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--- |
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language: |
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- mn |
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license: apache-2.0 |
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base_model: zagibest/whisper-small-custom-data |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small MN with custom data + Common voice + Google Fluers - Zagi |
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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: fleurs |
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type: fleurs |
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config: mn_mn |
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split: None |
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args: 'config: mn, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 34.56211146253681 |
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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 MN with custom data + Common voice + Google Fluers - Zagi |
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This model is a fine-tuned version of [zagibest/whisper-small-custom-data](https://huggingface.co/zagibest/whisper-small-custom-data) on the fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5343 |
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- Wer: 34.5621 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 4000 |
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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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| 0.1792 | 2.59 | 500 | 0.4204 | 38.2103 | |
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| 0.0121 | 5.18 | 1000 | 0.4673 | 36.5634 | |
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| 0.0034 | 7.77 | 1500 | 0.4964 | 35.6309 | |
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| 0.0009 | 10.36 | 2000 | 0.5044 | 34.7366 | |
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| 0.0007 | 12.95 | 2500 | 0.5166 | 34.7366 | |
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| 0.0004 | 15.54 | 3000 | 0.5271 | 34.5785 | |
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| 0.0003 | 18.13 | 3500 | 0.5323 | 34.5948 | |
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| 0.0003 | 20.73 | 4000 | 0.5343 | 34.5621 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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