Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForSequenceClassification 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-Wav2Vec2ForSequenceClassification 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-Wav2Vec2ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82affe314addf1214c96fd26a4eeb0a03d8a7fb78a5539e2775c1827093e500e
- Size of remote file:
- 153 kB
- SHA256:
- b8abe3503bdb343d8b64fe3625b22935c100cdc07ddcffd325dc49a94e6f8ffb
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