NLP / speech-audio-nlp

Speech and audio NLP

Waveforms, sampling rate, Fourier/STFT, spectrograms, Mel scale, MFCC, CTC, ASR, TTS, diarization, VAD, wav2vec, and Whisper-like models.

shellbackend needed later
Focus
  • spectrograms
  • MFCC
  • CTC alignment
  • ASR and TTS
Future feature idea

Waveform-to-spectrogram explorer with sampling rate, MFCC, CTC alignment, ASR, and TTS concepts.

Self-check
  1. What is the core job of "Speech and audio NLP"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.