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--- |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- facebook/multilingual_librispeech |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper largeV2 Spanish MLS |
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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: facebook/multilingual_librispeech spanish |
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type: facebook/multilingual_librispeech |
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config: spanish |
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split: test |
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args: spanish |
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metrics: |
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- name: Wer |
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type: wer |
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value: 3.4677966101694913 |
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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 largeV2 Spanish MLS |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/multilingual_librispeech spanish dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0910 |
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- Wer: 3.4678 |
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## Model description |
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The model is fine-tuned for 4000 updates/steps on multilingual librispeech Spanish train data. |
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- Zero-shot - 4.2 (MLS Spanish test) |
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- Fine-tune MLS spanish train - 3.46 (MLS Spanish test) (-17.61%) |
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- Zero-shot - 6.3 (CV11 test) |
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- Fine-tune MLS spanish train - 8.38 (CV11 test) |
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When the model is fine-tuned on specific dataset, the model loose its ability to generalise across datasets. |
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Here the model is fine-tuned on MLS Spanish and evaluated on CV11 Spanish test. We can observe the drop in performance on CV11 test data. |
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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: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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: 4000 |
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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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| 0.2176 | 0.25 | 1000 | 0.1200 | 5.3932 | |
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| 0.1845 | 0.5 | 2000 | 0.1055 | 4.2 | |
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| 0.4516 | 0.75 | 3000 | 0.0977 | 3.6768 | |
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| 0.1549 | 1.14 | 4000 | 0.0910 | 3.4678 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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