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Model: rcastrovexler/whisper-small-es-cl
Source: Original Platform

README.md

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+---
+library_name: transformers
+language:
+- es
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+datasets:
+- ylacombe/google-chilean-spanish
+metrics:
+- wer
+model-index:
+- name: Whisper Small ES-CL - Roberto Castro-Vexler
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: OpenSLR Chilean Spanish
+      type: ylacombe/google-chilean-spanish
+      args: 'config: es-cl, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 5.930960948953752
+---
+
+<!-- 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 ES-CL - Roberto Castro-Vexler
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the OpenSLR Chilean Spanish dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1552
+- Wer: 5.9310
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 4000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Wer    |
+|:-------------:|:-------:|:----:|:---------------:|:------:|
+| 0.0038        | 8.6207  | 1000 | 0.1381          | 6.1975 |
+| 0.0002        | 17.2414 | 2000 | 0.1466          | 5.8510 |
+| 0.0001        | 25.8621 | 3000 | 0.1528          | 5.9709 |
+| 0.0001        | 34.4828 | 4000 | 0.1552          | 5.9310 |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.20.1