Instructions to use hf-internal-testing/wav2vec2-conformer-seq-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/wav2vec2-conformer-seq-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/wav2vec2-conformer-seq-class")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/wav2vec2-conformer-seq-class") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/wav2vec2-conformer-seq-class") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74f3b54cc6fe7f786055975add539b1bfba46d969751207315cba693d2e0950e
- Size of remote file:
- 221 kB
- SHA256:
- a815e67cc0c72c5a1f72a64df5d537e9ac3ebc6a0a4c840d2720395e973ffc53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.