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import torch
from funasr_detach.register import tables
from funasr_detach.utils.load_utils import extract_fbank, load_audio_text_image_video
@tables.register("dataset_classes", "AudioDataset")
class AudioDataset(torch.utils.data.Dataset):
"""
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
):
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.frontend = frontend
self.fs = 16000 if frontend is None else frontend.fs
self.data_type = "sound"
self.tokenizer = tokenizer
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;
# 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)
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():
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
)
return outputs
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