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| | | import torch |
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| | | import torch.distributed as dist |
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| | | import torchaudio |
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| | | import numpy as np |
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| | | import soundfile |
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| | | from kaldiio import ReadHelper |
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| | | from torch.utils.data import IterableDataset |
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| | | sample_dict["key"] = key |
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| | | elif data_type == "sound": |
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| | | key, path = item.strip().split() |
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| | | waveform, sampling_rate = torchaudio.load(path) |
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| | | try: |
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| | | waveform, sampling_rate = torchaudio.load(path) |
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| | | except: |
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| | | waveform, sampling_rate = soundfile.read(path, dtype='float32') |
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| | | if waveform.ndim == 2: |
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| | | waveform = waveform[:, 0] |
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| | | waveform = np.expand_dims(waveform, axis=0) |
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| | | waveform = torch.tensor(waveform) |
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| | | if self.frontend_conf is not None: |
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| | | if sampling_rate != self.frontend_conf["fs"]: |
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| | | waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate, |
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| | |
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| | | data_types = conf.get("data_types", "kaldi_ark,text") |
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| | | |
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| | | pre_hwfile = conf.get("pre_hwlist", None) |
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| | | pre_prob = conf.get("pre_prob", 0) # unused yet |
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| | | |
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| | | hw_config = {"sample_rate": conf.get("sample_rate", 0.6), |
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| | | "double_rate": conf.get("double_rate", 0.1), |
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| | | "hotword_min_length": conf.get("hotword_min_length", 2), |
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| | | "hotword_max_length": conf.get("hotword_max_length", 8), |
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| | | "pre_prob": conf.get("pre_prob", 0.0)} |
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| | | |
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| | | # pre_prob = conf.get("pre_prob", 0) # unused yet |
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| | | if pre_hwfile is not None: |
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| | | pre_hwlist = [] |
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| | | with open(pre_hwfile, 'r') as fin: |
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| | |
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| | | pre_hwlist.append(line.strip()) |
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| | | else: |
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| | | pre_hwlist = None |
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| | | |
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| | | hw_config = {"sample_rate": conf.get("sample_rate", 0.6), |
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| | | "double_rate": conf.get("double_rate", 0.1), |
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| | | "hotword_min_length": conf.get("hotword_min_length", 2), |
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| | | "hotword_max_length": conf.get("hotword_max_length", 8), |
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| | | "pre_prob": conf.get("pre_prob", 0.0), |
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| | | "pre_hwlist": pre_hwlist} |
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| | | |
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| | | |
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| | | |
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| | | dataset = AudioDataset(scp_lists, |
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| | | data_names, |
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| | |
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| | | mode=mode, |
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| | | ) |
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| | | |
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| | | filter_conf = conf.get('filter_conf', {}) |
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| | | filter_fn = partial(filter, **filter_conf) |
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| | | dataset = FilterIterDataPipe(dataset, fn=filter_fn) |
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| | | |
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| | | if "text" in data_names: |
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| | | vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict, 'bpe_tokenizer': bpe_tokenizer, 'hw_config': hw_config} |
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| | | tokenize_fn = partial(tokenize, **vocab) |
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| | | dataset = MapperIterDataPipe(dataset, fn=tokenize_fn) |
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| | | |
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| | | filter_conf = conf.get('filter_conf', {}) |
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| | | filter_fn = partial(filter, **filter_conf) |
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| | | dataset = FilterIterDataPipe(dataset, fn=filter_fn) |
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| | | |
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| | | if shuffle: |
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| | | buffer_conf = conf.get('shuffle_conf', {}) |
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| | | buffer_size = buffer_conf['shuffle_size'] |
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