Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: openai/whisper-large-v2
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_17_0
+metrics:
+- wer
+model-index:
+- name: asr-whisper-helpline-sw-v1
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: sw
+      split: validation
+      args: sw
+    metrics:
+    - name: Wer
+      type: wer
+      value: 23.61977361977362
+---
+
+<!-- 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. -->
+
+# asr-whisper-helpline-sw-v1
+
+This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_17_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4024
+- Wer: 23.6198
+
+## 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: 16
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 10000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
+|:-------------:|:------:|:-----:|:---------------:|:-------:|
+| 0.5275        | 0.05   | 500   | 0.7083          | 43.6821 |
+| 0.4754        | 0.1    | 1000  | 0.5733          | 42.0651 |
+| 0.2844        | 0.15   | 1500  | 0.5390          | 36.2093 |
+| 0.2647        | 0.2    | 2000  | 0.5060          | 34.3151 |
+| 0.2939        | 0.25   | 2500  | 0.4761          | 34.8926 |
+| 0.285         | 1.0094 | 3000  | 0.4610          | 35.0427 |
+| 0.1837        | 1.0594 | 3500  | 0.4516          | 34.0725 |
+| 0.2148        | 1.1094 | 4000  | 0.4309          | 33.1023 |
+| 0.2611        | 1.1594 | 4500  | 0.4328          | 29.0945 |
+| 0.1923        | 1.2094 | 5000  | 0.4180          | 31.2428 |
+| 0.0849        | 1.2594 | 5500  | 0.4229          | 25.8027 |
+| 0.1913        | 2.0188 | 6000  | 0.4051          | 28.4592 |
+| 0.0693        | 2.0688 | 6500  | 0.4256          | 29.1753 |
+| 0.1261        | 2.1188 | 7000  | 0.4057          | 28.8750 |
+| 0.0808        | 2.1688 | 7500  | 0.4054          | 28.3899 |
+| 0.0524        | 2.2188 | 8000  | 0.4248          | 25.6757 |
+| 0.0845        | 2.2688 | 8500  | 0.4159          | 26.5073 |
+| 0.0611        | 3.0282 | 9000  | 0.4142          | 25.4447 |
+| 0.0562        | 3.0782 | 9500  | 0.4104          | 25.6872 |
+| 0.0867        | 3.1282 | 10000 | 0.4024          | 23.6198 |
+
+
+### Framework versions
+
+- Transformers 4.56.2
+- Pytorch 2.8.0+cu128
+- Datasets 2.21.0
+- Tokenizers 0.22.1

generation_config.json

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+  "forced_decoder_ids": [
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+  "language": "sw",
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+  "use_cache": false
+}

metrics_summary.json

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+{
+  "final_loss": 0.0867,
+  "min_loss": 0.0359,
+  "max_loss": 2.5771,
+  "avg_loss": 0.2143185,
+  "total_steps": 10000,
+  "total_metrics_logged": 221
+}