Opening the paper…
Deep Learning Practice, Quiz 2
In a speaker diarization pipeline that uses Whisper for transcription and an embedding model for speaker identification,you observe that one speaker's segments are consistently broken into multiple smaller segments, each assigned a different speaker label (e.g., SPEAKER 1, SPEAKER 3, SPEAKER 5). The transcribed text for this speaker is perfectly accurate. What is the MOST likely cause of this specific issue?
In a speaker diarization pipeline that uses Whisper for transcription and an embedding model for speaker identification,you observe that one speaker's segments are consistently broken into multiple smaller segments, each assigned a different speaker label (e.g., SPEAKER 1, SPEAKER 3, SPEAKER 5). The transcribed text for this speaker is perfectly accurate. What is the MOST likely cause of this specific issue? Consider the following code snippet for using a pretrained Wav2Vec2 model: from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import torch processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") # Pretend this is audio data input_values = torch.randn(16000) # 1 second of fake audio # Forward pass logits = model(input_values).logits What is the primary issue in the above code? Which statements accurately describe the roles and differences of the data collators used in the ASR and TTS training scripts?