Update private_README.md

private_README.md

@@ -265,7 +265,7 @@ from nemo.collections.asr.models import SortformerEncLabelModel, ASRModel
 import torch
 # A speaker diarization model is needed for tracking the speech activity of each speaker.
 diar_model = SortformerEncLabelModel.from_pretrained("nvidia/diar_streaming_sortformer_4spk-v2.1").eval().to(torch.device("cuda"))
-asr_model = ASRModel.from_pretrained("nvidia/multitalker-parakeet-streaming-0.6b-v1.nemo").eval().to(torch.device("cuda"))
+asr_model = ASRModel.from_pretrained("nvidia/multitalker-parakeet-streaming-0.6b-v1").eval().to(torch.device("cuda"))
 
 # Use the pre-defined dataclass template `MultitalkerTranscriptionConfig` from `multitalker_transcript_config.py`. 
 # Configure the diarization model using streaming parameters:
@@ -309,7 +309,7 @@ for step_num, (chunk_audio, chunk_lengths) in enumerate(streaming_buffer_iter):
                     is_buffer_empty=streaming_buffer.is_buffer_empty(),
                     drop_extra_pre_encoded=drop_extra_pre_encoded,
                 )
-
+                print(multispk_asr_streamer.instance_manager.batch_asr_states[0].seglsts)
 # Generate the speaker-tagged transcript and print it.
 multispk_asr_streamer.generate_seglst_dicts_from_parallel_streaming(samples=samples)
 print(multispk_asr_streamer.instance_manager.seglst_dict_list)