Model Architectures
Audio, speech & music models
A family of task-specific pipelines: speech recognition, speech synthesis, audio understanding, voice conversion, sound generation, and music generation are not one architecture.
Mental model
Choose the task first. ASR maps audio to text; TTS and music generation map conditions to acoustic or codec representations; understanding maps audio to labels or embeddings.
Data flow
- Waveform or spectrogram
- Acoustic / codec representation
- Encoder, seq2seq, diffusion, or token generator
- Text, labels, codec tokens, or waveform
- Task-specific decoding
How it trains
Objectives vary: transcription uses sequence likelihood, representation learning may mask audio, codec generators predict discrete audio tokens, and diffusion systems learn denoising in waveform, spectrogram, or latent space.
How inference runs
ASR usually encodes audio then decodes text; generation may predict codec tokens autoregressively or iteratively denoise. Streaming constraints can require chunked encoders and bounded look-ahead.
Strengths
- Speech transcription and translation
- Natural speech, sound, and music synthesis
- Audio embeddings support search, moderation, and classification
Trade-offs
- Accents, noise, overlap, code-switching, and domain vocabulary shift quality
- Long-form generation needs temporal structure and consistency
- Voice identity, consent, watermarking, and copyright need explicit governance
Use it when
- The modality itself carries needed information
- You have task-, language-, and environment-specific evaluations
- Latency and streaming requirements are designed up front
Avoid or challenge it when
- A transcript already contains all relevant information
- Identity cloning lacks informed consent
- One benchmark is being used as proof across languages and acoustic conditions
Illustrative published families
- • Whisper-style encoder-decoder ASR
- • AudioLM-style hierarchical codec-token generation
- • Waveform or latent diffusion audio systems