| | |
|---|
| | | |
|---|
| | | |
|---|
| | | class AudioDataset(IterableDataset): |
|---|
| | | def __init__(self, scp_lists, data_names, data_types, shuffle=True, mode="train"): |
|---|
| | | def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, mode="train"): |
|---|
| | | self.scp_lists = scp_lists |
|---|
| | | self.data_names = data_names |
|---|
| | | self.data_types = data_types |
|---|
| | | self.frontend_conf = frontend_conf |
|---|
| | | self.shuffle = shuffle |
|---|
| | | self.mode = mode |
|---|
| | | self.epoch = -1 |
|---|
| | |
|---|
| | | elif data_type == "sound": |
|---|
| | | key, path = item.strip().split() |
|---|
| | | waveform, sampling_rate = torchaudio.load(path) |
|---|
| | | if self.frontend_conf is not None: |
|---|
| | | if sampling_rate != self.frontend_conf["fs"]: |
|---|
| | | waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate, |
|---|
| | | new_freq=self.frontend_conf["fs"])(waveform) |
|---|
| | | sampling_rate = self.frontend_conf["fs"] |
|---|
| | | waveform = waveform.numpy() |
|---|
| | | mat = waveform[0] |
|---|
| | | sample_dict[data_name] = mat |
|---|
| | |
|---|
| | | seg_dict, |
|---|
| | | punc_dict, |
|---|
| | | conf, |
|---|
| | | frontend_conf, |
|---|
| | | mode="train", |
|---|
| | | batch_mode="padding"): |
|---|
| | | scp_lists = read_lists(data_list_file) |
|---|
| | | shuffle = conf.get('shuffle', True) |
|---|
| | | data_names = conf.get("data_names", "speech,text") |
|---|
| | | data_types = conf.get("data_types", "kaldi_ark,text") |
|---|
| | | dataset = AudioDataset(scp_lists, data_names, data_types, shuffle=shuffle, mode=mode) |
|---|
| | | dataset = AudioDataset(scp_lists, data_names, data_types, frontend_conf=frontend_conf, shuffle=shuffle, mode=mode) |
|---|
| | | |
|---|
| | | filter_conf = conf.get('filter_conf', {}) |
|---|
| | | filter_fn = partial(filter, **filter_conf) |
|---|
