update large dataset for sampling rate

仁迷

2023-03-01 7984a37f8ce2d0db89913b22bad521e6a64aed27

update large dataset for sampling rate

 funasr/datasets/large_datasets/build_dataloader.py

@@ -34,7 +34,7 @@
    return seg_dict

class ArkDataLoader(AbsIterFactory):
    def __init__(self, data_list, dict_file, dataset_conf, seg_dict_file=None, punc_dict_file=None, mode="train"):
    def __init__(self, data_list, dict_file, dataset_conf, frontend_conf=None, seg_dict_file=None, punc_dict_file=None, mode="train"):
        symbol_table = read_symbol_table(dict_file) if dict_file is not None else None
        if seg_dict_file is not None:
            seg_dict = load_seg_dict(seg_dict_file)
@@ -45,10 +45,11 @@
        else:
            punc_dict = None
        self.dataset_conf = dataset_conf
        self.frontend_conf = frontend_conf
        logging.info("dataloader config: {}".format(self.dataset_conf))
        batch_mode = self.dataset_conf.get("batch_mode", "padding")
        self.dataset = Dataset(data_list, symbol_table, seg_dict, punc_dict,
                               self.dataset_conf, mode=mode, batch_mode=batch_mode)
                               self.dataset_conf, self.frontend_conf, mode=mode, batch_mode=batch_mode)

    def build_iter(self, epoch, shuffle=True):
        self.dataset.set_epoch(epoch)

 funasr/datasets/large_datasets/dataset.py

@@ -28,10 +28,11 @@


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
@@ -119,6 +120,11 @@
                    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
@@ -153,13 +159,14 @@
            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)

 funasr/tasks/abs_task.py

@@ -1348,11 +1348,13 @@
            if args.dataset_type == "large":
                from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
                train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
                                                   frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
                                                   seg_dict_file=args.seg_dict_file if hasattr(args,
                                                                                               "seg_dict_file") else None,
                                                   punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
                                                   mode="train")
                valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
                                                   frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
                                                   seg_dict_file=args.seg_dict_file if hasattr(args,
                                                                                               "seg_dict_file") else None,
                                                   punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,