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--- |
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language: |
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- te |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- openslr |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-small-telugu-large-data |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: google/fleurs |
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type: openslr |
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config: te_in |
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split: None |
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metrics: |
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- name: Wer |
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type: wer |
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value: 38.84604916991744 |
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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-small-telugu-large-data |
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This [model](steja/whisper-small-telugu-large-data) is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs and openslr dataset in telugu. |
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It achieves the following results on the evaluation set (google/fleurs, test set): |
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- Loss: 0.3310 |
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- Wer: 38.8460 |
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[openai/whisper-small](https://huggingface.co/openai/whisper-small) has the following zero shot performance on google/fleurs test set: |
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- Wer: 117.91 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 64 |
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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: 500 |
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- training_steps: 5000 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.128 | 2.27 | 500 | 0.2015 | 45.1692 | |
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| 0.0462 | 4.55 | 1000 | 0.1877 | 41.1050 | |
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| 0.0184 | 6.82 | 1500 | 0.2241 | 40.5153 | |
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| 0.0045 | 9.09 | 2000 | 0.2590 | 39.7260 | |
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| 0.0019 | 11.36 | 2500 | 0.2824 | 39.0819 | |
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| 0.0006 | 13.64 | 3000 | 0.3002 | 38.9096 | |
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| 0.0002 | 15.91 | 3500 | 0.3141 | 38.5920 | |
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| 0.0001 | 18.18 | 4000 | 0.3232 | 38.7463 | |
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| 0.0001 | 20.45 | 4500 | 0.3289 | 38.8370 | |
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| 0.0001 | 22.73 | 5000 | 0.3310 | 38.8460 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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