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| import json | |
| import random | |
| import torch | |
| import torchaudio | |
| from torch.utils.data import Dataset | |
| class AudioTextDataset(Dataset): | |
| """Can sample data from audio-text databases | |
| Params: | |
| sampling_rate: audio sampling rate | |
| max_clip_len: max length (seconds) of audio clip to be sampled | |
| """ | |
| def __init__( | |
| self, | |
| datafiles=[''], | |
| sampling_rate=32000, | |
| max_clip_len=5, | |
| ): | |
| all_data_json = [] | |
| for datafile in datafiles: | |
| with open(datafile, 'r') as fp: | |
| data_json = json.load(fp)['data'] | |
| all_data_json.extend(data_json) | |
| self.all_data_json = all_data_json | |
| self.sampling_rate = sampling_rate | |
| self.max_length = max_clip_len * sampling_rate | |
| def __len__(self): | |
| return len(self.all_data_json) | |
| def _cut_or_randomcrop(self, waveform): | |
| # waveform: [1, samples] | |
| # random crop | |
| if waveform.size(1) > self.max_length: | |
| random_idx = random.randint(0, waveform.size(1)-self.max_length) | |
| waveform = waveform[:, random_idx:random_idx+self.max_length] | |
| else: | |
| temp_wav = torch.zeros(1, self.max_length) | |
| temp_wav[:, 0:waveform.size(1)] = waveform | |
| waveform = temp_wav | |
| assert waveform.size(1) == self.max_length, \ | |
| f"number of audio samples is {waveform.size(1)}" | |
| return waveform | |
| def _read_audio(self, index): | |
| try: | |
| audio_path = self.all_data_json[index]['wav'] | |
| audio_data, audio_rate = torchaudio.load(audio_path, channels_first=True) | |
| text = self.all_data_json[index]['caption'] | |
| # drop short utterance | |
| if audio_data.size(1) < self.sampling_rate * 1: | |
| raise Exception(f'{audio_path} is too short, drop it ...') | |
| return text, audio_data, audio_rate | |
| except Exception as e: | |
| print(f'error: {e} occurs, when loading {audio_path}') | |
| random_index = random.randint(0, len(self.all_data_json)-1) | |
| return self._read_audio(index=random_index) | |
| def __getitem__(self, index): | |
| # create a audio tensor | |
| text, audio_data, audio_rate = self._read_audio(index) | |
| audio_len = audio_data.shape[1] / audio_rate | |
| # convert stero to single channel | |
| if audio_data.shape[0] > 1: | |
| # audio_data: [samples] | |
| audio_data = (audio_data[0] + audio_data[1]) / 2 | |
| else: | |
| audio_data = audio_data.squeeze(0) | |
| # resample audio clip | |
| if audio_rate != self.sampling_rate: | |
| audio_data = torchaudio.functional.resample(audio_data, orig_freq=audio_rate, new_freq=self.sampling_rate) | |
| audio_data = audio_data.unsqueeze(0) | |
| audio_data = self._cut_or_randomcrop(audio_data) | |
| data_dict = { | |
| 'text': text, | |
| 'waveform': audio_data, | |
| 'modality': 'audio_text' | |
| } | |
| return data_dict | |