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---
base_model: pyannote/segmentation-3.0
library_name: transformers.js
license: mit
---
https://huggingface.co/pyannote/segmentation-3.0 with ONNX weights to be compatible with Transformers.js.
## Transformers.js (v3) usage
```js
import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers';
// Load model and processor
const model_id = 'onnx-community/pyannote-segmentation-3.0';
const model = await AutoModelForAudioFrameClassification.from_pretrained(model_id);
const processor = await AutoProcessor.from_pretrained(model_id);
// Read and preprocess audio
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/mlk.wav';
const audio = await read_audio(url, processor.feature_extractor.config.sampling_rate);
const inputs = await processor(audio);
// Run model with inputs
const { logits } = await model(inputs);
// {
// logits: Tensor {
// dims: [ 1, 767, 7 ], // [batch_size, num_frames, num_classes]
// type: 'float32',
// data: Float32Array(5369) [ ... ],
// size: 5369
// }
// }
const result = processor.post_process_speaker_diarization(logits, audio.length);
// [
// [
// { id: 0, start: 0, end: 1.0512535626298245, confidence: 0.8220156481664611 },
// { id: 2, start: 1.0512535626298245, end: 2.3398869619825127, confidence: 0.9008811707860472 },
// ...
// ]
// ]
// Display result
console.table(result[0], ['start', 'end', 'id', 'confidence']);
// ┌─────────┬────────────────────┬────────────────────┬────┬─────────────────────┐
// │ (index) │ start │ end │ id │ confidence │
// ├─────────┼────────────────────┼────────────────────┼────┼─────────────────────┤
// │ 0 │ 0 │ 1.0512535626298245 │ 0 │ 0.8220156481664611 │
// │ 1 │ 1.0512535626298245 │ 2.3398869619825127 │ 2 │ 0.9008811707860472 │
// │ 2 │ 2.3398869619825127 │ 3.5946089560890773 │ 0 │ 0.7521651315796233 │
// │ 3 │ 3.5946089560890773 │ 4.578039708226655 │ 2 │ 0.8491978128022479 │
// │ 4 │ 4.578039708226655 │ 4.594995410849717 │ 0 │ 0.2935352600416393 │
// │ 5 │ 4.594995410849717 │ 6.121008646925269 │ 3 │ 0.6788051309866024 │
// │ 6 │ 6.121008646925269 │ 6.256654267909762 │ 0 │ 0.37125512393851134 │
// │ 7 │ 6.256654267909762 │ 8.630452635138397 │ 2 │ 0.7467035186353542 │
// │ 8 │ 8.630452635138397 │ 10.088643060721703 │ 0 │ 0.7689364814666032 │
// │ 9 │ 10.088643060721703 │ 12.58113134631177 │ 2 │ 0.9123324509131324 │
// │ 10 │ 12.58113134631177 │ 13.005023911888312 │ 0 │ 0.4828358177572041 │
// └─────────┴────────────────────┴────────────────────┴────┴─────────────────────┘
```
## 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`). |