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library_name: transformers
tags:
- audio
- wavcoch
- tokenizer
- neural-codec
---
# WavCochCausalV64k-20ms-babyview
WavCoch causal **tokenizer** (8192-vocab FSQ) trained on the `bad` + `bvd` corpora. Quantizes
16 kHz audio to FSQ code indices and decodes codes back to a cochleagram. This is the tokenizer
only — it does **not** render waveforms. Its codebook is distinct from
`TuKoResearch/WavCochCausalV8192`, so codes are **not** interchangeable.
- Trained checkpoint: `model_best.pt` (step 199000)
```python
from transformers import AutoModel
import torch, torchaudio
m = AutoModel.from_pretrained("TuKoResearch/WavCochCausalV64k-20ms-babyview", trust_remote_code=True).eval()
wav, sr = torchaudio.load("clip.wav") # resample to 16 kHz first if needed
codes = m.quantize(wav.unsqueeze(0)) # [1, N] FSQ indices
coch = m.decode(codes) # [1, T, out_channels] cochleagram
```
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