Instructions to use hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification 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-UniSpeechSatForSequenceClassification 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-UniSpeechSatForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-tiny-model-private/tiny-random-UniSpeechSatForSequenceClassification/resolve/main/preprocessor_config.json
215 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |