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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: openai/whisper-large-v3 |
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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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- mozilla-foundation/common_voice_16_1 |
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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 Large MN - Ankhbayasgalan Davaadorj |
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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: Common Voice 16.1 & FLEURS |
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type: mozilla-foundation/common_voice_16_1 |
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config: mn |
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split: None |
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args: 'config: mn, split: test+validation' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 33.65601452065343 |
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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 MN - Ankhbayasgalan Davaadorj |
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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 16.1 & FLEURS dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4942 |
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- Wer: 33.6560 |
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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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- 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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| 0.0361 | 5.99 | 1000 | 0.3833 | 42.0109 | |
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| 0.0016 | 11.98 | 2000 | 0.4445 | 37.2092 | |
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| 0.0002 | 17.96 | 3000 | 0.4784 | 34.0410 | |
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| 0.0001 | 23.95 | 4000 | 0.4942 | 33.6560 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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