Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification") model = AutoModelForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification/resolve/f5050879a09567f8acdf4f7bf642ca14937c7393/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification@f5050879a09567f8acdf4f7bf642ca14937c7393/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ForAudioFrameClassification/resolve/f5050879a09567f8acdf4f7bf642ca14937c7393/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 | |
| } | |