File size: 3,052 Bytes
e857f97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | # -*- coding: utf-8 -*-
# @Time : 6/19/21 12:23 AM
# @Author : Yuan Gong
# @Affiliation : Massachusetts Institute of Technology
# @Email : yuangong@mit.edu
# @File : dataloader.py
# modified from:
# Author: David Harwath
# with some functions borrowed from https://github.com/SeanNaren/deepspeech.pytorch
import csv
import json
import logging
import torchaudio
import numpy as np
import torch
import torch.nn.functional
from torch.utils.data import Dataset
import random
def make_midname_dict(label_csv):
index_lookup = {}
with open(label_csv, 'r') as f:
csv_reader = csv.DictReader(f)
line_count = 0
for row in csv_reader:
index_lookup[row['mid']] = row['display_name']
line_count += 1
return index_lookup
def make_index_dict(label_csv):
index_lookup = {}
with open(label_csv, 'r') as f:
csv_reader = csv.DictReader(f)
line_count = 0
for row in csv_reader:
index_lookup[row['mid']] = row['index']
line_count += 1
return index_lookup
def make_name_dict(label_csv):
name_lookup = {}
with open(label_csv, 'r') as f:
csv_reader = csv.DictReader(f)
line_count = 0
for row in csv_reader:
name_lookup[row['index']] = row['display_name']
line_count += 1
return name_lookup
def lookup_list(index_list, label_csv):
label_list = []
table = make_name_dict(label_csv)
for item in index_list:
label_list.append(table[item])
return label_list
def preemphasis(signal,coeff=0.97):
"""perform preemphasis on the input signal.
:param signal: The signal to filter.
:param coeff: The preemphasis coefficient. 0 is none, default 0.97.
:returns: the filtered signal.
"""
return np.append(signal[0],signal[1:]-coeff*signal[:-1])
class AudiosetDataset(Dataset):
def __init__(self, dataset_json_file, audio_conf, label_csv=None):
"""
Dataset that manages audio recordings
:param audio_conf: Dictionary containing the audio loading and preprocessing settings
:param dataset_json_file
"""
self.datapath = dataset_json_file
with open(dataset_json_file, 'r') as fp:
data_json = json.load(fp)
self.data = data_json['data']
self.index_dict = make_index_dict(label_csv)
self.label_num = len(self.index_dict)
def __getitem__(self, index):
datum = self.data[index]
label_indices = np.zeros(self.label_num)
try:
fbank, mix_lambda = self._wav2fbank(datum['wav'])
except Exception as e:
logging.warning(f"Error at {datum['wav']} with \"{e}\"")
return self.__getitem__(random.randint(0, self.__len__()-1))
for label_str in datum['labels'].split(','):
label_indices[int(self.index_dict[label_str])] = 1.0
label_indices = torch.FloatTensor(label_indices)
return fbank, label_indices
def __len__(self):
return len(self.data) |