import torch
|
import json
|
import torch.distributed as dist
|
import numpy as np
|
import kaldiio
|
import librosa
|
import torchaudio
|
import time
|
import logging
|
|
from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank
|
from funasr.utils.register import register_class, registry_tables
|
|
@register_class("dataset_classes", "AudioDataset")
|
class AudioDataset(torch.utils.data.Dataset):
|
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 = registry_tables.index_ds_classes.get(index_ds.lower())
|
self.index_ds = index_ds_class(path)
|
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(source, fs=self.fs)
|
speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend) # speech: [b, T, d]
|
target = item["target"]
|
ids = self.tokenizer.encode(target)
|
ids_lengths = len(ids)
|
text, text_lengths = torch.tensor(ids, dtype=torch.int64), 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 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
|
