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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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- robust-speech-event |
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- hf-asr-leaderboard |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-hi-cv8-b2 |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: mozilla-foundation/common_voice_8_0 |
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name: Common Voice 7 |
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args: hi |
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metrics: |
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- type: wer |
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value: 0.3891350503092403 |
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name: Test WER |
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- name: Test CER |
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type: cer |
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value: 0.13016327327131985 |
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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: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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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: NA |
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- name: Test CER |
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type: cer |
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value: NA |
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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-xls-r-300m-hi-cv8-b2 |
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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_8_0 - HI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7322 |
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- Wer: 0.3469 |
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### Evaluation Commands |
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1. To evaluate on mozilla-foundation/common_voice_8_0 with test split |
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs |
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2. To evaluate on speech-recognition-community-v2/dev_data |
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Hindi language isn't available in speech-recognition-community-v2/dev_data |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.00025 |
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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: 700 |
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- num_epochs: 35 |
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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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| 9.6226 | 1.04 | 200 | 3.8855 | 1.0 | |
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| 3.4678 | 2.07 | 400 | 3.4283 | 1.0 | |
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| 2.3668 | 3.11 | 600 | 1.0743 | 0.7175 | |
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| 0.7308 | 4.15 | 800 | 0.7663 | 0.5498 | |
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| 0.4985 | 5.18 | 1000 | 0.6957 | 0.5001 | |
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| 0.3817 | 6.22 | 1200 | 0.6932 | 0.4866 | |
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| 0.3281 | 7.25 | 1400 | 0.7034 | 0.4983 | |
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| 0.2752 | 8.29 | 1600 | 0.6588 | 0.4606 | |
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| 0.2475 | 9.33 | 1800 | 0.6514 | 0.4328 | |
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| 0.219 | 10.36 | 2000 | 0.6396 | 0.4176 | |
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| 0.2036 | 11.4 | 2200 | 0.6867 | 0.4162 | |
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| 0.1793 | 12.44 | 2400 | 0.6943 | 0.4196 | |
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| 0.1724 | 13.47 | 2600 | 0.6862 | 0.4260 | |
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| 0.1554 | 14.51 | 2800 | 0.7615 | 0.4222 | |
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| 0.151 | 15.54 | 3000 | 0.7058 | 0.4110 | |
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| 0.1335 | 16.58 | 3200 | 0.7172 | 0.3986 | |
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| 0.1326 | 17.62 | 3400 | 0.7182 | 0.3923 | |
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| 0.1225 | 18.65 | 3600 | 0.6995 | 0.3910 | |
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| 0.1146 | 19.69 | 3800 | 0.7075 | 0.3875 | |
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| 0.108 | 20.73 | 4000 | 0.7297 | 0.3858 | |
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| 0.1048 | 21.76 | 4200 | 0.7413 | 0.3850 | |
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| 0.0979 | 22.8 | 4400 | 0.7452 | 0.3793 | |
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| 0.0946 | 23.83 | 4600 | 0.7436 | 0.3759 | |
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| 0.0897 | 24.87 | 4800 | 0.7289 | 0.3754 | |
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| 0.0854 | 25.91 | 5000 | 0.7271 | 0.3667 | |
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| 0.0803 | 26.94 | 5200 | 0.7378 | 0.3656 | |
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| 0.0752 | 27.98 | 5400 | 0.7488 | 0.3680 | |
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| 0.0718 | 29.02 | 5600 | 0.7185 | 0.3619 | |
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| 0.0702 | 30.05 | 5800 | 0.7428 | 0.3554 | |
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| 0.0653 | 31.09 | 6000 | 0.7447 | 0.3559 | |
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| 0.0638 | 32.12 | 6200 | 0.7327 | 0.3523 | |
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| 0.058 | 33.16 | 6400 | 0.7339 | 0.3488 | |
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| 0.0594 | 34.2 | 6600 | 0.7322 | 0.3469 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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