update

游雁

2024-03-22 fcbbe8af9f22a41611d9506af17cae1e422f9fec

 funasr/bin/compute_audio_cmvn.py

@@ -28,7 +28,7 @@
def main(**kwargs):
    print(kwargs)
    # set random seed
    tables.print()
    # tables.print()
    set_all_random_seed(kwargs.get("seed", 0))
    torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
    torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
@@ -54,21 +54,14 @@
    dataset_train = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=None, is_training=False, **kwargs.get("dataset_conf"))

    # dataloader
    batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
    batch_sampler_train = None
    if batch_sampler is not None:
        batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
        dataset_conf = kwargs.get("dataset_conf")
        dataset_conf["batch_type"] = "example"
        dataset_conf["batch_size"] = 1
        batch_sampler_train = batch_sampler_class(dataset_train, is_training=False, **dataset_conf)
    batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "BatchSampler")
    batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
    dataset_conf = kwargs.get("dataset_conf")
    dataset_conf["batch_type"] = "example"
    dataset_conf["batch_size"] = 1
    batch_sampler_train = batch_sampler_class(dataset_train, is_training=False, **dataset_conf)

    
    dataloader_train = torch.utils.data.DataLoader(dataset_train,
                                                collate_fn=dataset_train.collator,
                                                batch_sampler=batch_sampler_train,
                                                num_workers=int(kwargs.get("dataset_conf").get("num_workers", 4)),
                                                pin_memory=True)
    dataloader_train = torch.utils.data.DataLoader(dataset_train, collate_fn=dataset_train.collator, **batch_sampler_train)
    
    iter_stop = int(kwargs.get("scale", 1.0)*len(dataloader_train))