Create README.md

README.md

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+---
+language:
+- ro
+license: apache-2.0
+base_model: openai/whisper-base
+tags:
+- hf-asr-leaderboard
+- generated_from_trainer
+datasets:
+- iulik-pisik/horoscop_neti
+- iulik-pisik/audio_vreme
+metrics:
+- wer
+model-index:
+- name: Whisper Base - finetuned on weather and horoscope
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Vreme ProTV and Horoscop Neti
+      type: iulik-pisik/audio_vreme
+      config: default
+      split: test
+      args: 'config: ro, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 13.61
+pipeline_tag: automatic-speech-recognition
+---
+
+
+
+
+
+# Whisper Base - finetuned on weather and horoscope
+This model is a fine-tuned version of [openai/whisper-base](openai/whisper-base) on the Vreme ProTV and Horoscop Neti datasets.
+It achieves the following results on the evaluation set:
+
+- Loss: 0.0016
+- Wer: 13.61
+
+
+## Model description
+
+This is a fine-tuned version of the Whisper Base model, specifically adapted for Romanian language Automatic Speech Recognition (ASR) 
+in the domains of weather forecasts and horoscopes. The model has been trained on two custom datasets to improve its performance
+in transcribing Romanian speech in these specific contexts.
+
+## Training procedure
+
+The model was fine-tuned using transfer learning techniques on the pre-trained Whisper Base model. 
+Two custom datasets were used: audio recordings of weather forecasts and horoscopes in Romanian.
+
+### 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: 3000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Epoch | Step | Validation Loss | WER     |
+|:-----:|:----:|:---------------:|:-------:|
+| 3.85  | 1000 | 0.0784          | 14.2716 |
+| 7.69  | 2000 | 0.0124          | 14.1371 |
+| 11.54 | 3000 | 0.0022          | 13.6796 |
+| 15.38 | 4000 | 0.0016          | 13.6168 |
+
+
+
+### Framework versions
+
+- Transformers 4.39.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2