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AUDIO ~15 min6 concepts · 5 quiz questions

Speech AI

Whisper architecture, fine-tuning on custom speech data, building production STT pipelines.

Overview

Speech AI has undergone a revolution with OpenAI's Whisper model (2022), establishing a new standard for robust automatic speech recognition (ASR). This module covers Whisper's architecture, fine-tuning on domain-specific audio, optimizing for production with faster-whisper and WhisperX, and building end-to-end speech pipelines.

Concept Flashcards

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Concept

Whisper Architecture

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Explanation

Whisper is a sequence-to-sequence transformer trained on 680K hours of web audio. Audio is converted to 80-channel log-Mel spectrogram (30s chunks), processed by a convolutional stem + encoder, then decoded autoregressively. Special tokens handle task conditioning: <|transcribe|>, <|translate|>, <|en|>.

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System Architecture

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Whisper converts audio → log-Mel spectrogram → convolutional stem → encoder → autoregressive decoder with task conditioning tokens.

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conditioningtokensAudio Waveform(16kHz)STFT + MelFilterbankConv1D Stem(×2)TransformerEncoderTask Tokens<|en|><|transcribe|>TransformerDecoderTranscript
Audio Processing
Encoder
Decoder
Task Conditioning

Key Concepts

6 concepts — click to expand

Whisper is a sequence-to-sequence transformer trained on 680K hours of web audio. Audio is converted to 80-channel log-Mel spectrogram (30s chunks), processed by a convolutional stem + encoder, then decoded autoregressively. Special tokens handle task conditioning: <|transcribe|>, <|translate|>, <|en|>.

Tech Stack

Whisper APIHuggingFace TransformersPyTorchGradioFastAPI
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