legacy-datasets/common_voice
Updated • 949 • 147
How to use menhior/wav2vec2-large-xls-r-300m-turkish-colab-full with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="menhior/wav2vec2-large-xls-r-300m-turkish-colab-full") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("menhior/wav2vec2-large-xls-r-300m-turkish-colab-full")
model = AutoModelForCTC.from_pretrained("menhior/wav2vec2-large-xls-r-300m-turkish-colab-full", device_map="auto")# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("menhior/wav2vec2-large-xls-r-300m-turkish-colab-full")
model = AutoModelForCTC.from_pretrained("menhior/wav2vec2-large-xls-r-300m-turkish-colab-full", 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 |
|---|---|---|---|---|
| 3.9196 | 3.67 | 400 | 0.6539 | 0.6524 |
| 0.3908 | 7.34 | 800 | 0.4486 | 0.4502 |
| 0.1859 | 11.01 | 1200 | 0.4015 | 0.3799 |
| 0.1228 | 14.68 | 1600 | 0.4080 | 0.3741 |
| 0.0956 | 18.35 | 2000 | 0.3930 | 0.3468 |
| 0.0757 | 22.02 | 2400 | 0.4163 | 0.3355 |
| 0.0573 | 25.69 | 2800 | 0.3983 | 0.3115 |
| 0.0463 | 29.36 | 3200 | 0.3991 | 0.3050 |
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="menhior/wav2vec2-large-xls-r-300m-turkish-colab-full")