Instructions to use sharrnah/wav2vec2-bert-CV16-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharrnah/wav2vec2-bert-CV16-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sharrnah/wav2vec2-bert-CV16-de")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sharrnah/wav2vec2-bert-CV16-de") model = AutoModelForCTC.from_pretrained("sharrnah/wav2vec2-bert-CV16-de") - Notebooks
- Google Colab
- Kaggle
wav2vec2-bert-CV16-de
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - DE dataset. It achieves the following results on the evaluation set:
- Loss: 0.095182
- Wer: 0.066672
- Steps: 25000
- epoch: 10
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