End of training
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README.md
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---
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license: apache-2.0
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base_model: facebook/wav2vec2-xls-r-300m
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: wav2vec2-hindi-speech
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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_11_0
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type: common_voice_11_0
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config: hi
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split: test
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args: hi
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metrics:
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- name: Wer
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type: wer
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value: 1.0
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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-hindi-speech
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_11_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.1914
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- Wer: 1.0
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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: 0.0001
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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: 4
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- total_train_batch_size: 128
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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: 200
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- num_epochs: 15
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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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| 5.8962 | 11.68 | 400 | 3.1914 | 1.0 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.2+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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model.safetensors
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size 6506
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