Instructions to use makhataei/Wav2vec2-xlsr-Shemo-Ravdess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makhataei/Wav2vec2-xlsr-Shemo-Ravdess with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("makhataei/Wav2vec2-xlsr-Shemo-Ravdess") model = Wav2Vec2ForSpeechClassification.from_pretrained("makhataei/Wav2vec2-xlsr-Shemo-Ravdess", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from makhataei/Wav2vec2-xlsr-Shemo-Ravdess: direct link, hf CLI and curl.
- Browser
- Download file 262 Bytes
-
https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://makhataei/Wav2vec2-xlsr-Shemo-Ravdess/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess/resolve/main/preprocessor_config.json
262 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2ProcessorWithLM", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |