Update README.md

README.md

@@ -7,6 +7,60 @@ base_model: pyannote/segmentation-3.0
 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 '@xenova/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