Returned previous names of return_hypotheses

Signed-off-by: Ssofja <sofiakostandian@gmail.com>

nemo/collections/asr/metrics/bleu.py

@@ -116,7 +116,7 @@ def __init__(
                 encoder_hidden_states=predictions,
                 encoder_input_mask=predictions_mask,
                 decoder_input_ids=input_ids,
-                return_all_hypotheses=False,
+                return_hypotheses=False,
             )
         else:
             raise TypeError(f"WER metric does not support decoding of type {type(self.decoding)}")

nemo/collections/asr/metrics/wer.py

@@ -280,7 +280,7 @@ def __init__(
                 encoder_hidden_states=predictions,
                 encoder_input_mask=predictions_mask,
                 decoder_input_ids=input_ids,
-                return_all_hypotheses=False,
+                return_hypotheses=False,
             )
         else:
             raise TypeError(f"WER metric does not support decoding of type {type(self.decoding)}")

nemo/collections/asr/models/aed_multitask_models.py

@@ -457,7 +457,7 @@ def transcribe(
         self,
         audio: Union[str, List[str], np.ndarray, DataLoader],
         batch_size: int = 4,
-        return_all_hypotheses: bool = False,
+        return_hypotheses: bool = False,
         num_workers: int = 0,
         channel_selector: Optional[ChannelSelectorType] = None,
         augmentor: DictConfig = None,
@@ -506,7 +506,7 @@ def transcribe(
         if override_config is None:
             trcfg = MultiTaskTranscriptionConfig(
                 batch_size=batch_size,
-                return_all_hypotheses=return_all_hypotheses,
+                return_hypotheses=return_hypotheses,
                 num_workers=num_workers,
                 channel_selector=channel_selector,
                 augmentor=augmentor,
@@ -955,7 +955,7 @@ def _transcribe_output_processing(self, outputs, trcfg: MultiTaskTranscriptionCo
             encoder_hidden_states=enc_states,
             encoder_input_mask=enc_mask,
             decoder_input_ids=decoder_input_ids,
-            return_all_hypotheses=trcfg.return_all_hypotheses,
+            return_hypotheses=trcfg.return_hypotheses,
         )
 
         del enc_states, enc_mask, decoder_input_ids
@@ -1086,7 +1086,7 @@ def predict_step(
             encoder_hidden_states=enc_states,
             encoder_input_mask=enc_mask,
             decoder_input_ids=batch.prompt,
-            return_all_hypotheses=False,
+            return_hypotheses=False,
         )
         if batch.cuts:
             return list(zip(batch.cuts, text))

nemo/collections/asr/models/ctc_models.py

@@ -616,7 +616,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
         transcribed_texts = self.wer.decoding.ctc_decoder_predictions_tensor(
             decoder_outputs=log_probs,
             decoder_lengths=encoded_len,
-            return_all_hypotheses=False,
+            return_hypotheses=False,
         )
 
         if isinstance(sample_id, torch.Tensor):
@@ -711,7 +711,7 @@ def _transcribe_output_processing(self, outputs, trcfg: TranscribeConfig) -> Gen
         hypotheses = self.decoding.ctc_decoder_predictions_tensor(
             logits,
             decoder_lengths=logits_len,
-            return_all_hypotheses=trcfg.return_all_hypotheses,
+            return_hypotheses=trcfg.return_hypotheses,
         )
         if trcfg.return_hypotheses:
             if logits.is_cuda:

nemo/collections/asr/models/hybrid_rnnt_ctc_models.py

@@ -211,7 +211,7 @@ def _transcribe_output_processing(
         hypotheses = self.ctc_decoding.ctc_decoder_predictions_tensor(
             logits,
             encoded_len,
-            return_all_hypotheses=trcfg.return_all_hypotheses,
+            return_hypotheses=trcfg.return_hypotheses,
         )
         logits = logits.cpu()
 
@@ -504,7 +504,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
         del signal
 
         best_hyp_text = self.decoding.rnnt_decoder_predictions_tensor(
-            encoder_output=encoded, encoded_lengths=encoded_len, return_all_hypotheses=False
+            encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False
         )
         if isinstance(sample_id, torch.Tensor):
             sample_id = sample_id.cpu().detach().numpy()

nemo/collections/asr/models/rnnt_models.py

@@ -816,7 +816,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
         del signal
 
         best_hyp_text = self.decoding.rnnt_decoder_predictions_tensor(
-            encoder_output=encoded, encoded_lengths=encoded_len, return_all_hypotheses=False
+            encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False
         )
 
