Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Malayalam
wav2vec2-bert
Eval Results (legacy)
Instructions to use vrclc/W2V2-BERT-withLM-Malayalam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrclc/W2V2-BERT-withLM-Malayalam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vrclc/W2V2-BERT-withLM-Malayalam")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("vrclc/W2V2-BERT-withLM-Malayalam") model = AutoModelForCTC.from_pretrained("vrclc/W2V2-BERT-withLM-Malayalam") - Notebooks
- Google Colab
- Kaggle
| { | |
| "feature_extractor_type": "SeamlessM4TFeatureExtractor", | |
| "feature_size": 80, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2ProcessorWithLM", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| } | |