Instructions to use Xenova/wavlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/wavlm-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'Xenova/wavlm-base');
Upload folder using huggingface_hub
Browse files- config.json +1 -1
- onnx/model.onnx +2 -2
- onnx/model_quantized.onnx +2 -2
- quantize_config.json +28 -28
config.json
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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"transformers_version": "4.33.
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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"xvector_output_dim": 512
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],
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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"transformers_version": "4.33.2",
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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"xvector_output_dim": 512
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onnx/model.onnx
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size 377935358
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onnx/model_quantized.onnx
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quantize_config.json
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{
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"per_channel":
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"reduce_range":
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"per_model_config": {
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"model": {
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"op_types": [
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"Transpose",
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"Slice",
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"Greater",
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"Softmax",
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"Add"
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],
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"weight_type": "QUInt8"
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}
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{
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"per_channel": false,
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"reduce_range": false,
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"per_model_config": {
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"model": {
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"op_types": [
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"MatMul",
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"Expand",
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"Min",
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"ReduceSum",
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"Slice",
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"ReduceMean",
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"Abs",
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"Less",
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"Add",
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"Sqrt",
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"Transpose",
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"Constant",
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"Log",
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"Div",
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"Reshape",
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"Mul",
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"Concat",
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"Sub",
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"Shape",
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"Softmax",
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"Gemm",
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"Conv",
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"weight_type": "QUInt8"
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}
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