         if isinstance(sample_id, torch.Tensor):
@@ -946,7 +946,7 @@ def _transcribe_output_processing(
         hyp = self.decoding.rnnt_decoder_predictions_tensor(
             encoded,
             encoded_len,
-            return_all_hypotheses=trcfg.return_all_hypotheses,
+            return_hypotheses=trcfg.return_hypotheses,
             partial_hypotheses=trcfg.partial_hypothesis,
         )
         # cleanup memory

nemo/collections/asr/parts/mixins/mixins.py

@@ -705,7 +705,7 @@ def conformer_stream_step(
             best_hyp = self.decoding.rnnt_decoder_predictions_tensor(
                 encoder_output=encoded,
                 encoded_lengths=encoded_len,
-                return_all_hypotheses=True,
+                return_hypotheses=True,
                 partial_hypotheses=previous_hypotheses,
             )
             greedy_predictions = [hyp.y_sequence for hyp in best_hyp]

nemo/collections/asr/parts/mixins/transcription.py

@@ -57,7 +57,7 @@ class InternalTranscribeConfig:
 @dataclass
 class TranscribeConfig:
     batch_size: int = 4
-    return_all_hypotheses: bool = False
+    return_hypotheses: bool = False
     num_workers: Optional[int] = None
     channel_selector: ChannelSelectorType = None
     augmentor: Optional[DictConfig] = None
@@ -69,13 +69,6 @@ class TranscribeConfig:
 
     _internal: Optional[InternalTranscribeConfig] = None
 
-    @property
-    def return_hypotheses(self):
-        return self.return_all_hypotheses
-
-    @return_hypotheses.setter
-    def return_hypotheses(self, value):
-        self.return_all_hypotheses = value
 
 
 def get_value_from_transcription_config(trcfg, key, default):

nemo/collections/asr/parts/submodules/ctc_decoding.py

@@ -359,7 +359,7 @@ def ctc_decoder_predictions_tensor(
         decoder_outputs: torch.Tensor,
         decoder_lengths: torch.Tensor = None,
         fold_consecutive: bool = True,
-        return_all_hypotheses: bool = False,
+        return_hypotheses: bool = False,
     ) -> Union[List[Hypothesis], List[List[Hypothesis]]]:
         """
         Decodes a sequence of labels to words
@@ -372,7 +372,7 @@ def ctc_decoder_predictions_tensor(
                 of the sequence in the padded `predictions` tensor.
             fold_consecutive: Bool, determine whether to perform "ctc collapse", folding consecutive tokens
                 into a single token.
-            return_all_hypotheses: Bool flag whether to return just the decoding predictions of the model
+            return_hypotheses: Bool flag whether to return just the decoding predictions of the model
                 or a Hypothesis object that holds information such as the decoded `text`,
                 the `alignment` of emited by the CTC Model, and the `length` of the sequence (if available).
                 May also contain the log-probabilities of the decoder (if this method is called via
@@ -426,7 +426,7 @@ def ctc_decoder_predictions_tensor(
 
                     all_hypotheses.append(decoded_hyps)
 
-            if return_all_hypotheses:
+            if return_hypotheses:
                 return all_hypotheses  # type: list[list[Hypothesis]]
 
             # alaptev: The line below might contain a bug. Do we really want all_hyp_text to be flat?
@@ -444,7 +444,7 @@ def ctc_decoder_predictions_tensor(
                 # If computing timestamps
                 if self.compute_timestamps is True:
                     # greedy decoding, can get high-level confidence scores
-                    if return_all_hypotheses and (self.preserve_word_confidence or self.preserve_token_confidence):
+                    if return_hypotheses and (self.preserve_word_confidence or self.preserve_token_confidence):
                         hypotheses = self.compute_confidence(hypotheses)
                     else:
                         # remove unused token_repetitions from Hypothesis.text
@@ -454,7 +454,7 @@ def ctc_decoder_predictions_tensor(
                     for hyp_idx in range(len(hypotheses)):
                         hypotheses[hyp_idx] = self.compute_ctc_timestamps(hypotheses[hyp_idx], timestamp_type)
 
-            if return_all_hypotheses:
+            if return_hypotheses:
                 return hypotheses
 
             return [Hypothesis(h.score, h.y_sequence, h.text) for h in hypotheses]

nemo/collections/asr/parts/submodules/multitask_decoding.py

@@ -214,7 +214,7 @@ def decode_predictions_tensor(
         encoder_hidden_states: torch.Tensor,
         encoder_input_mask: torch.Tensor,
         decoder_input_ids: Optional[torch.Tensor] = None,
-        return_all_hypotheses: bool = False,
+        return_hypotheses: bool = False,
         partial_hypotheses: Optional[List[Hypothesis]] = None,
     ) -> Union[List[Hypothesis], List[List[Hypothesis]]]:
         """
@@ -260,7 +260,7 @@ def decode_predictions_tensor(
                 hypotheses.append(decoded_hyps[0])  # best hypothesis
                 all_hypotheses.append(decoded_hyps)
 
