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--- |
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language: |
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- ur |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- automatic-speech-recognition |
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- robust-speech-event |
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- hf-asr-leaderboard |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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metrics: |
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- wer |
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- cer |
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pipeline_tag: automatic-speech-recognition |
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base_model: Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 |
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model-index: |
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- name: wav2vec2-urdu |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Urdu Speech Recognition |
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dataset: |
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name: Common Voice (Urdu) |
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type: common_voice |
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metrics: |
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- type: wer |
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value: 57.47 |
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name: WER |
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config: load_metric("wer") |
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verified: true |
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- type: cer |
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value: 32.68 |
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name: CER |
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config: load_metric("cer") |
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verified: true |
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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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# wav2vec2-large-xls-r-300m-Urdu |
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This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Wer: 0.5747 |
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- Cer: 0.3268 |
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## Model description |
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The training and valid dataset is 0.58 hours. It was hard to train any model on lower number of so I decided to take vakyansh-wav2vec2-urdu-urm-60 checkpoint and finetune the wav2vec2 model. |
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## Training procedure |
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Trained on Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 due to lesser number of samples. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 64 |
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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: 128 |
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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: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 4.3054 | 16.67 | 50 | 9.0055 | 0.8306 | 0.4869 | |
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| 2.0629 | 33.33 | 100 | 9.5849 | 0.6061 | 0.3414 | |
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| 0.8966 | 50.0 | 150 | 4.8686 | 0.6052 | 0.3426 | |
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| 0.4197 | 66.67 | 200 | 12.3261 | 0.5817 | 0.3370 | |
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| 0.294 | 83.33 | 250 | 11.9653 | 0.5712 | 0.3328 | |
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| 0.2329 | 100.0 | 300 | 7.6846 | 0.5747 | 0.3268 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
