Fixing an issue with confidence ensembles

examples/asr/transcribe_speech.py

@@ -130,6 +130,8 @@ class TranscriptionConfig:
 
     # Set to True to output greedy timestamp information (only supported models)
     compute_timestamps: bool = False
+    # set to True if need to return full alignment information
+    preserve_alignment: bool = False
 
     # Set to True to output language ID information
     compute_langs: bool = False
@@ -230,6 +232,8 @@ def main(cfg: TranscriptionConfig) -> Union[TranscriptionConfig, List[Hypothesis
     # we will adjust this flag if the model does not support it
     compute_timestamps = cfg.compute_timestamps
     compute_langs = cfg.compute_langs
+    # has to be True if timestamps are required
+    preserve_alignment = True if cfg.compute_timestamps else cfg.preserve_alignment
 
     # Check whether model and decoder type match
     if isinstance(asr_model, EncDecCTCModel):
@@ -252,7 +256,7 @@ def main(cfg: TranscriptionConfig) -> Union[TranscriptionConfig, List[Hypothesis
             decoding_cfg = cfg.rnnt_decoding if cfg.decoder_type == 'rnnt' else cfg.ctc_decoding
             decoding_cfg.compute_timestamps = cfg.compute_timestamps  # both ctc and rnnt support it
             if 'preserve_alignments' in decoding_cfg:
-                decoding_cfg.preserve_alignments = cfg.compute_timestamps
+                decoding_cfg.preserve_alignments = preserve_alignment
             if 'compute_langs' in decoding_cfg:
                 decoding_cfg.compute_langs = cfg.compute_langs
             if hasattr(asr_model, 'cur_decoder'):
@@ -267,7 +271,7 @@ def main(cfg: TranscriptionConfig) -> Union[TranscriptionConfig, List[Hypothesis
             cfg.rnnt_decoding.compute_langs = cfg.compute_langs
 
             if 'preserve_alignments' in cfg.rnnt_decoding:
-                cfg.rnnt_decoding.preserve_alignments = cfg.compute_timestamps
+                cfg.rnnt_decoding.preserve_alignments = preserve_alignment
 
             asr_model.change_decoding_strategy(cfg.rnnt_decoding)
         else:

nemo/collections/asr/models/confidence_ensemble.py

@@ -106,6 +106,11 @@ def get_filtered_logprobs(hypothesis: Hypothesis, exclude_blank: bool) -> torch.
                 filtered_logprobs = logprobs[:1]
         else:
             filtered_logprobs = logprobs
+
+    # need to make sure logprobs are always normalized, so checking if they sum up to 1
+    if not torch.allclose(filtered_logprobs[0].exp().sum(), torch.tensor(1.0)):
+        filtered_logprobs = torch.log_softmax(filtered_logprobs, dim=1)
+
     return filtered_logprobs
 
 
@@ -217,10 +222,6 @@ def update_decoding_parameters(self, decoding_cfg: DictConfig):
         with open_dict(decoding_cfg):
             decoding_cfg.temperature = self.cfg.temperature
             decoding_cfg.preserve_alignments = True
-            if 'confidence_cfg' in decoding_cfg:
-                decoding_cfg.confidence_cfg.preserve_frame_confidence = True
-            else:
-                decoding_cfg.confidence_cfg = ConfidenceConfig(preserve_frame_confidence=True)
 
     def setup_training_data(self, train_data_config: Union[DictConfig, Dict]):
         """Pass-through to the ensemble models.

scripts/confidence_ensembles/build_ensemble.py

@@ -458,7 +458,7 @@ def find_best_confidence(
     return best_conf_spec.to_confidence_config(), best_pipe
 
 
-@hydra_runner(schema=BuildEnsembleConfig)
+@hydra_runner(config_name="BuildEnsembleConfig", schema=BuildEnsembleConfig)
 def main(cfg: BuildEnsembleConfig):
     # silencing all messages from nemo/ptl to avoid dumping tons of configs to the stdout
     logging.getLogger('pytorch_lightning').setLevel(logging.CRITICAL)
@@ -471,12 +471,10 @@ def main(cfg: BuildEnsembleConfig):
     pl.seed_everything(cfg.random_seed)
     cfg.transcription.random_seed = None  # seed is already applied
     cfg.transcription.return_transcriptions = True
-    # that sets preserve_alignment to True
-    cfg.transcription.compute_timestamps = True
+    cfg.transcription.preserve_alignment = True
     cfg.transcription.ctc_decoding.temperature = cfg.temperature
     cfg.transcription.rnnt_decoding.temperature = cfg.temperature
     # this ensures that generated output is after log-softmax for consistency with CTC
-    cfg.transcription.rnnt_decoding.confidence_cfg.preserve_frame_confidence = True
 
     train_confidences = []
     dev_confidences = []

scripts/confidence_ensembles/test_confidence_ensembles.py

@@ -113,4 +113,4 @@ def test_confidence_ensemble(tmp_path, build_args):
     )
 
     results = speech_to_text_eval.main(eval_cfg)
-    assert results.metric_value < 0.15  # relaxed check for better than 15% WER
+    assert results.metric_value < 0.20  # relaxed check for better than 20% WER