def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
"""Performs greedy decoding on the logits given in input (best path).
Note: Regardless of the value of merge_repeated, if the maximum index of a
given time and batch corresponds to the blank index `(num_classes - 1)`, no
new element is emitted.
If `merge_repeated` is `True`, merge repeated classes in output.
This means that if consecutive logits' maximum indices are the same,
only the first of these is emitted. The sequence `A B B * B * B` (where '*'
is the blank label) becomes
* `A B B B` if `merge_repeated=True`.
* `A B B B B` if `merge_repeated=False`.
Args:
inputs: 3-D `float` `Tensor` sized `[max_time, batch_size, num_classes]`.
The logits.
sequence_length: 1-D `int32` vector containing sequence lengths, having size
`[batch_size]`.
merge_repeated: Boolean. Default: True.
Returns:
A tuple `(decoded, neg_sum_logits)` where
decoded: A single-element list. `decoded[0]`
is an `SparseTensor` containing the decoded outputs s.t.:
`decoded.indices`: Indices matrix `(total_decoded_outputs, 2)`.
The rows store: `[batch, time]`.
`decoded.values`: Values vector, size `(total_decoded_outputs)`.
The vector stores the decoded classes.
`decoded.dense_shape`: Shape vector, size `(2)`.
The shape values are: `[batch_size, max_decoded_length]`
neg_sum_logits: A `float` matrix `(batch_size x 1)` containing, for the
sequence found, the negative of the sum of the greatest logit at each
timeframe.
"""
outputs = gen_ctc_ops.ctc_greedy_decoder(inputs,
sequence_length,
merge_repeated=merge_repeated)
(decoded_ix, decoded_val, decoded_shape, log_probabilities) = outputs
return ([
sparse_tensor.SparseTensor(decoded_ix, decoded_val, decoded_shape)
], log_probabilities)
def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
"""Performs greedy decoding on the logits given in input (best path).
Note: Regardless of the value of merge_repeated, if the maximum index of a
given time and batch corresponds to the blank index `(num_classes - 1)`, no
new element is emitted.
If `merge_repeated` is `True`, merge repeated classes in output.
This means that if consecutive logits' maximum indices are the same,
only the first of these is emitted. The sequence `A B B * B * B` (where '*'
is the blank label) becomes
* `A B B B` if `merge_repeated=True`.
* `A B B B B` if `merge_repeated=False`.
Args:
inputs: 3-D `float` `Tensor` sized
`[max_time, batch_size, num_classes]`. The logits.
sequence_length: 1-D `int32` vector containing sequence lengths,
having size `[batch_size]`.
merge_repeated: Boolean. Default: True.
Returns:
A tuple `(decoded, neg_sum_logits)` where
decoded: A single-element list. `decoded[0]`
is an `SparseTensor` containing the decoded outputs s.t.:
`decoded.indices`: Indices matrix `(total_decoded_outputs, 2)`.
The rows store: `[batch, time]`.
`decoded.values`: Values vector, size `(total_decoded_outputs)`.
The vector stores the decoded classes.
`decoded.dense_shape`: Shape vector, size `(2)`.
The shape values are: `[batch_size, max_decoded_length]`
neg_sum_logits: A `float` matrix `(batch_size x 1)` containing, for the
sequence found, the negative of the sum of the greatest logit at each
timeframe.
"""
outputs = gen_ctc_ops.ctc_greedy_decoder(
inputs, sequence_length, merge_repeated=merge_repeated)
(decoded_ix, decoded_val, decoded_shape, log_probabilities) = outputs
return ([sparse_tensor.SparseTensor(decoded_ix, decoded_val, decoded_shape)],
log_probabilities)
def decode_ctc(
logits: Union[list, np.ndarray],
merge_repeated=True,
alphabet: Union[None, np.ndarray, List[str]] = None,
seq_lens: Optional[List[int]] = None,
):
if alphabet is not None and isinstance(alphabet, list):
alphabet = np.array(alphabet)
if isinstance(logits, list):
logits = np.array(logits)
decoded_ix, decoded_val, decoded_shape, log_probabilities = ctc_greedy_decoder(
np.transpose(logits, (1, 0, 2)),
np.full((logits.shape[0], ), fill_value=logits.shape[1], dtype=np.int)
if not seq_lens else seq_lens,
merge_repeated=merge_repeated,
)
return _decoded_to_rows(decoded_ix.numpy(),
decoded_val.numpy(),
decoded_shape.numpy(),
alphabet=alphabet,
aslist=True)