decoders.py
```
# Copyright (c) 2019, Myrtle Software Limited. All rights reserved.
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
from model_rnnt import label_collate
class TransducerDecoder:
"""Decoder base class.
Args:
alphabet: An Alphabet object.
blank_symbol: The symbol in `alphabet` to use as the blank during CTC
decoding.
model: Model to use for prediction.
"""
def __init__(self, blank_index, model):
self._model = model
self._SOS = -1 # start of sequence
self._blank_id = blank_index
def _pred_step(self, label, hidden, device):
if label == self._SOS:
return self._model.predict(None, hidden, add_sos=False)
# return self._model.prediction(None, hidden, add_sos=False)
if label > self._blank_id:
label -= 1
label = label_collate([[label]]).to(device)
return self._model.predict(label, hidden, add_sos=False)
# return self._model.prediction(label, hidden, add_sos=False)
def _joint_step(self, enc, pred, log_normalize=False):
logits = self._model.joint(enc, pred)[:, 0, 0, :]
if not log_normalize:
return logits
probs = F.log_softmax(logits, dim=len(logits.shape) - 1)
return probs
def _get_last_symb(self, labels):
return self._SOS if labels == [] else labels[-1]
```
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