-            if return_all_hypotheses:
+            if return_hypotheses:
                 return all_hypotheses
 
             all_hyp = [[Hypothesis(h.score, h.y_sequence, h.text) for h in hh] for hh in all_hypotheses]
@@ -269,7 +269,7 @@ def decode_predictions_tensor(
         else:
             hypotheses = self.decode_hypothesis(prediction_list)
 
-            if return_all_hypotheses:
+            if return_hypotheses:
                 # greedy decoding, can get high-level confidence scores
                 if self.preserve_frame_confidence and (
                     self.preserve_word_confidence or self.preserve_token_confidence

nemo/collections/asr/parts/submodules/rnnt_decoding.py

@@ -492,7 +492,7 @@ def rnnt_decoder_predictions_tensor(
         self,
         encoder_output: torch.Tensor,
         encoded_lengths: torch.Tensor,
-        return_all_hypotheses: bool = False,
+        return_hypotheses: bool = False,
         partial_hypotheses: Optional[List[Hypothesis]] = None,
     ) -> Union[List[Hypothesis], List[List[Hypothesis]]]:
         """
@@ -541,7 +541,7 @@ def rnnt_decoder_predictions_tensor(
                 hypotheses.append(decoded_hyps[0])  # best hypothesis
                 all_hypotheses.append(decoded_hyps)
 
-            if return_all_hypotheses:
+            if return_hypotheses:
                 return all_hypotheses  # type: list[list[Hypothesis]]
 
             all_hyp = [[Hypothesis(h.score, h.y_sequence, h.text) for h in hh] for hh in all_hypotheses]
@@ -556,7 +556,7 @@ def rnnt_decoder_predictions_tensor(
                 for hyp_idx in range(len(hypotheses)):
                     hypotheses[hyp_idx] = self.compute_rnnt_timestamps(hypotheses[hyp_idx], timestamp_type)
 
-            if return_all_hypotheses:
+            if return_hypotheses:
                 # greedy decoding, can get high-level confidence scores
                 if self.preserve_frame_confidence and (
                     self.preserve_word_confidence or self.preserve_token_confidence

nemo/collections/multimodal/speech_cv/models/visual_ctc_models.py

@@ -209,7 +209,7 @@ def transcribe(
                         current_hypotheses = self.decoding.ctc_decoder_predictions_tensor(
                             logits,
                             decoder_lengths=logits_len,
-                            return_all_hypotheses=return_hypotheses,
+                            return_hypotheses=return_hypotheses,
                         )
 
                         if return_hypotheses:

nemo/collections/multimodal/speech_cv/models/visual_hybrid_rnnt_ctc_models.py

@@ -178,7 +178,7 @@ def transcribe(
                     best_hyp = self.ctc_decoding.ctc_decoder_predictions_tensor(
                         logits,
                         encoded_len,
-                        return_all_hypotheses=return_hypotheses,
+                        return_hypotheses=return_hypotheses,
                     )
                     if return_hypotheses:
                         # dump log probs per file
@@ -468,7 +468,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
         del signal
 
         best_hyp_text = self.decoding.rnnt_decoder_predictions_tensor(
-            encoder_output=encoded, encoded_lengths=encoded_len, return_all_hypotheses=False
+            encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False
         )
 
         sample_id = sample_id.cpu().detach().numpy()

nemo/collections/multimodal/speech_cv/models/visual_rnnt_models.py

@@ -289,7 +289,7 @@ def transcribe(
                     best_hyp = self.decoding.rnnt_decoder_predictions_tensor(
                         encoded,
                         encoded_len,
-                        return_all_hypotheses=return_hypotheses,
+                        return_hypotheses=return_hypotheses,
                         partial_hypotheses=partial_hypothesis,
                     )
 
