legacy-datasets/common_voice
Updated • 949 • 147
How to use vioannidis/wav2vec2-large-xls-r-300m-dutch-fast-colab with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="vioannidis/wav2vec2-large-xls-r-300m-dutch-fast-colab") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("vioannidis/wav2vec2-large-xls-r-300m-dutch-fast-colab")
model = AutoModelForCTC.from_pretrained("vioannidis/wav2vec2-large-xls-r-300m-dutch-fast-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 |
|---|---|---|---|---|
| 4.9018 | 0.58 | 200 | 2.9377 | 1.0 |
| 2.1148 | 1.15 | 400 | 1.1957 | 0.8662 |
| 0.7195 | 1.73 | 600 | 0.7060 | 0.5645 |
| 0.3888 | 2.31 | 800 | 0.5290 | 0.4377 |
| 0.2488 | 2.88 | 1000 | 0.4580 | 0.3763 |
Base model
facebook/wav2vec2-xls-r-300m