README.md

---
license: mit
library_name: transformers.js
base_model: pyannote/segmentation-3.0
---

https://huggingface.co/pyannote/segmentation-3.0 with ONNX weights to be compatible with Transformers.js.


## Torch → ONNX conversion code:
```py
# pip install torch onnx https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip
import torch
from pyannote.audio import Model

model = Model.from_pretrained(
  "pyannote/segmentation-3.0", 
  use_auth_token="hf_...", # <-- Set your HF token here
).eval()

dummy_input = torch.zeros(2, 1, 160000)
torch.onnx.export(
    model,
    dummy_input,
    'model.onnx',
    do_constant_folding=True,
    input_names=["input_values"],
    output_names=["logits"],
    dynamic_axes={
        "input_values": {0: "batch_size", 1: "num_channels", 2: "num_samples"},
        "logits": {0: "batch_size", 1: "num_frames"},
    },
)
```

---


Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).