Fix merge conflicts in conv_asr

nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py

@@ -165,7 +165,7 @@ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
         )
 
         # setting the RNNT decoder as the default one
-        self.cur_decoder = "rnnt"
+        self.cur_decoder = "ctc"
 
     def _setup_dataloader_from_config(self, config: Optional[Dict]):
 

nemo/collections/asr/models/hybrid_rnnt_ctc_models.py

@@ -106,6 +106,7 @@ def transcribe(
         verbose: bool = True,
         override_config: Optional[TranscribeConfig] = None,
         language_id: str = None, #CTEMO
+        logprobs: bool = False,
     ) -> TranscriptionReturnType:
         """
         Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping.
@@ -145,7 +146,8 @@ def transcribe(
             augmentor=augmentor,
             verbose=verbose,
             override_config=override_config,
-            language_id = language_id #CTEMO
+            language_id = language_id, #CTEMO
+            logprobs=logprobs
         )
 
     def _transcribe_on_begin(self, audio, trcfg: TranscribeConfig):
@@ -161,6 +163,7 @@ def _transcribe_on_end(self, trcfg: TranscribeConfig):
             self.ctc_decoder.unfreeze()
 
     def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig):
+
         if self.cur_decoder == "rnnt":
             return super()._transcribe_forward(batch, trcfg)
 
@@ -172,7 +175,7 @@ def _transcribe_forward(self, batch: Any, trcfg: TranscribeConfig):
             language_ids = [language_id] * len(batch[0])
         logits = self.ctc_decoder(encoder_output=encoded, language_ids=language_ids)
         output = dict(logits=logits, encoded_len=encoded_len, language_ids=language_ids)
-
+        
         del encoded
         return output
 
@@ -200,9 +203,11 @@ def _transcribe_output_processing(
                     best_hyp[idx].alignments = best_hyp[idx].y_sequence
 
         # DEPRECATED?
-        # if logprobs:
-        #     for logit, elen in zip(logits, encoded_len):
-        #         logits_list.append(logit[:elen])
+        if trcfg.logprobs:
+            logits_list = []
+            for logit, elen in zip(logits, encoded_len):
+                logits_list.append(logit[:elen])
+            return logits_list
 
         del logits, encoded_len
 

nemo/collections/asr/models/rnnt_models.py

@@ -244,6 +244,7 @@ def transcribe(
         verbose: bool = True,
         language_id: str = None, #CTEMO
         override_config: Optional[TranscribeConfig] = None,
+        logprobs: bool = False,
     ) -> TranscriptionReturnType:
         """
         Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping.
@@ -281,6 +282,7 @@ def transcribe(
             augmentor=augmentor,
             verbose=verbose,
             override_config=override_config,
+            logprobs=logprobs,
             # Additional arguments
             partial_hypothesis=partial_hypothesis,
         )

nemo/collections/asr/modules/audio_preprocessing.py

@@ -280,8 +280,8 @@ def __init__(
             stft_conv=stft_conv,  # Deprecated arguments; kept for config compatibility
         )
 
-    def input_example(self, max_batch: int = 8, max_dim: int = 32000, min_length: int = 200):
-        batch_size = torch.randint(low=1, high=max_batch, size=[1]).item()
+    def input_example(self, max_batch: int = 1, max_dim: int = 32000, min_length: int = 200):
+        batch_size = 1 # torch.randint(low=1, high=max_batch, size=[1]).item()
         max_length = torch.randint(low=min_length, high=max_dim, size=[1]).item()
         signals = torch.rand(size=[batch_size, max_length]) * 2 - 1
         lengths = torch.randint(low=min_length, high=max_dim, size=[batch_size])

nemo/collections/asr/modules/conv_asr.py

@@ -453,6 +453,7 @@ def __init__(self, feat_in, num_classes, init_mode="xavier_uniform", vocabulary=
         self.temperature = 1.0
         self.multisoftmax = multisoftmax
         self.language_masks = language_masks
+
     @typecheck()
     def forward(self, encoder_output, language_ids=None): #CTEMO
         # Adapter module forward step

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

@@ -60,6 +60,7 @@ class TranscribeConfig:
     channel_selector: ChannelSelectorType = None
     augmentor: Optional[DictConfig] = None
     verbose: bool = True
+    logprobs: bool = False
 
     # Utility
     partial_hypothesis: Optional[List[Any]] = None
@@ -194,6 +195,7 @@ def transcribe(
         augmentor: DictConfig = None,
         verbose: bool = True,
         override_config: Optional[TranscribeConfig] = None,
+        logprobs: bool = False,
         **config_kwargs,
     ) -> GenericTranscriptionType:
         """
@@ -242,6 +244,7 @@ def transcribe(
                 channel_selector=channel_selector,
                 augmentor=augmentor,
                 verbose=verbose,
+                logprobs=logprobs,
                 **config_kwargs,
             )
         else:

nemo/collections/asr/parts/preprocessing/features.py

@@ -63,12 +63,12 @@ def normalize_batch(x, seq_len, normalize_type):
         x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device)
         x_std = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device)
         for i in range(x.shape[0]):
-            if x[i, :, : seq_len[i]].shape[1] == 1:
-                raise ValueError(
-                    "normalize_batch with `per_feature` normalize_type received a tensor of length 1. This will result "
-                    "in torch.std() returning nan. Make sure your audio length has enough samples for a single "
-                    "feature (ex. at least `hop_length` for Mel Spectrograms)."
-                )
+            # if x[i, :, : seq_len[i]].shape[1] == 1:
+            #     raise ValueError(
+            #         "normalize_batch with `per_feature` normalize_type received a tensor of length 1. This will result "
+            #         "in torch.std() returning nan. Make sure your audio length has enough samples for a single "
+            #         "feature (ex. at least `hop_length` for Mel Spectrograms)."
+            #     )
             x_mean[i, :] = x[i, :, : seq_len[i]].mean(dim=1)
             x_std[i, :] = x[i, :, : seq_len[i]].std(dim=1)
         # make sure x_std is not zero