Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mscs23021/Whisper_Small_Final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mscs23021/Whisper_Small_Final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mscs23021/Whisper_Small_Final")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mscs23021/Whisper_Small_Final") model = AutoModelForSpeechSeq2Seq.from_pretrained("mscs23021/Whisper_Small_Final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mscs23021/Whisper_Small_Final: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
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https://huggingface.co/mscs23021/Whisper_Small_Final/resolve/main/README.md
- Command line
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hf download hf://mscs23021/Whisper_Small_Final/README.md
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curl -L -o README.md https://huggingface.co/mscs23021/Whisper_Small_Final/resolve/main/README.md
1.77 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: mscs23021/Whisper_small_urdu_t | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - common_voice_13_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper_Small_Final | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: common_voice_13_0 | |
| type: common_voice_13_0 | |
| config: ur | |
| split: test | |
| args: ur | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 110.79826732673268 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Whisper_Small_Final | |
| This model is a fine-tuned version of [mscs23021/Whisper_small_urdu_t](https://huggingface.co/mscs23021/Whisper_small_urdu_t) on the common_voice_13_0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5675 | |
| - Wer: 110.7983 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 16 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 300 | |
| - num_epochs: 3 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 | |