---
license: cc-by-nc-4.0
base_model: litagin/anime_speaker_embedding_ecapa_tdnn_groupnorm
pipeline_tag: audio-classification
tags:
  - speaker-verification
  - character-identity
  - cross-lingual
  - anime
  - speech
language:
  - ja
  - en
---

# KyaraEmbed

An encoder for anime **character** identity rather than speaker identity. Given an audio clip, it
returns a 192-dimensional embedding whose cosine similarity follows the character across a change
of performer, and separates characters who share one.

Fine-tuned from a public anime-domain ECAPA-TDNN. Evaluation numbers and the benchmark this was
built for live in the [KyaraBench repository](https://github.com/sizigi/kyarabench).

## Checkpoint

| File | Size | Notes |
|---|---|---|
| `kyaraembed_v1.0.pt` | 83 MB | ECAPA-TDNN, 20.8M parameters, 192-d output |
| `kyaraembed_v1.0.pt.sha256` | — | Checksum |
| `LICENSE-MODEL`, `NOTICE` | — | Licence and the upstream attribution chain |

## How to use

```bash
pip install torch soundfile librosa anime-speaker-embedding
```

```python
import numpy as np, torch, soundfile as sf, librosa
from anime_speaker_embedding import AnimeSpeakerEmbedding

device = "cuda" if torch.cuda.is_available() else "cpu"
model = AnimeSpeakerEmbedding(device=device, variant="char")
model.load_state_dict(torch.load("kyaraembed_v1.0.pt", map_location=device))
model.eval()

def load(path, seconds=10.0, sr=16000):
    """Mono 16 kHz at a fixed length: longer is truncated, shorter is zero-padded."""
    wav, file_sr = sf.read(path, dtype="float32", always_2d=True)
    wav = wav.mean(axis=1)
    if file_sr != sr:
        wav = librosa.resample(wav, orig_sr=file_sr, target_sr=sr)
    out = np.zeros(int(seconds * sr), dtype="float32")
    wav = wav[:len(out)]
    out[:len(wav)] = wav
    return out

x = torch.from_numpy(np.stack([load("a.wav"), load("b.wav")])).to(device)
with torch.no_grad():
    z = torch.nn.functional.normalize(model(x).squeeze(1), dim=-1)
print(f"similarity: {float(z[0] @ z[1]):.3f}")
```

Keep the fixed length. The encoder pools over time, so padding a clip to whatever else happens to
be in its batch changes its embedding; padding to a fixed length does not.

For several clips per side, mean-pool the normalised embeddings and renormalise before the cosine:

```python
def prototype(paths):
    x = torch.from_numpy(np.stack([load(p) for p in paths])).to(device)
    with torch.no_grad():
        z = torch.nn.functional.normalize(model(x).squeeze(1), dim=-1)
    return torch.nn.functional.normalize(z.mean(0), dim=-1)
```

## Licence

CC BY-NC 4.0. Part of the audio behind these weights is held under terms permitting
non-commercial research only. See `LICENSE-MODEL` and `NOTICE`.