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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- spgispeech_xs |
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base_model: facebook/mms-300m |
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model-index: |
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- name: wav2vec2-large-mms-300m-SPGI-xs |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Test set for spgispeech |
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type: kensho/spgispeech |
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config: test |
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split: test |
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metrics: |
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- type: wer |
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value: 100.0 |
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name: WER |
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- type: cer |
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value: 91.89 |
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name: CER |
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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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# wav2vec2-large-mms-300m-SPGI-xs |
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This model is a fine-tuned version of [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) on the spgispeech_xs dataset. |
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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: 0.001 |
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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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- 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: 100 |
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- training_steps: 120 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.4.0 |
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- Tokenizers 0.15.0 |
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