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README.md
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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-small
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tags:
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- generated_from_trainer
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datasets:
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- generator
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metrics:
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- wer
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model-index:
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- name: whisper-es-multicorpus-streaming-v3
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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: generator
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type: generator
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config: default
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split: train
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 0.06276824034334764
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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-es-multicorpus-streaming-v3
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the generator dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0816
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- Wer: 0.0628
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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: 4
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 100
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- training_steps: 1000
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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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| 0.1346 | 0.2 | 200 | 0.1566 | 0.2221 |
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| 0.117 | 0.4 | 400 | 0.1217 | 0.0790 |
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| 0.0861 | 0.6 | 600 | 0.1079 | 0.0633 |
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| 0.0419 | 1.1180 | 800 | 0.1172 | 0.0899 |
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| 0.0467 | 1.318 | 1000 | 0.0816 | 0.0628 |
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### Framework versions
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- Transformers 4.57.2
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- Pytorch 2.9.0+cu126
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- Datasets 4.4.1
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- Tokenizers 0.22.1
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