Audio transcription (Whisper) in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Whisper in the context of audio transcription?

Whisper is an AI model developed by OpenAI that converts spoken language in audio files into written text, helping computers understand speech.

intermediate

How does Whisper handle different languages and accents?

Whisper is trained on many languages and accents, so it can understand and transcribe speech from diverse speakers with good accuracy.

beginner

What is the main output of the Whisper model?

The main output is the text transcription of the spoken words in the audio, often including timestamps and confidence scores.

intermediate

Why is Whisper considered robust for noisy audio?

Whisper was trained on a large variety of audio, including noisy and low-quality recordings, making it good at understanding speech even with background noise.

beginner

What are common uses of Whisper in real life?

Whisper is used for creating subtitles, voice assistants, transcribing meetings, and helping people with hearing difficulties by converting speech to text.

What does Whisper primarily do?

A Generate music from text

B Convert audio speech to text

C Translate text between languages

D Detect objects in images

Whisper is designed to convert spoken audio into written text.

Which feature helps Whisper work well with different accents?

A Training on diverse languages and accents

B Using only English audio

C Ignoring background noise

D Manual transcription correction

Whisper is trained on many languages and accents, improving its understanding.

What kind of data was Whisper trained on to improve noise handling?

A Noisy and low-quality audio

B Images and videos

C Text documents

D Only clean studio recordings

Whisper was trained on noisy and low-quality audio to be robust in real-world conditions.

Which of these is NOT a typical use of Whisper?

A Creating subtitles for videos

B Helping voice assistants understand speech

C Transcribing meetings

D Generating 3D models

Whisper does not generate 3D models; it transcribes speech to text.

What extra information can Whisper provide besides text?

A Audio volume levels

B Video frames

C Timestamps and confidence scores

D Speaker emotions

Whisper can output timestamps for words and confidence scores for accuracy.

Explain how Whisper converts audio speech into text and why it is useful.

Think about how computers listen and write down what they hear.

Describe the training data characteristics that make Whisper robust to noisy audio.

Consider what kind of sounds Whisper learned from.

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