@@ -725,7 +725,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
         del signal
 
         best_hyp_text = self.decoding.rnnt_decoder_predictions_tensor(
-            encoder_output=encoded, encoded_lengths=encoded_len, return_all_hypotheses=False
+            encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False
         )
 
         sample_id = sample_id.cpu().detach().numpy()

nemo/collections/tts/g2p/models/ctc.py

@@ -357,7 +357,7 @@ def _infer(
                 )
 
                 preds_str = self.decoding.ctc_decoder_predictions_tensor(
-                    log_probs, decoder_lengths=encoded_len, return_all_hypotheses=False
+                    log_probs, decoder_lengths=encoded_len, return_hypotheses=False
                 )
                 all_preds.extend(preds_str)
 

tests/collections/asr/decoding/test_ctc_decoding.py

@@ -126,7 +126,7 @@ def test_char_decoding_greedy_forward(
 
         with torch.no_grad():
             hypotheses = decoding.ctc_decoder_predictions_tensor(
-                input_signal, length, fold_consecutive=True, return_all_hypotheses=False
+                input_signal, length, fold_consecutive=True, return_hypotheses=False
             )
             texts = [hyp.text for hyp in hypotheses]
 
@@ -148,7 +148,7 @@ def test_char_decoding_greedy_forward_hypotheses(self, alignments, timestamps):
 
         with torch.no_grad():
             hyps = decoding.ctc_decoder_predictions_tensor(
-                input_signal, length, fold_consecutive=True, return_all_hypotheses=True
+                input_signal, length, fold_consecutive=True, return_hypotheses=True
             )
 
             for idx, hyp in enumerate(hyps):
@@ -179,7 +179,7 @@ def test_subword_decoding_greedy_forward(self, tmp_tokenizer):
 
         with torch.no_grad():
             hypotheses = decoding.ctc_decoder_predictions_tensor(
-                input_signal, length, fold_consecutive=True, return_all_hypotheses=False
+                input_signal, length, fold_consecutive=True, return_hypotheses=False
             )
             texts = [hyp.text for hyp in hypotheses]
 
@@ -200,7 +200,7 @@ def test_subword_decoding_greedy_forward_hypotheses(self, tmp_tokenizer, alignme
 
         with torch.no_grad():
             hyps = decoding.ctc_decoder_predictions_tensor(
-                input_signal, length, fold_consecutive=True, return_all_hypotheses=True
+                input_signal, length, fold_consecutive=True, return_hypotheses=True
             )
 
             for idx, hyp in enumerate(hyps):
@@ -286,11 +286,11 @@ def test_batched_decoding_logprobs(
 
         with torch.inference_mode():
             hyps = unbatched_decoding.ctc_decoder_predictions_tensor(
-                input_signal, length, fold_consecutive=True, return_all_hypotheses=True
+                input_signal, length, fold_consecutive=True, return_hypotheses=True
             )
 
             batched_hyps = batched_decoding.ctc_decoder_predictions_tensor(
-                input_signal, length, fold_consecutive=True, return_all_hypotheses=True
+                input_signal, length, fold_consecutive=True, return_hypotheses=True
             )
 
             assert len(hyps) == len(batched_hyps) == B
@@ -353,11 +353,11 @@ def test_batched_decoding_labels(self, tmp_tokenizer, timestamps, length_is_none
 
         with torch.inference_mode():
             hyps = unbatched_decoding.ctc_decoder_predictions_tensor(
-                input_labels, length, fold_consecutive=True, return_all_hypotheses=True
+                input_labels, length, fold_consecutive=True, return_hypotheses=True
             )
 
             batched_hyps = batched_decoding.ctc_decoder_predictions_tensor(
-                input_labels, length, fold_consecutive=True, return_all_hypotheses=True
+                input_labels, length, fold_consecutive=True, return_hypotheses=True
             )
 
             assert len(hyps) == len(batched_hyps) == B

tests/collections/asr/decoding/test_rnnt_decoding.py

@@ -438,7 +438,7 @@ def test_subword_decoding_compute_timestamps(self, test_data_dir, decoding_strat
             decoding_cfg=cfg, decoder=model.decoder, joint=model.joint, tokenizer=model.tokenizer
         )
 
-        hyps = decoding.rnnt_decoder_predictions_tensor(encoded, encoded_len, return_all_hypotheses=True)
+        hyps = decoding.rnnt_decoder_predictions_tensor(encoded, encoded_len, return_hypotheses=True)
 
         check_subword_timestamps(hyps[0], decoding)
 
@@ -473,7 +473,7 @@ def test_char_decoding_compute_timestamps(self, test_data_dir, decoding_strategy
 
         decoding = RNNTDecoding(decoding_cfg=cfg, decoder=model.decoder, joint=model.joint, vocabulary=vocab)
 
-        hyps = decoding.rnnt_decoder_predictions_tensor(encoded, encoded_len, return_all_hypotheses=True)
+        hyps = decoding.rnnt_decoder_predictions_tensor(encoded, encoded_len, return_hypotheses=True)
 
         check_char_timestamps(hyps[0], decoding)