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| import csv |
| import json |
| import logging |
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| 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) |
|
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| 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) |
|
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|
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| return fbank, label_indices |
|
|
| def __len__(self): |
| return len(self.data) |
