Merged the latest nemo main

Signed-off-by: Taejin Park <tango4j@gmail.com>
chimechallenge · Feb 9, 2024 · 1ef50cd · 1ef50cd
1 parent fa0495a
commit 1ef50cd
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Showing 3 changed files with 7 additions and 10 deletions.
diff --git a/nemo/collections/asr/metrics/der.py b/nemo/collections/asr/metrics/der.py
@@ -182,13 +182,11 @@ def score_labels(
         itemized_errors = (DER, CER, FA, MISS)
 
         if verbose:
-            # logging.info(f"\n{metric.report()}")
-            pass
-        logging.info(
-            "Cumulative Results for collar {} sec and ignore_overlap {}: \n| FA: {:.4f} | MISS: {:.4f} | CER: {:.4f} | DER: {:.4f} | Spk. Count Acc. {:.4f}\n".format(
-                collar, ignore_overlap, FA, MISS, CER, DER, spk_count_acc
+            logging.info(
+                "Cumulative Results for collar {} sec and ignore_overlap {}: \n| FA: {:.4f} | MISS: {:.4f} | CER: {:.4f} | DER: {:.4f} | Spk. Count Acc. {:.4f}\n".format(
+                    collar, ignore_overlap, FA, MISS, CER, DER, spk_count_acc
+                )
             )
-        )
 
         return metric, mapping_dict, itemized_errors
     elif verbose:

diff --git a/nemo/collections/asr/models/msdd_v2_models.py b/nemo/collections/asr/models/msdd_v2_models.py
@@ -1669,7 +1669,7 @@ def __init__(self, cfg: Union[DictConfig, NeuralDiarizerInferenceConfig], msdd_m
         self.use_adaptive_thres = cfg.diarizer.msdd_model.parameters.get('use_adaptive_thres', True)
         self.max_pred_length = cfg.diarizer.msdd_model.parameters.get('max_pred_length', 0)
         self.diar_eval_settings = cfg.diarizer.msdd_model.parameters.get(
-            'diar_eval_settings', [(0.25, False), (0.25, True)]
+            'diar_eval_settings', [(0.25, False)]
         )
         if msdd_model is not None:
             self.msdd_model = msdd_model
@@ -2164,7 +2164,6 @@ def run_overlap_aware_eval(
             hop_len_in_cs=int(self.feat_per_sec * self.msdd_model.cfg.interpolated_scale/2),
             ts_vad_threshold=self._cfg.diarizer.msdd_model.parameters.ts_vad_threshold,
         )
-
         for k, (collar, ignore_overlap) in enumerate(self.diar_eval_settings):
             output = score_labels(
                 rttm_map,

diff --git a/scripts/chime7/pipeline/asr/run_asr.py b/scripts/chime7/pipeline/asr/run_asr.py
@@ -267,13 +267,13 @@ def autocast():
                     )
                 else:
                     transcriptions = asr_model.transcribe(
-                        paths2audio_files=filepaths,
+                        audio=filepaths,
                         batch_size=cfg.batch_size,
                         num_workers=cfg.num_workers,
                         return_hypotheses=return_hypotheses,
                         channel_selector=cfg.channel_selector,
                         augmentor=augmentor,
-                        normalize_db=cfg.normalize_db,
+                        # normalize_db=cfg.normalize_db,
                     )
 
         logging.info(f"Finished transcribing {len(filepaths)} files !")