Instructions to use kehanlu/mandarin-wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kehanlu/mandarin-wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kehanlu/mandarin-wav2vec2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("kehanlu/mandarin-wav2vec2") model = AutoModel.from_pretrained("kehanlu/mandarin-wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from kehanlu/mandarin-wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 258 Bytes
-
https://huggingface.co/kehanlu/mandarin-wav2vec2/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://kehanlu/mandarin-wav2vec2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kehanlu/mandarin-wav2vec2/resolve/main/preprocessor_config.json
258 Bytes
| { | |
| "do_normalize": false, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2Processor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |