Fix ASR Buffered inference scripts

README.rst

@@ -61,6 +61,7 @@ Key Features
 ------------
 
 * Speech processing
+    * `HuggingFace Space for Audio Transcription (File, Micriphone and YouTube) <https://huggingface.co/spaces/smajumdar/nemo_multilingual_language_id>`_
     * `Automatic Speech Recognition (ASR) <https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/intro.html>`_
         * Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC, ...
         * Supports CTC and Transducer/RNNT losses/decoders

nemo/collections/asr/metrics/rnnt_wer.py

@@ -445,7 +445,7 @@ def decode_hypothesis(self, hypotheses_list: List[Hypothesis]) -> List[Union[Hyp
                 # keep the original predictions, wrap with the number of repetitions per token and alignments
                 # this is done so that `rnnt_decoder_predictions_tensor()` can process this hypothesis
                 # in order to compute exact time stamps.
-                alignments = hypotheses_list[ind].alignments
+                alignments = copy.deepcopy(hypotheses_list[ind].alignments)
                 token_repetitions = [1] * len(alignments)  # preserve number of repetitions per token
                 hypothesis = (prediction, alignments, token_repetitions)
             else:

nemo/collections/asr/parts/utils/streaming_utils.py

@@ -362,7 +362,7 @@ def __init__(self, asr_model, chunk_size, buffer_size):
         '''
 
         self.NORM_CONSTANT = 1e-5
-        if asr_model.cfg.preprocessor.log:
+        if hasattr(asr_model.preprocessor, 'log') and asr_model.preprocessor.log:
             self.ZERO_LEVEL_SPEC_DB_VAL = -16.635  # Log-Melspectrogram value for zero signal
         else:
             self.ZERO_LEVEL_SPEC_DB_VAL = 0.0
@@ -576,7 +576,7 @@ def __init__(self, asr_model, frame_len=1.6, batch_size=4, total_buffer=4.0):
           frame_overlap: duration of overlaps before and after current frame, seconds
           offset: number of symbols to drop for smooth streaming
         '''
-        if asr_model.cfg.preprocessor.log:
+        if hasattr(asr_model.preprocessor, 'log') and asr_model.preprocessor.log:
             self.ZERO_LEVEL_SPEC_DB_VAL = -16.635  # Log-Melspectrogram value for zero signal
         else:
             self.ZERO_LEVEL_SPEC_DB_VAL = 0.0
@@ -963,6 +963,7 @@ def __init__(
 
         self.all_alignments = [[] for _ in range(self.batch_size)]
         self.all_preds = [[] for _ in range(self.batch_size)]
+        self.all_timestamps = [[] for _ in range(self.batch_size)]
         self.previous_hypotheses = None
         self.batch_index_map = {
             idx: idx for idx in range(self.batch_size)
@@ -990,6 +991,7 @@ def reset(self):
 
         self.all_alignments = [[] for _ in range(self.batch_size)]
         self.all_preds = [[] for _ in range(self.batch_size)]
+        self.all_timestamps = [[] for _ in range(self.batch_size)]
         self.previous_hypotheses = None
         self.batch_index_map = {idx: idx for idx in range(self.batch_size)}
 
@@ -1110,6 +1112,14 @@ def _get_batch_preds(self):
             if not has_signal_ended:
                 self.all_preds[global_index_key].append(pred.cpu().numpy())
 
+        timestamps = [hyp.timestep for hyp in best_hyp]
+        for idx, timestep in enumerate(timestamps):
+            global_index_key = new_batch_keys[idx]  # get index of this sample in the global batch
+
+            has_signal_ended = self.frame_bufferer.signal_end[global_index_key]
+            if not has_signal_ended:
+                self.all_timestamps[global_index_key].append(timestep)
+
         if self.stateful_decoding:
             # State resetting is being done on sub-batch only, global index information is not being updated
             reset_states = self.asr_model.decoder.initialize_state(encoded)