# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Talha/urdumodel")
model = AutoModelForCTC.from_pretrained("Talha/urdumodel")Quick Links
urdumodel
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used.
Training procedure
All the code is available here https://github.com/talhaanwarch/Urdu-ASR
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Model score on test
When I train I got different WER and CER score on test set, but when I tested locally I got WER of 0.27 without language model and 0.22 with language model.
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Talha/urdumodel")