Automatic Speech Recognition
Transformers
PyTorch
Uzbek
wav2vec2
pretraining
mozilla-foundation/common_voice_10_0
AIRI_UZ
Generated from Trainer
Instructions to use oyqiz/uzbek_stt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oyqiz/uzbek_stt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oyqiz/uzbek_stt")# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("oyqiz/uzbek_stt") model = AutoModelForPreTraining.from_pretrained("oyqiz/uzbek_stt") - Notebooks
- Google Colab
- Kaggle
Oyqiz jamoasi a'zolari tomonidan qilingan STT ning eng yaxshi versiyasi!
Foziljon To'lqinov, Shaxboz Zohidov, Abduraxim Jabborov, Yahyoxon Rahimov, Mahmud Jumanazarov
Bu model facebook/wav2vec2-xls-r-300m va MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - UZ dataseti bilan 100ta epoxta o'qitilgan. O'qitish natijalari:
- Xatolik: 0.1963
- So'z xatoligi: 0.2102
O'qitish giperparameterlari
O'qitish uchun ishlatilgan giperparameterlar:
- learning_rate: 0.00003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
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