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--- |
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language: |
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- hi |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- hi |
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- mozilla-foundation/common_voice_7_0 |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_7_0 |
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metrics: |
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- wer |
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- cer |
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model-index: |
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- name: shivam/wav2vec2-xls-r-hindi |
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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: Common Voice Corpus 7.0 |
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type: mozilla-foundation/common_voice_7_0 |
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args: hi |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 52.3 |
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- name: Test CER |
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type: cer |
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value: 26.09 |
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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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# |
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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 MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2282 |
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- Wer: 0.6838 |
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## Evaluation results on Common Voice 7 "test" (Running ./eval.py): |
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### With LM |
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- WER: 52.30 |
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- CER: 26.09 |
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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: 7.5e-05 |
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- train_batch_size: 8 |
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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: 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: 2000 |
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- num_epochs: 50.0 |
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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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| 5.3155 | 3.4 | 500 | 4.5582 | 1.0 | |
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| 3.3369 | 6.8 | 1000 | 3.4269 | 1.0 | |
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| 2.1785 | 10.2 | 1500 | 1.7191 | 0.8831 | |
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| 1.579 | 13.6 | 2000 | 1.3604 | 0.7647 | |
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| 1.3773 | 17.01 | 2500 | 1.2737 | 0.7519 | |
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| 1.3165 | 20.41 | 3000 | 1.2457 | 0.7401 | |
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| 1.2274 | 23.81 | 3500 | 1.3617 | 0.7301 | |
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| 1.1787 | 27.21 | 4000 | 1.2068 | 0.7010 | |
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| 1.1467 | 30.61 | 4500 | 1.2416 | 0.6946 | |
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| 1.0801 | 34.01 | 5000 | 1.2312 | 0.6990 | |
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| 1.0709 | 37.41 | 5500 | 1.2984 | 0.7138 | |
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| 1.0307 | 40.81 | 6000 | 1.2049 | 0.6871 | |
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| 1.0003 | 44.22 | 6500 | 1.1956 | 0.6841 | |
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| 1.004 | 47.62 | 7000 | 1.2101 | 0.6793 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu113 |
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- Datasets 1.18.1.dev0 |
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- Tokenizers 0.11.0 |
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