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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/wav2vec2-large-xlsr-53
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tags:
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- generated_from_trainer
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datasets:
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- fleurs
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metrics:
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- wer
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model-index:
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- name: wav2vec2-common_voice-en-finetune
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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: fleurs
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type: fleurs
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config: en_us
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split: test
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args: en_us
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metrics:
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- name: Wer
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type: wer
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value: 0.260403073800203
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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-common_voice-en-finetune
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3437
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- Wer: 0.2604
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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: 0.0003
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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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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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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- num_epochs: 10.0
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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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| No log | 1.0870 | 100 | 3.5106 | 1.0 |
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| No log | 2.1739 | 200 | 2.9226 | 1.0 |
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| No log | 3.2609 | 300 | 2.8745 | 1.0 |
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| No log | 4.3478 | 400 | 1.8100 | 0.9804 |
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| 3.7609 | 5.4348 | 500 | 0.4771 | 0.4207 |
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| 3.7609 | 6.5217 | 600 | 0.3808 | 0.3484 |
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| 3.7609 | 7.6087 | 700 | 0.3408 | 0.2872 |
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| 3.7609 | 8.6957 | 800 | 0.3479 | 0.2719 |
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| 3.7609 | 9.7826 | 900 | 0.3437 | 0.2604 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.4.0+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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