Instructions to use DarliAI/kissi-w2v2-lg-xls-r-300m-bemba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarliAI/kissi-w2v2-lg-xls-r-300m-bemba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DarliAI/kissi-w2v2-lg-xls-r-300m-bemba")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DarliAI/kissi-w2v2-lg-xls-r-300m-bemba") model = AutoModelForCTC.from_pretrained("DarliAI/kissi-w2v2-lg-xls-r-300m-bemba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
w2v2-lg-xls-r-300m-bemba
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m.
It achieves the following results on the evaluation set:
- eval_loss: 0.29369
- eval_wer: 0.36509
- eval_runtime: 99.7157
- eval_samples_per_second: 28.269
- eval_steps_per_second: 1.705
- epoch: 11.59793
- step: 15000
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Model tree for DarliAI/kissi-w2v2-lg-xls-r-300m-bemba
Base model
facebook/wav2vec2-xls-r-300m