---
license: apache-2.0
tags:
- audio
- speech
- tokenizer
- vocoder
- wavcoch
library_name: transformers
---

# WavCochCausalV64000100M

**WavCoch** is a causal waveform-to-cochleagram tokenizer by **Greta Tuckute** and **Klemen Kotar**.



## Model Details

| Parameter | Value |
|-----------|-------|
| Parameters | ~93.05M |
| Window Size | 1001 |
| Hop Length | 80 |
| Encoder Dim | 1536 |
| Vocabulary Size | 64000 |
| Includes Vocoder | False |

## Usage

```python
from transformers import AutoModel

wavcoch = AutoModel.from_pretrained(
    "TuKoResearch/WavCochCausalV64000100M",
    trust_remote_code=True,
)

codes = wavcoch.quantize(waveform_tensor)
coch = wavcoch.decode(codes)
embeddings = wavcoch(
    input_values=waveform_tensor,
    output_hidden_states=True,
    sampling_rate=16000,
).hidden_states[0]
```

## Notes

This repo contains the WavCoch tokenizer/autoencoder only. Audio decoding requires a vocoder-enabled checkpoint.

When called with `output_hidden_states=True`, WavCoch exposes a single hidden-state layer:
the post-FSQ projected embedding sequence used for direct probing.