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?

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Deep Learning Practice Quiz 2: 3 August 2025 (May 2025 term)

Question 1

+3 marksOne correct option

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?

Question 2

+3 marksOne correct option

Consider the following code snippet for using a pretrained Wav2Vec2 model:

python

1from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
2import torch
3
4processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
5model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
6
7# Pretend this is audio data
8input_values = torch.randn(16000)  # 1 second of fake audio
9
10# Forward pass
11logits = model(input_values).logits

What is the primary issue in the above code?

Question 3

+5 marksOne or more correct options

Which statements accurately describe the roles and differences of the data collators used in the ASR and TTS training scripts?

Select all that apply.

8 more questions in this paper

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More on the Deep Learning Practice Quiz 2 3 Aug 2025 paper

The IIT Madras BS Deep Learning Practice (Deep Learning Practice) Quiz 2 paper sat on 3 Aug 2025, in the May 2025 term: 11 questions for 50 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

Feature	Deep Learning Practice Quiz 2 3 Aug 2025 at a glance
Term	May 2025 term
Subject	Deep Learning Practice
Course code	BSDA5013
Questions	11
Marks	50
Duration	120 min
MCQ	4
MSQ	4
Numerical	3
Official paper	IIT M IMPROVEMENT AN EXAM QIM2 03 Aug 2025
Negative marking	No negative marking.
Updated	25 Sept 2026