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Fix incorrect doc.
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@ -39,8 +39,8 @@ The following shows the steps:
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(iv) Use "b" as "src" and its shifted version as "tgt".
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We can get another likelihood value, denoted as "ab_other"
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So for the path pair (a, b), (a, c), we can get the following log-likelihood
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values, viewed as two tensors:
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So for the path pair (a, b), (a, c), (b, a), (b, c), (c, a), and (c, b),
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we can get the following log-likelihood values, viewed as two tensors:
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self = [ab_self, ac_self, ba_self, bc_self, ca_self, cb_self]
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@ -219,7 +219,7 @@ def make_repeat_map(row_splits: torch.Tensor):
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>>> row_splits = torch.tensor([0, 3, 5], dtype=torch.int32)
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>>> make_repeat_map(row_splits)
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tensor([0, 1, 2, 0, 1, 2, 0, 1, 2, 3, 4, 3, 4], dtype=torch.int32)
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tensor([1, 2, 0, 2, 0, 1, 4, 3], dtype=torch.int32)
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"""
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device = row_splits.device
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@ -250,12 +250,11 @@ def make_repeat_map(row_splits: torch.Tensor):
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def make_repeat(tokens: k2.RaggedTensor) -> k2.RaggedTensor:
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"""Repeat the number of paths of an utterance to the number that
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equals to the number of paths in the utterance.
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"""Repeat paths in an utterance.
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For instance, if an utterance contains 3 paths: [path1 path2 path3],
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after repeating, this utterance will contain 9 paths:
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[path1 path2 path3] [path1 path2 path3] [path1 path2 path3]
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after repeating, this utterance will contain 6 paths:
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[path2 path3] [path1 path3] [path1 path2]
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>>> tokens = k2.RaggedTensor([ [[1, 2, 3], [4, 5], [9]], [[5, 8], [10, 1]] ])
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>>> tokens.to_str_simple()
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