Instructions to use hf-tiny-model-private/tiny-random-WavLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-WavLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-tiny-model-private/tiny-random-WavLMForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-WavLMForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 161 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-WavLMForSequenceClassification/resolve/fed02152b2dd3dbea9d2c87b9c976d47ef537c7d/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-WavLMForSequenceClassification@fed02152b2dd3dbea9d2c87b9c976d47ef537c7d/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-WavLMForSequenceClassification/resolve/fed02152b2dd3dbea9d2c87b9c976d47ef537c7d/pytorch_model.bin
161 kB
- Xet hash:
- 78c51863789ce2d69f4b40bb11b480f6ae3606130db7915201b09618f36b4e23
- Size of remote file:
- 161 kB
- SHA256:
- c75613e6c841cdc619c1aaec1a97a3a368708cdc618e9842b32d31430b933595
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