update data2vec pretrain: dataset

funasr/datasets/collate_fn.py

@@ -78,6 +78,58 @@ def common_collate_fn(
             lens = torch.tensor([d[key].shape[0] for d in data], dtype=torch.long)
             output[key + "_lengths"] = lens
 
+    output = (uttids, output)
+    assert check_return_type(output)
+    return output
+
+def crop_to_max_size(feature, target_size):
+    size = len(feature)
+    diff = size - target_size
+    if diff <= 0:
+        return feature
+
+    start = np.random.randint(0, diff + 1)
+    end = size - diff + start
+    return feature[start:end]
+
+
+def clipping_collate_fn(
+        data: Collection[Tuple[str, Dict[str, np.ndarray]]],
+        max_sample_size=None,
+        not_sequence: Collection[str] = (),
+) -> Tuple[List[str], Dict[str, torch.Tensor]]:
+    # mainly for pre-training
+    assert check_argument_types()
+    uttids = [u for u, _ in data]
+    data = [d for _, d in data]
+
+    assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
+    assert all(
+        not k.endswith("_lengths") for k in data[0]
+    ), f"*_lengths is reserved: {list(data[0])}"
+
+    output = {}
+    for key in data[0]:
+        array_list = [d[key] for d in data]
+        tensor_list = [torch.from_numpy(a) for a in array_list]
+        sizes = [len(s) for s in tensor_list]
+        if max_sample_size is None:
+            target_size = min(sizes)
+        else:
+            target_size = min(min(sizes), max_sample_size)
+        tensor = tensor_list[0].new_zeros(len(tensor_list), target_size, tensor_list[0].shape[1])
+        for i, (source, size) in enumerate(zip(tensor_list, sizes)):
+            diff = size - target_size
+            if diff == 0:
+                tensor[i] = source
+            else:
+                tensor[i] = crop_to_max_size(source, target_size)
+        output[key] = tensor
+
+        if key not in not_sequence:
+            lens = torch.tensor([source.shape[0] for source in tensor], dtype=torch.long)
+            output[key + "_lengths"] = lens
+
     output = (uttids, output)
     assert check_return_type(output)
     return output

funasr/datasets/large_datasets/dataset.py

@@ -102,6 +102,8 @@ class AudioDataset(IterableDataset):
                 elif data_type == "text" or data_type == "sound":
                     text_reader = open(data_file, "r")
                     reader_list.append(text_reader)
+                elif data_type == "none":
+                    continue
                 else:
                     raise TypeError("Data type {} is not supported".format(data_type))
 

funasr/datasets/large_datasets/utils/filter.py

@@ -6,13 +6,21 @@ def filter(data,
            speech_length_max=15000,
            token_length_min=0,
            token_length_max=200):
-    assert "speech" in data
-    assert "text" in data
+    assert "speech" in data or "text" in data
 
-    if "sampling_rate" in data:
-        speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
+    if "speech" in data and "text" in data:
+        if "sampling_rate" in data:
+            speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
+        else:
+            speech_length = data["speech"].shape[0]
+        num_tokens = len(data['text'])
+        return speech_length_min < speech_length < speech_length_max and token_length_min < num_tokens < token_length_max
+    elif "speech" in data:
+        if "sampling_rate" in data:
+            speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
+        else:
+            speech_length = data["speech"].shape[0]
+        return speech_length_min < speech_length < speech_length_max
     else:
-        speech_length = data["speech"].shape[0]
-    num_tokens = len(data['text'])
-
-    return speech_length_min < speech_length < speech_length_max and token_length_min < num_tokens < token_length_max
+        num_tokens = len(data['text'])
+        return token_length_min < num_tokens < token_length_max