---
license: mit
base_model: Aratako/MioCodec-25Hz-24kHz
base_model_relation: adapter
language:
  - ja
  - en
tags:
  - speaker-embedding
  - webgpu
  - browser
  - voice-cloning
---

# MioCodec's speaker encoder, in one file for the browser

The global-embedding path of
[Aratako/MioCodec-25Hz-24kHz](https://huggingface.co/Aratako/MioCodec-25Hz-24kHz)'s
encoder — reference audio in, a 128-dimensional speaker embedding out —
collected into a single f32 safetensors so a browser can fetch it in one
request. Built for [voxshot](https://github.com/m96-chan/voxshot).

**This is a repackaging, not a new model.** No weight here was trained; every
tensor is copied from one of the two sources below.

| | |
| --- | ---: |
| `encoder-weights.safetensors` | 117,303,232 B, 99 tensors, 29,322,838 params |
| `encoder-weights.json` | 8,905 B — tensor index and provenance |

## Why it exists

The weights for this one path live in **two different places**: the
`GlobalEncoder` (50 tensors) is in MioCodec's own checkpoint, while the WavLM
front end it runs on top of is a torchaudio download. A browser should not
have to know that, nor fetch a 378 MB `.pth` to use 4 MB of it. So the path is
collected once, ahead of time, into one file.

Only the **global** branch is included. MioCodec's encoder also produces
content tokens — that half is not here, and is not needed for speaker
embedding.

## What the path is

```
24 kHz mono
  → symmetric zero pad
  → resample 24k→16k          (torchaudio's polyphase filter, precomputed
                               here as a conv1d kernel [2, 1, 23], stride 3)
  → WavLM feature extractor    (7 strided convs, GroupNorm on the first, GELU)
  → LayerNorm + Linear 512→768
  → positional conv            (grouped k=128, weight-norm already folded)
  → transformer layers 1 and 2 (12 heads; bucketed, gated relative-position
                               bias — the global branch reads only these two)
  → mean of the two layers
  → ConvNeXt backbone, 4 blocks, 768→384
  → attentive statistics pooling → Linear 768→128 → LayerNorm
  → 128-dim embedding
```

Two details a reimplementation gets wrong and this file cannot tell you, so
they are written down here:

- torchaudio's Base+ WavLM is **post-norm**, so an encoder-level LayerNorm runs
  between the positional-conv residual and layer 1.
- the pooling softmax is over **time**, not channels.

## Naming

WavLM tensors keep torchaudio's own names under a `wavlm.` prefix, the
`GlobalEncoder` tensors keep MioCodec's names under `global_encoder.`, and the
precomputed resampler is `resample.kernel`. `encoder-weights.json` lists every
tensor with its shape.

The positional convolution's weight normalization is **already folded** into
its weight — there is no separate `weight_g` / `weight_v` to combine.

## Provenance

Produced by [`spike/miocodec/export_encoder_weights.py`](https://github.com/m96-chan/voxshot/blob/main/spike/miocodec/export_encoder_weights.py) from:

```
Aratako/MioCodec-25Hz-24kHz  model.safetensors
  523,087,956 bytes
  sha256 60483759cde136451d53ff5a2f7e283c015758ab540de9037b5d4519366a7705

torchaudio WAVLM_BASE_PLUS  wavlm_base_plus.pth
  377,604,347 bytes
  sha256 136a3e720c04f2c77bf7a4dc6a3868b14d5a2c145a988114b733cb1a8428be98
```

Both digests are recorded in `encoder-weights.json` and cross-checked at export
time against the golden this port is verified with, so the weights and the
checkpoints they came from cannot drift apart unnoticed.

## Accuracy

The TypeScript port that consumes this file was checked stage by stage against
the reference encoder running under torch, on two real reference clips and one
synthetic input. Every stage agrees to 1e-4 relative to its own peak, except
four ConvNeXt stages on one clip which reach 1e-4 in the reference's *own*
f32-versus-f64 comparison — conditioning of that clip, not port error. The
final embedding agrees to 7.3e-7.

## License and attribution

MIT, which both sources carry.

- **MioCodec**: [Aratako/MioCodec-25Hz-24kHz](https://huggingface.co/Aratako/MioCodec-25Hz-24kHz) by Chihiro Arata, MIT.
- **WavLM Base+**: originally published by the authors of *WavLM* (Chen et al., 2022) under the MIT License and redistributed by torchaudio under the same license.
  [Source](https://github.com/microsoft/unilm/tree/65f15af2a307ebb64cfb25adf54375b002e6fe8d/wavlm#pre-trained-models) ·
  [License](https://github.com/microsoft/unilm/blob/65f15af2a307ebb64cfb25adf54375b002e6fe8d/LICENSE)

```bibtex
@article{chen2022wavlm,
  title   = {WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing},
  author  = {Chen, Sanyuan and Wang, Chengyi and Chen, Zhengyang and others},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  year    = {2022}
}
```