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rnnt_greedy_decoding.py: typos? auto-repressively -> auto-regressively #6989

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Jul 7, 2023
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rnnt_greedy_decoding.py: typos? auto-repressively -> auto-regressively #6989

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22 changes: 11 additions & 11 deletions nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,7 +241,7 @@ def _joint_step(self, enc, pred, log_normalize: Optional[bool] = None):
class GreedyRNNTInfer(_GreedyRNNTInfer):
"""A greedy transducer decoder.

Sequence level greedy decoding, performed auto-repressively.
Sequence level greedy decoding, performed auto-regressively.

Args:
decoder_model: rnnt_utils.AbstractRNNTDecoder implementation.
Expand Down Expand Up @@ -326,7 +326,7 @@ def forward(
partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None,
):
"""Returns a list of hypotheses given an input batch of the encoder hidden embedding.
Output token is generated auto-repressively.
Output token is generated auto-regressively.

Args:
encoder_output: A tensor of size (batch, features, timesteps).
Expand Down Expand Up @@ -479,7 +479,7 @@ def _greedy_decode(
class GreedyBatchedRNNTInfer(_GreedyRNNTInfer):
"""A batch level greedy transducer decoder.

Batch level greedy decoding, performed auto-repressively.
Batch level greedy decoding, performed auto-regressively.

Args:
decoder_model: rnnt_utils.AbstractRNNTDecoder implementation.
Expand Down Expand Up @@ -571,7 +571,7 @@ def forward(
partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None,
):
"""Returns a list of hypotheses given an input batch of the encoder hidden embedding.
Output token is generated auto-repressively.
Output token is generated auto-regressively.

Args:
encoder_output: A tensor of size (batch, features, timesteps).
Expand Down Expand Up @@ -1034,7 +1034,7 @@ def __init__(self, encoder_model: str, decoder_joint_model: str, max_symbols_per

def __call__(self, audio_signal: torch.Tensor, length: torch.Tensor):
"""Returns a list of hypotheses given an input batch of the encoder hidden embedding.
Output token is generated auto-repressively.
Output token is generated auto-regressively.

Args:
encoder_output: A tensor of size (batch, features, timesteps).
Expand Down Expand Up @@ -1455,7 +1455,7 @@ def _get_initial_states(self, batchsize):
class GreedyMultiblankRNNTInfer(GreedyRNNTInfer):
"""A greedy transducer decoder for multi-blank RNN-T.

Sequence level greedy decoding, performed auto-repressively.
Sequence level greedy decoding, performed auto-regressively.

Args:
decoder_model: rnnt_utils.AbstractRNNTDecoder implementation.
Expand Down Expand Up @@ -1655,7 +1655,7 @@ def _greedy_decode(

class GreedyBatchedMultiblankRNNTInfer(GreedyBatchedRNNTInfer):
"""A batch level greedy transducer decoder.
Batch level greedy decoding, performed auto-repressively.
Batch level greedy decoding, performed auto-regressively.
Args:
decoder_model: rnnt_utils.AbstractRNNTDecoder implementation.
joint_model: rnnt_utils.AbstractRNNTJoint implementation.
Expand Down Expand Up @@ -2207,7 +2207,7 @@ class GreedyBatchedRNNTInferConfig:
class GreedyTDTInfer(_GreedyRNNTInfer):
"""A greedy TDT decoder.

Sequence level greedy decoding, performed auto-repressively.
Sequence level greedy decoding, performed auto-regressively.

Args:
decoder_model: rnnt_utils.AbstractRNNTDecoder implementation.
Expand Down Expand Up @@ -2289,7 +2289,7 @@ def forward(
partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None,
):
"""Returns a list of hypotheses given an input batch of the encoder hidden embedding.
Output token is generated auto-repressively.
Output token is generated auto-regressively.
Args:
encoder_output: A tensor of size (batch, features, timesteps).
encoded_lengths: list of int representing the length of each sequence
Expand Down Expand Up @@ -2459,7 +2459,7 @@ def _greedy_decode(

class GreedyBatchedTDTInfer(_GreedyRNNTInfer):
"""A batch level greedy TDT decoder.
Batch level greedy decoding, performed auto-repressively.
Batch level greedy decoding, performed auto-regressively.
Args:
decoder_model: rnnt_utils.AbstractRNNTDecoder implementation.
joint_model: rnnt_utils.AbstractRNNTJoint implementation.
Expand Down Expand Up @@ -2547,7 +2547,7 @@ def forward(
partial_hypotheses: Optional[List[rnnt_utils.Hypothesis]] = None,
):
"""Returns a list of hypotheses given an input batch of the encoder hidden embedding.
Output token is generated auto-repressively.
Output token is generated auto-regressively.
Args:
encoder_output: A tensor of size (batch, features, timesteps).
encoded_lengths: list of int representing the length of each sequence
Expand Down