Understanding Whisper AI: How Modern Transcription Really Works

Published October 15, 2025 • 13 min read • By Alessandro Saladino

AI transcription seems like magic: speak into a microphone, get perfect text back. But understanding how it actually works helps you choose the right tools, optimize your setup, and know when to trust the results.

What is Whisper?

Whisper is an automatic speech recognition (ASR) model developed by OpenAI and released in September 2022. Unlike previous transcription systems, Whisper was trained on 680,000 hours of multilingual audio scraped from the internet.

Key Characteristics:

• Open source (Apache 2.0 license)
• Supports 100+ languages
• Runs entirely offline on local devices
• Multiple model sizes from tiny (39M parameters) to large (1.5B parameters)
• Trained on diverse audio: podcasts, YouTube, audiobooks, lectures

Before Whisper, accurate transcription required expensive cloud services or complex proprietary systems. Whisper democratized high-quality transcription by being free, open, and runnable locally.

How Speech Recognition Works

The Traditional Approach (Pre-2020)

Older systems used a pipeline approach:

1. Feature Extraction: Convert audio waveform to spectrograms
2. Acoustic Model: Map audio features to phonemes (sound units)
3. Language Model: Predict word sequences from phonemes
4. Decoding: Find most likely text given constraints

This approach required separate training for each language, struggled with accents, and needed large vocabularies manually defined.

The Modern Approach (Whisper and Similar)

Whisper uses an end-to-end transformer architecture:

1. Audio Preprocessing: Convert audio to 80-channel mel-spectrogram
2. Encoder: Neural network processes audio features
3. Decoder: Neural network generates text tokens autoregressively
4. Output: Complete transcript with punctuation

The model learns everything from data: language patterns, acoustic variations, punctuation, even speaker styles. No manual rules required.

Whisper Model Sizes Explained

Choosing the Right Model

Use Tiny/Base when:

• Audio is very clear (studio quality)
• Speaker has neutral accent
• Speed is critical (real-time captions)
• Running on older/slower hardware

Use Small when:

• This is the sweet spot for most use cases
• Good balance of speed and accuracy
• Works well on modern laptops
• Recommended for general transcription

Use Medium/Large when:

• Audio quality is poor (background noise, echo)
• Speaker has strong accent or speaks quickly
• Technical jargon, medical terminology, specialized vocabulary
• Accuracy is paramount (legal, medical, research)
• Hardware can handle it (GPU recommended)

How Whisper Handles Languages

Unlike older systems that needed separate models per language, Whisper is multilingual. It detects language automatically and transcribes in the source language OR translates to English.

Language Detection

Whisper analyzes the first few seconds of audio to identify the language. Accuracy:

• Very high for major languages (English, Spanish, French, German, Chinese)
• Good for most others
• Can be manually specified if detection fails

Supported Languages (99 total)

Best performance: English, Chinese, German, Spanish, Russian, French, Japanese, Portuguese, Italian, Korean

Good performance: 50+ additional languages

Experimental: Lower-resource languages

Translation vs Transcription

• Transcription: German audio → German text
• Translation: German audio → English text

Whisper can do both. Translation quality is good but not perfect—best used for general understanding, not critical translation needs.

Under the Hood: Technical Deep Dive

Mel-Spectrogram Processing

Whisper converts audio to a mel-spectrogram—a visual representation of sound that emphasizes frequencies humans hear best. This is similar to how our ears work: more sensitive to speech frequencies (300-3000 Hz) than very low or very high frequencies.

Process:

1. Resample audio to 16kHz (if necessary)
2. Apply FFT (Fast Fourier Transform) to get frequency content
3. Map frequencies to mel scale (logarithmic, like human hearing)
4. Create 80-channel spectrogram (80 frequency bands)
5. Feed into encoder

Transformer Architecture

Whisper uses the transformer architecture that revolutionized AI:

Encoder:

• Processes entire audio chunk at once (not sequential)
• Uses "attention" mechanism to focus on relevant audio features
• Creates rich representation of audio content
• Multiple layers progressively refine understanding

Decoder:

• Generates text one token at a time
• Each token influences next token (autoregressive)
• Attends to both encoder output and previous tokens
• Handles punctuation, capitalization naturally

Beam Search Decoding

Instead of always picking the most likely next word, Whisper explores multiple possibilities simultaneously:

1. Start with several candidate sequences
2. For each, predict next token
3. Keep top N most likely sequences (beam width)
4. Repeat until completion
5. Choose best overall sequence

This produces more coherent transcripts than greedy (always most likely) decoding.

Accuracy: What Affects It?

Audio Quality (Biggest Factor)

• Clean audio, quiet environment: 95-99% accuracy
• Some background noise: 90-95%
• Noisy environment, poor mic: 80-90%
• Very noisy, phone quality: 70-80%

Speaker Characteristics

• Clear enunciation: +5% accuracy
• Native speaker accent: Best performance
• Non-native but clear: -2 to -5%
• Strong regional accent: -5 to -10%
• Speech impediment/mumbling: -10 to -20%

Content Type

• Conversational speech: Best (trained heavily on this)
• Scripted/reading: Excellent
• Technical jargon: Good (depends on model size)
• Medical/legal terms: Moderate (large models better)
• Made-up words/names: Challenging (will use closest real words)

Limitations and Failure Modes

What Whisper Struggles With

1. Speaker Diarization
Whisper doesn't identify who is speaking. All speech is transcribed as continuous text without speaker labels.

