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