speech = preprocessor_speech_class(
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**kwargs.get("preprocessor_speech_conf")
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)
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self.preprocessor_speech = preprocessor_speech
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preprocessor_text = kwargs.get("preprocessor_text", None)
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if preprocessor_text:
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preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
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preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
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self.preprocessor_text = preprocessor_text
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self.frontend = frontend
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self.fs = 16000 if frontend is None else frontend.fs
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self.data_type = "sound"
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print(tokenizer)
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self.tokenizer = tokenizer
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self.int_pad_value = int_pad_value
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self.float_pad_value = float_pad_value
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def get_source_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_source_len(item)
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def get_target_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_target_len(item)
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def __len__(self):
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return len(self.index_ds)
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def __getitem__(self, index):
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item = self.index_ds[index]
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# import pdb;
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# pdb.set_trace()
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source = item["source"]
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src, fs=self.fs)
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speech, speech_lengths = extract_fbank(
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data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
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) # speech: [b, T, d]
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target = item["target"]
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if self.preprocessor_text:
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target = self.preprocessor_text(target)
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if self.tokenizer[0]:
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ids = self.tokenizer[0].encode(target)
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text = torch.tensor(ids, dtype=torch.int64)
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# print("target: ", target, ", ids: ", str(ids))
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else:
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ids = target
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text = ids
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if self.tokenizer[1]:
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ids2 = self.tokenizer[1].encode(target)
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text2 = torch.tensor(ids2, dtype=torch.int64)
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# print("target: ", target, ", ids2: ", str(ids2))
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else:
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ids2 = target
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text2 = ids2
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ids_lengths = len(ids)
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text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
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ids2_lengths = len(ids2)
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text2_lengths = torch.tensor([ids2_lengths], dtype=torch.int32)
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return {"speech": speech[0, :, :],
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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"text2": text2,
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"text2_lengths": text2_lengths,
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}
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def collator(self, samples: list=None):
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outputs = {}
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for sample in samples:
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for key in sample.keys():
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if isinstance(data_list[0], torch.Tensor):
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if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(
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data_list, batch_first=True, padding_value=pad_value
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)
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return outputs
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