legacy-datasets/common_voice
Updated • 974 • 147
How to use KatMarie/wav2vec2-large-xls-r-300m-euskera2.1-colab with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="KatMarie/wav2vec2-large-xls-r-300m-euskera2.1-colab") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("KatMarie/wav2vec2-large-xls-r-300m-euskera2.1-colab")
model = AutoModelForCTC.from_pretrained("KatMarie/wav2vec2-large-xls-r-300m-euskera2.1-colab", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.1152 | 1.49 | 700 | 0.3510 | 0.4973 |
| 0.1969 | 2.98 | 1400 | 0.2552 | 0.3643 |
| 0.1027 | 4.47 | 2100 | 0.2379 | 0.3108 |
| 0.0648 | 5.96 | 2800 | 0.2291 | 0.2787 |
Base model
facebook/wav2vec2-xls-r-300m