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build | ||
_ext | ||
pytorch_ctc | ||
*.pyc | ||
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# Byte-compiled / optimized / DLL files | ||
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import torch | ||
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from cffi import FFI | ||
ffi = FFI() | ||
from ._ext.ctc_decode import ctc_beam_decode | ||
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def beam_decode(probs, seq_len=None, top_paths=1, beam_width=10, merge_repeated=True): | ||
prob_size = probs.size() | ||
max_seq_len = prob_size[0] | ||
batch_size = prob_size[1] | ||
num_classes = prob_size[2] | ||
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if seq_len is not None and batch_size != seq_len.size(): | ||
raise ValueError("seq_len shape must be a 1xbatch_size tensor or None") | ||
if top_paths < 1 or top_paths > beam_width: | ||
raise ValueError("top_paths must be greater than 1 and less than or equal to the beam_width") | ||
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seq_len = torch.IntTensor(batch_size).zero_().add_(max_seq_len) if seq_len is None else seq_len | ||
output = torch.IntTensor(top_paths, batch_size, max_seq_len) | ||
scores = torch.FloatTensor(top_paths, batch_size) | ||
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merge_int = 1 if merge_repeated else 0 | ||
result = ctc_beam_decode(probs, seq_len, output, scores, top_paths, beam_width, merge_int) | ||
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return output, scores |
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