Workaround: Use separate tools for diarization, or record speakers on separate channels.

2. Rare Technical Terms
Words not in training data may be transcribed as similar-sounding common words.

Example: "Kubernetes" might become "communities" if model hasn't seen the term.

Workaround: Use larger models, provide context, or use post-processing to fix known issues.

3. Hallucinations
On very quiet audio or silence, Whisper sometimes generates phantom text—usually repeated phrases.

Example: Silence might generate "Thank you for watching. Thank you for watching. Thank you for watching..."

Workaround: Voice Activity Detection (VAD) to skip silent sections.

4. Overlapping Speech
When multiple people talk simultaneously, accuracy drops significantly.

Workaround: Avoid overlapping speech in recordings when possible.

5. Very Long Audio
Whisper processes audio in 30-second chunks. Occasionally, words are cut off at chunk boundaries.

Workaround: Implementations like Whisper.cpp use overlapping windows and smart merging.

Whisper.cpp: Optimized Implementation

Tells me More uses Whisper.cpp, a high-performance C++ implementation of Whisper:

Advantages

• Speed: 2-3× faster than Python/PyTorch implementation
• Memory: Lower RAM usage
• Metal Support: GPU acceleration on Apple Silicon and Intel Macs
• No Dependencies: Self-contained, no Python environment needed
• Quantization: Smaller models without significant accuracy loss

Quantization Explained

Original Whisper models use 32-bit floating-point numbers. Quantization reduces precision to 8-bit or 16-bit integers:

• FP32 (original): Highest accuracy, largest size, slowest
• FP16: Half the size, ~same accuracy, faster on GPUs
• INT8: Quarter size, tiny accuracy loss, much faster

For most use cases, quantized models are indistinguishable from originals while being significantly faster.

Comparing Whisper to Alternatives

Whisper vs Google Speech-to-Text

Whisper vs AWS Transcribe

Similar to Google comparison: AWS Transcribe offers excellent accuracy and speaker diarization, but requires cloud upload and charges per minute. Whisper is free, private, offline.

Whisper vs Assembly AI

Assembly AI specializes in transcription with additional features (sentiment analysis, topic detection, entity recognition). More expensive, cloud-only. Whisper is simpler but free and private.

Post-Processing with LLMs

Modern transcription workflows combine Whisper with language models for enhancement:

What LLMs Fix

• Capitalization: Proper nouns, sentences
• Punctuation: Commas, periods, question marks
• Filler Words: Remove "um," "uh," "like"
• Grammar: Fix grammatical errors from speech
• Formatting: Paragraph breaks, structure
• Context Errors: "their/there/they're" based on meaning

How Tells me More Uses LLMs

Tells me More uses Llama 3.2 1B for post-processing:

1. Whisper generates raw transcript
2. LLM analyzes and corrects transcript
3. Output: polished, publication-ready text

This two-stage approach combines Whisper's speech recognition with LLM's language understanding for best results.

The Future of Transcription AI

Emerging Trends

Real-time Processing: Faster models enabling live captions with minimal delay

Multimodal Models: Combining audio with video (lip reading) for better accuracy

Emotion Detection: Recognizing tone, sentiment, speaker state

Better Diarization: Built-in speaker identification without separate tools

Tiny Models: Running sophisticated models on phones, watches, embedded devices

Whisper's Evolution

OpenAI released Whisper in 2022. Since then:

• Community created Whisper.cpp, faster-whisper, whisper-jax (speed improvements)
• Distilled models (smaller with similar accuracy)
• Fine-tuned versions for specific domains (medical, legal, etc.)
• Integration into countless applications and services

Whisper is now the de facto standard for open-source transcription.

Practical Takeaways

For Best Results

1. Audio quality matters most: Good mic + quiet room > model size
2. Choose appropriate model: Small for most, Large for challenging audio
3. Use GPU acceleration: 5-10× faster processing
4. Post-process with LLM: Significantly improves readability
5. Keep originals: AI makes mistakes—always keep source audio

Understanding Limitations

• No AI is 100% accurate—always review critical transcripts
• Rare terms, names, jargon may be wrong
• Background noise degrades quality more than model size helps
• Speaker diarization requires additional tools

Conclusion

Whisper represents a paradigm shift in transcription: from expensive, cloud-dependent services to free, private, accurate local processing. Understanding how it works helps you:

• Choose the right model for your needs
• Optimize audio quality for best results
• Know when to trust results and when to verify
• Appreciate the technology behind the magic

The combination of Whisper for transcription and LLMs for post-processing creates a powerful, private, unlimited transcription workflow that was impossible just a few years ago.

Whether you're transcribing meetings, creating content, or documenting conversations, understanding the technology helps you get better results and make informed decisions about your workflow.