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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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tags: |
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- automatic-speech-recognition |
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- whisper |
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- urdu |
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- mozilla-foundation/common_voice_17_0 |
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- hf-asr-leaderboard |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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metrics: |
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- wer |
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- cer |
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- bleu |
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- chrf |
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model-index: |
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- name: whisper-large-v3-urdu |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Common Voice 17.0 (Urdu) |
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type: mozilla-foundation/common_voice_17_0 |
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config: ur |
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split: test |
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args: ur |
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metrics: |
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- type: wer |
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value: 26.019 |
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name: WER |
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- type: cer |
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value: 9.426 |
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name: CER |
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- type: bleu |
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value: 59.446 |
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name: BLEU |
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- type: chrf |
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value: 82.902 |
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name: ChrF |
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language: |
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- ur |
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pipeline_tag: automatic-speech-recognition |
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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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# Whisper large V3 Urdu ASR Model 🥇 |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the common_voice_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0204 |
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- Wer: 21.4712 |
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- Cer: 7.1975 |
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## Quick Usage |
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```python |
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from transformers import pipeline |
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transcriber = pipeline( |
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"automatic-speech-recognition", |
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model="kingabzpro/whisper-large-v3-turbo-urdu" |
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) |
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transcriber.model.generation_config.forced_decoder_ids = None |
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transcriber.model.generation_config.language = "ur" |
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transcription = transcriber("audio2.mp3") |
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print(transcription) |
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``` |
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```sh |
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{'text': 'دیکھیے پانی کب تک بہتا اور مچھلی کب تک تیرتی ہے'} |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1500 |
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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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| 0.0261 | 0.5089 | 300 | 0.0254 | 30.0224 | 10.3646 | |
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| 0.0211 | 1.0170 | 600 | 0.0226 | 25.8588 | 8.5780 | |
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| 0.0121 | 1.5259 | 900 | 0.0206 | 24.2158 | 7.9412 | |
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| 0.0093 | 2.0339 | 1200 | 0.0195 | 21.3032 | 7.2018 | |
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| 0.0043 | 2.5428 | 1500 | 0.0204 | 21.4712 | 7.1975 | |
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### Framework versions |
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- Transformers 4.52.2 |
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- Pytorch 2.7.1+cu126 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.2 |
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--- |
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## Evaluation |
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Urdu ASR Evaluation on Common Voice 17.0 (Test Split). |
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| Metric | Value | Description | |
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|--------|----------|------------------------------------| |
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| **WER** | 26.019% | Word Error Rate (lower is better) | |
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| **CER** | 9.426% | Character Error Rate | |
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| **BLEU** | 59.446% | BLEU Score (higher is better) | |
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| **ChrF** | 82.902 | Character n-gram F-score | |
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>👉 Review the testing script: [Testing Whisper Large V3 Urdu](https://www.kaggle.com/code/kingabzpro/testing-urdu-whisper-large-v3) |
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