Comparison Table
| Codec | Flat Token Rate (tok/sec) | Codebooks | Framerate (Hz) | Vocab Size |
|---|---|---|---|---|
| DAC | 801 | 9 | 89 | 1024 |
| SNAC 24kHz | ~150 | 3 | varies | 4096 |
| WavTokenizer | 40 or 75 | 1 | — | 4096 |
| X-Codec2 | ~50 | 1 | — | 65536 |
Flat Token Rate — codecs produce a (codebooks × timesteps) matrix. Flattened into a 1D sequence for autoregressive modeling, the total tokens/sec is codebooks × framerate.
DAC produces 9 codebooks at 89 frames/sec → 801 tok/sec. High quality, but sequences are ~10× longer than WavTokenizer for the same audio duration. Memory-intensive for LLM training.
SNAC matches DAC in perceptual quality with ~3–6× lower token rate. Best general-purpose choice for speech tasks.
WavTokenizer achieves extreme compression (40–75 tok/sec, single codebook). Excellent for clean TTS tasks. Performs poorly on degraded/noisy speech — it was not trained on such data and can introduce artifacts.
X-Codec2 — stable at version 1.3.0. Single codebook, low token rate, large vocabulary. Pairs well with Llasa for TTS.
When to Use Each
| Task | Recommended Codec |
|---|---|
| Speech enhancement (noisy input) | SNAC or DAC |
| TTS / zero-shot voice cloning | X-Codec2 or WavTokenizer |
| Speech editing | DAC (fine-grained control) |
| Autoregressive LM on long audio | WavTokenizer or X-Codec2 |
| Codec quality research | DAC (richest representation) |