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--- |
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license: mit |
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base_model: UmarRamzan/w2v2-bert-urdu |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: w2v2-bert-urdu |
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results: [] |
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language: |
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- ur |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Wav2Vec-Bert-2.0-ngram-Urdu |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset. The fine-tuned model is enhanced with the addition of an ngram language model that has also been trained on the same dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3681 |
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- Wer: 0.2407 |
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## Usage Instructions |
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```python |
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from transformers import AutoFeatureExtractor, Wav2Vec2BertModel |
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import torch |
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from datasets import load_dataset |
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dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") |
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dataset = dataset.sort("id") |
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sampling_rate = dataset.features["audio"].sampling_rate |
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processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu") |
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model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu") |
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# audio file is decoded on the fly |
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inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
