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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: wav2vec2-large-xlsr-53-AsanteTwi-04
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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: common_voice_13_0
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type: common_voice_13_0
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config: tw
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split: test
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args: tw
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metrics:
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- name: Wer
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type: wer
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value: 0.625
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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-xlsr-53-AsanteTwi-04
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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 common_voice_13_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7250
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- Wer: 0.625
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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: 100
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- num_epochs: 90
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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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| 13.2642 | 8.33 | 50 | 4.7327 | 1.0 |
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| 3.1075 | 16.67 | 100 | 3.1680 | 1.0 |
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| 2.8849 | 25.0 | 150 | 2.9745 | 1.0 |
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| 2.8553 | 33.33 | 200 | 2.9167 | 1.0 |
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| 2.8333 | 41.67 | 250 | 2.8538 | 1.0 |
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| 2.6501 | 50.0 | 300 | 2.3417 | 1.0 |
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| 1.8966 | 58.33 | 350 | 1.1529 | 0.875 |
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| 0.9431 | 66.67 | 400 | 0.8519 | 0.75 |
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| 0.5951 | 75.0 | 450 | 0.7970 | 0.625 |
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| 0.444 | 83.33 | 500 | 0.7250 | 0.625 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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