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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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- mozilla-foundation/common_voice_7_0 |
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- generated_from_trainer |
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- hi |
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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_7_0 |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-hi-CV7 |
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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 7 |
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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: 35.31946325249292 |
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- name: Test CER |
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type: cer |
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value: 11.310803379493076 |
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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: vot |
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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-CV7 |
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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: 0.6588 |
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- Wer: 0.2987 |
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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-CV7 --dataset mozilla-foundation/common_voice_7_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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NA |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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# |
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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: 2000 |
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- num_epochs: 60 |
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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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| 12.809 | 1.36 | 200 | 6.2066 | 1.0 | |
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| 4.3402 | 2.72 | 400 | 3.5184 | 1.0 | |
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| 3.4365 | 4.08 | 600 | 3.2779 | 1.0 | |
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| 1.8643 | 5.44 | 800 | 0.9875 | 0.6270 | |
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| 0.7504 | 6.8 | 1000 | 0.6382 | 0.4666 | |
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| 0.5328 | 8.16 | 1200 | 0.6075 | 0.4505 | |
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| 0.4364 | 9.52 | 1400 | 0.5785 | 0.4215 | |
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| 0.3777 | 10.88 | 1600 | 0.6279 | 0.4227 | |
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| 0.3374 | 12.24 | 1800 | 0.6536 | 0.4192 | |
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| 0.3236 | 13.6 | 2000 | 0.5911 | 0.4047 | |
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| 0.2877 | 14.96 | 2200 | 0.5955 | 0.4097 | |
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| 0.2643 | 16.33 | 2400 | 0.5923 | 0.3744 | |
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| 0.2421 | 17.68 | 2600 | 0.6307 | 0.3814 | |
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| 0.2218 | 19.05 | 2800 | 0.6036 | 0.3764 | |
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| 0.2046 | 20.41 | 3000 | 0.6286 | 0.3797 | |
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| 0.191 | 21.77 | 3200 | 0.6517 | 0.3889 | |
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| 0.1856 | 23.13 | 3400 | 0.6193 | 0.3661 | |
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| 0.1721 | 24.49 | 3600 | 0.7034 | 0.3727 | |
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| 0.1656 | 25.85 | 3800 | 0.6293 | 0.3591 | |
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| 0.1532 | 27.21 | 4000 | 0.6075 | 0.3611 | |
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| 0.1507 | 28.57 | 4200 | 0.6313 | 0.3565 | |
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| 0.1381 | 29.93 | 4400 | 0.6564 | 0.3578 | |
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| 0.1359 | 31.29 | 4600 | 0.6724 | 0.3543 | |
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| 0.1248 | 32.65 | 4800 | 0.6789 | 0.3512 | |
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| 0.1198 | 34.01 | 5000 | 0.6442 | 0.3539 | |
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| 0.1125 | 35.37 | 5200 | 0.6676 | 0.3419 | |
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| 0.1036 | 36.73 | 5400 | 0.7017 | 0.3435 | |
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| 0.0982 | 38.09 | 5600 | 0.6828 | 0.3319 | |
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| 0.0971 | 39.45 | 5800 | 0.6112 | 0.3351 | |
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| 0.0968 | 40.81 | 6000 | 0.6424 | 0.3252 | |
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| 0.0893 | 42.18 | 6200 | 0.6707 | 0.3304 | |
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| 0.0878 | 43.54 | 6400 | 0.6432 | 0.3236 | |
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| 0.0827 | 44.89 | 6600 | 0.6696 | 0.3240 | |
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| 0.0788 | 46.26 | 6800 | 0.6564 | 0.3180 | |
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| 0.0753 | 47.62 | 7000 | 0.6574 | 0.3130 | |
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| 0.0674 | 48.98 | 7200 | 0.6698 | 0.3175 | |
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| 0.0676 | 50.34 | 7400 | 0.6441 | 0.3142 | |
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| 0.0626 | 51.7 | 7600 | 0.6642 | 0.3121 | |
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| 0.0617 | 53.06 | 7800 | 0.6615 | 0.3117 | |
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| 0.0599 | 54.42 | 8000 | 0.6634 | 0.3059 | |
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| 0.0538 | 55.78 | 8200 | 0.6464 | 0.3033 | |
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| 0.0571 | 57.14 | 8400 | 0.6503 | 0.3018 | |
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| 0.0491 | 58.5 | 8600 | 0.6625 | 0.3025 | |
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| 0.0511 | 59.86 | 8800 | 0.6588 | 0.2987 | |
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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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