Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use AdaCodruta/wav2vec2_common_voice17_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaCodruta/wav2vec2_common_voice17_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AdaCodruta/wav2vec2_common_voice17_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("AdaCodruta/wav2vec2_common_voice17_finetuning") model = AutoModelForCTC.from_pretrained("AdaCodruta/wav2vec2_common_voice17_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from AdaCodruta/wav2vec2_common_voice17_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 220 Bytes
-
https://huggingface.co/AdaCodruta/wav2vec2_common_voice17_finetuning/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://AdaCodruta/wav2vec2_common_voice17_finetuning/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/AdaCodruta/wav2vec2_common_voice17_finetuning/resolve/main/preprocessor_config.json
220 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 1 | |
| } | |