Audio transcription (Whisper) in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the Whisper model for transcription.

Prompt Engineering / GenAI

import whisper
model = whisper.load_model([1])

Drag options to blanks, or click blank then click option'

A"tiny"

B"fast"

C"large-v2"

D"small"

Attempts:

3 left

💡 Hint

Common Mistakes

Using an invalid model name like 'fast' which does not exist.

Forgetting to put the model name in quotes.

The 'tiny' model is the smallest Whisper model and is loaded with whisper.load_model("tiny").

2fill in blank

medium

Complete the code to transcribe audio using the Whisper model.

Prompt Engineering / GenAI

result = model.transcribe([1])

Drag options to blanks, or click blank then click option'

Atranscription

Baudio_data

C"audio.wav"

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the model object instead of the audio filename.

Passing a variable that is not the audio file path.

The transcribe method takes the path to the audio file as a string, e.g., "audio.wav".

3fill in blank

hard

Fix the error in accessing the transcribed text from the result dictionary.

Prompt Engineering / GenAI

text = result[[1]]

Drag options to blanks, or click blank then click option'

A"transcription"

B"text"

C"result"

D"output"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transcription' or 'output' as keys which do not exist.

Trying to access the result as an attribute instead of a dictionary key.

The transcribed text is stored under the key 'text' in the result dictionary.

4fill in blank

hard

Fill both blanks to load the model and transcribe an audio file.

Prompt Engineering / GenAI

model = whisper.load_model([1])
result = model.transcribe([2])

Drag options to blanks, or click blank then click option'

A"base"

B"audio.mp3"

C"audio.wav"

D"large"

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing model names and audio file names.

Using unsupported audio file formats.

Load the 'base' model and transcribe the audio file 'audio.wav'.

5fill in blank

hard

Fill all three blanks to extract the transcribed text and print it.

Prompt Engineering / GenAI

model = whisper.load_model([1])
result = model.transcribe([2])
print(result[[3]])

Drag options to blanks, or click blank then click option'

A"small"

B"speech.mp3"

C"text"

D"tiny"

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong keys to access the transcription.

Passing incorrect file names or model names.

Load the 'tiny' model, transcribe 'speech.mp3', and print the 'text' key from the result.

Practice

1. What is the main purpose of the Whisper model in audio transcription?

easy

A. Translate text from one language to another

B. Convert spoken words in audio files into written text

C. Generate music from text descriptions

D. Detect objects in images

Solution

1. Step 1: Understand Whisper's function

   Whisper is designed to listen to audio and write down what it hears as text.

2. Step 2: Compare options to Whisper's purpose

   Only Convert spoken words in audio files into written text matches this function; others describe unrelated tasks.

3. Final Answer:

   Convert spoken words in audio files into written text -> Option B

4. Quick Check:

   Whisper transcribes speech to text [OK]

Hint: Whisper turns speech into text, not images or translations [OK]

Common Mistakes:

• Confusing transcription with translation
• Thinking Whisper generates images or music
• Mixing audio transcription with image recognition

2. Which of the following is the correct way to call the Whisper model's transcription method in Python?

easy

A. model.audio_transcribe()

B. model.transcript(audio_file)

C. model.transcribe_audio(audio_file)

D. model.transcribe(audio_file)

Solution

1. Step 1: Recall the official Whisper method name

   The method to get text from audio is called transcribe().

2. Step 2: Match method call syntax

   model.transcribe(audio_file) uses model.transcribe(audio_file), which is correct syntax.

3. Final Answer:

   model.transcribe(audio_file) -> Option D

4. Quick Check:

   Use transcribe() method for transcription [OK]

Hint: Remember method name is exactly 'transcribe' with parentheses [OK]

Common Mistakes:

• Using incorrect method names like 'transcript' or 'transcribe_audio'
• Omitting parentheses when calling the method
• Confusing method with attribute access

3. Given the following Python code using Whisper, what will be the output type of result?

model = whisper.load_model('small')
audio_path = 'speech.mp3'
result = model.transcribe(audio_path)
print(type(result))

medium

A.

B.

C.

D.

Solution

1. Step 1: Understand the output of transcribe()

   The transcribe() method returns a dictionary containing keys like 'text' with the transcription.

2. Step 2: Identify the Python type of the output

   Since the output holds multiple pieces of information, it is a dict, not a string or list.

3. Final Answer:

   <class 'dict'> -> Option C

4. Quick Check:

   Whisper transcribe returns dict with transcription text [OK]

Hint: Whisper returns a dict with keys, not just a string [OK]

Common Mistakes:

• Assuming output is a plain string of text
• Thinking output is a list of words
• Confusing tuple with dictionary

4. You run this code but get an error:

model = whisper.load_model('medium')
result = model.transcribe()

What is the likely cause of the error?

medium

A. Missing audio file argument in transcribe() call

B. Model size 'medium' is not supported

C. transcribe() method does not exist

D. Audio file path is incorrect

Solution

1. Step 1: Check method call requirements

   The transcribe() method requires an audio file path argument to process.

2. Step 2: Identify missing argument

   The code calls transcribe() without any argument, causing an error.

3. Final Answer:

   Missing audio file argument in transcribe() call -> Option A

4. Quick Check:

   transcribe() needs audio file input [OK]

Hint: Always pass audio file path to transcribe() [OK]

Common Mistakes:

• Forgetting to provide audio file argument
• Assuming model size 'medium' is invalid
• Thinking transcribe() needs no arguments

5. You want to transcribe a long audio file quickly but can accept slightly less accuracy. Which Whisper model size should you choose?

hard

A. tiny

B. medium

C. large

D. small

Solution

1. Step 1: Understand model size trade-offs

   Smaller models like 'tiny' are fastest but less accurate; larger models are slower but more accurate.

2. Step 2: Choose model balancing speed and accuracy

   'tiny' model offers the fastest transcription speed with acceptable accuracy trade-off for long audio.

3. Final Answer:

   tiny -> Option A

4. Quick Check:

   Choose 'tiny' for fastest transcription with some accuracy loss [OK]

Hint: Pick 'tiny' for fastest transcription with some accuracy trade-off [OK]

Common Mistakes:

• Choosing 'small' expecting fastest speed
• Picking 'large' for speed
• Confusing 'medium' as fastest