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--- |
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language: |
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- mr |
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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_8_0 |
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- generated_from_trainer |
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- mr |
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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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model-index: |
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- name: wav2vec2-large-xls-r-300m-mr-v2 |
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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 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: mr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 0.49378259125551544 |
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- name: Test CER |
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type: cer |
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value: 0.12470799640610962 |
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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: mr |
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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-mr-v2 |
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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 - MR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8729 |
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- Wer: 0.4942 |
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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-mr-v2 --dataset mozilla-foundation/common_voice_8_0 --config mr --split test --log_outputs |
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2. To evaluate on speech-recognition-community-v2/dev_data |
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev_data --config mr --split validation --chunk_length_s 10 --stride_length_s 1 |
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Note: Marathi language not found 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: 0.000333 |
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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: 1000 |
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- num_epochs: 200 |
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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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| 8.4934 | 9.09 | 200 | 3.7326 | 1.0 | |
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| 3.4234 | 18.18 | 400 | 3.3383 | 0.9996 | |
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| 3.2628 | 27.27 | 600 | 2.7482 | 0.9992 | |
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| 1.7743 | 36.36 | 800 | 0.6755 | 0.6787 | |
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| 1.0346 | 45.45 | 1000 | 0.6067 | 0.6193 | |
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| 0.8137 | 54.55 | 1200 | 0.6228 | 0.5612 | |
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| 0.6637 | 63.64 | 1400 | 0.5976 | 0.5495 | |
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| 0.5563 | 72.73 | 1600 | 0.7009 | 0.5383 | |
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| 0.4844 | 81.82 | 1800 | 0.6662 | 0.5287 | |
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| 0.4057 | 90.91 | 2000 | 0.6911 | 0.5303 | |
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| 0.3582 | 100.0 | 2200 | 0.7207 | 0.5327 | |
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| 0.3163 | 109.09 | 2400 | 0.7107 | 0.5118 | |
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| 0.2761 | 118.18 | 2600 | 0.7538 | 0.5118 | |
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| 0.2415 | 127.27 | 2800 | 0.7850 | 0.5178 | |
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| 0.2127 | 136.36 | 3000 | 0.8016 | 0.5034 | |
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| 0.1873 | 145.45 | 3200 | 0.8302 | 0.5187 | |
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| 0.1723 | 154.55 | 3400 | 0.9085 | 0.5223 | |
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| 0.1498 | 163.64 | 3600 | 0.8396 | 0.5126 | |
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| 0.1425 | 172.73 | 3800 | 0.8776 | 0.5094 | |
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| 0.1258 | 181.82 | 4000 | 0.8651 | 0.5014 | |
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| 0.117 | 190.91 | 4200 | 0.8772 | 0.4970 | |
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| 0.1093 | 200.0 | 4400 | 0.8729 | 0.4942 | |
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### Framework versions |
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- Transformers 4.16.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.2 |
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- Tokenizers 0.11.0 |
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