legacy-datasets/common_voice
Updated • 949 • 147
How to use zkealinvo/wav2vec2-large-xls-r-300m-turkish-colab with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="zkealinvo/wav2vec2-large-xls-r-300m-turkish-colab") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("zkealinvo/wav2vec2-large-xls-r-300m-turkish-colab")
model = AutoModelForCTC.from_pretrained("zkealinvo/wav2vec2-large-xls-r-300m-turkish-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 |
|---|---|---|---|---|
| 3.7794 | 3.67 | 400 | 0.6654 | 0.7095 |
| 0.3983 | 7.34 | 800 | 0.4403 | 0.4597 |
| 0.1914 | 11.01 | 1200 | 0.4143 | 0.4442 |
| 0.1289 | 14.68 | 1600 | 0.4276 | 0.3993 |
| 0.0966 | 18.35 | 2000 | 0.3938 | 0.3750 |
| 0.0775 | 22.02 | 2400 | 0.3959 | 0.3604 |
| 0.0597 | 25.69 | 2800 | 0.3758 | 0.3443 |
| 0.0471 | 29.36 | 3200 | 0.3767 | 0.3320 |
Base model
facebook/wav2vec2-xls-r-300m