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A fine-tuned XLS-R 300M CTC model for Urdu automatic speech recognition. It transcribes **16 kHz mono** audio and includes an optional 5-gram KenLM decoder. **Best reported result:** 39.89% WER / 16.70% CER with KenLM decoding on the Urdu Common Voice 8.0 test set. See the Kaggle evaluation notebook for a reproducible example. > **Note:** After installing the packages in a notebook environment, restart the kernel before running the inference code.
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2101 - Wer: 0.4939 - Cer: 0.2238 The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 30 - mixed_precision_training: Native AMP - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
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