Audio Classification
Transformers.js
ONNX
Transformers
PyTorch
English
wav2vec2
audio
musical-instruments
Eval Results (legacy)
Instructions to use onnx-community/Musical-Instrument-Classification-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/Musical-Instrument-Classification-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('audio-classification', 'onnx-community/Musical-Instrument-Classification-ONNX'); - Transformers
How to use onnx-community/Musical-Instrument-Classification-ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="onnx-community/Musical-Instrument-Classification-ONNX")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("onnx-community/Musical-Instrument-Classification-ONNX") model = AutoModelForAudioClassification.from_pretrained("onnx-community/Musical-Instrument-Classification-ONNX", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 740eefcddc687e9d41bf9ba67f9ff330334ec08b13a25f90cefcd48b0f4d4293
- Size of remote file:
- 189 MB
- SHA256:
- a5da1ced37d21aaf885edf4f7ee33fc5f28b8469211a251f316964bb9837ad6d
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