Instructions to use vasudevgupta/speech_jax_wav2vec2-large-lv60_100h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasudevgupta/speech_jax_wav2vec2-large-lv60_100h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vasudevgupta/speech_jax_wav2vec2-large-lv60_100h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("vasudevgupta/speech_jax_wav2vec2-large-lv60_100h") model = AutoModelForCTC.from_pretrained("vasudevgupta/speech_jax_wav2vec2-large-lv60_100h", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
- Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing
- Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/asr/train_wav2vec2.py
Following results are obtained with adce555df7402dc63f8f4d9d14cb286f4b9d4107
| dataset | WER |
|---|---|
| Librispeech-test-clean | 5.5 % |
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