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--- |
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language: |
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- es |
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license: apache-2.0 |
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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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- 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 Small Es - Sanchit Gandhi |
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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: Multilingual LibriSpeech |
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type: facebook/multilingual_librispeech |
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args: 'config: es, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 4.988756935106611 |
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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 Small Es - Sanchit Gandhi |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1252 |
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- Wer: 4.9888 |
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## Model description |
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More information needed |
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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: 2 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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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: 2500 |
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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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| 0.2346 | 0.2 | 500 | 0.1957 | 8.5131 | |
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| 0.1252 | 0.4 | 1000 | 0.1448 | 5.7876 | |
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| 0.2076 | 0.6 | 1500 | 0.1361 | 5.5786 | |
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| 0.2356 | 0.8 | 2000 | 0.1504 | 6.6611 | |
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| 0.1893 | 1.0 | 2500 | 0.1252 | 4.9888 | |
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### Framework versions |
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- Transformers 4.25.0.dev0 |
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- Pytorch 1.12.0 |
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- Datasets 2.6.2.dev0 |
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- Tokenizers 0.12.1 |
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