模型架构
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.
思维模型
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.
数据流
- Waveform or spectrogram
- Acoustic / codec representation
- Encoder, seq2seq, diffusion, or token generator
- Text, labels, codec tokens, or waveform
- Task-specific decoding
训练方式
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.
推理运行方式
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.
优势
- Speech transcription and translation
- Natural speech, sound, and music synthesis
- Audio embeddings support search, moderation, and classification
权衡
- 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
适用场景
- The modality itself carries needed information
- You have task-, language-, and environment-specific evaluations
- Latency and streaming requirements are designed up front
应避免或质疑的场景
- A transcript already contains all relevant information
- Identity cloning lacks informed consent
- One benchmark is being used as proof across languages and acoustic conditions
已发表的示例系列
- • Whisper-style encoder-decoder ASR
- • AudioLM-style hierarchical codec-token generation
- • Waveform or latent diffusion audio systems