Instructions to use GleamEyeBeast/common_voice_dataset_model_naive_char with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleamEyeBeast/common_voice_dataset_model_naive_char with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GleamEyeBeast/common_voice_dataset_model_naive_char")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("GleamEyeBeast/common_voice_dataset_model_naive_char") model = AutoModelForCTC.from_pretrained("GleamEyeBeast/common_voice_dataset_model_naive_char", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from GleamEyeBeast/common_voice_dataset_model_naive_char: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/GleamEyeBeast/common_voice_dataset_model_naive_char/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://GleamEyeBeast/common_voice_dataset_model_naive_char/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/GleamEyeBeast/common_voice_dataset_model_naive_char/resolve/main/preprocessor_config.json
214 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |