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--- |
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language: |
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- or |
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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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- mozilla-foundation/common_voice_7_0 |
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- or |
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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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model-index: |
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- name: XLS-R-300M - Odia |
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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: or |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 97.91 |
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- name: Test CER |
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type: cer |
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value: 247.09 |
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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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# wav2vec2-large-xls-r-300m-odia |
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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 - OR dataset. |
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It achieves the following results on the evaluation set: |
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``` |
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python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config as --split test --log_outputs |
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``` |
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- WER: 1.0921052631578947 |
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- CER: 2.5547945205479454 |
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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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Training machine details |
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- Platform: Linux-5.11.0-37-generic-x86_64-with-glibc2.10 |
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- CPU cores: 60 |
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- Python version: 3.8.8 |
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- PyTorch version: 1.10.1+cu102 |
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- GPU is visible: True |
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- Transformers version: 4.16.0.dev0 |
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- Datasets version: 1.17.1.dev0 |
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- soundfile version: 0.10.3 |
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Training script |
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```bash |
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python run_speech_recognition_ctc.py \ |
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--dataset_name="mozilla-foundation/common_voice_7_0" \ |
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--model_name_or_path="facebook/wav2vec2-xls-r-300m" \ |
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--dataset_config_name="or" \ |
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--output_dir="./wav2vec2-large-xls-r-300m-odia" \ |
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--overwrite_output_dir \ |
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--num_train_epochs="120" \ |
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--per_device_train_batch_size="16" \ |
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--per_device_eval_batch_size="16" \ |
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--gradient_accumulation_steps="2" \ |
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--learning_rate="7.5e-5" \ |
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--warmup_steps="500" \ |
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--length_column_name="input_length" \ |
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--evaluation_strategy="steps" \ |
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--text_column_name="sentence" \ |
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--chars_to_ignore , ? . ! \- \; \: \" β % β β οΏ½ β \β β¦ \β \' \β \β \ |
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--save_steps="500" \ |
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--eval_steps="500" \ |
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--logging_steps="100" \ |
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--layerdrop="0.0" \ |
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--activation_dropout="0.1" \ |
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--save_total_limit="3" \ |
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--freeze_feature_encoder \ |
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--feat_proj_dropout="0.0" \ |
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--mask_time_prob="0.75" \ |
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--mask_time_length="10" \ |
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--mask_feature_prob="0.25" \ |
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--mask_feature_length="64" \ |
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--gradient_checkpointing \ |
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--use_auth_token \ |
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--fp16 \ |
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--group_by_length \ |
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--do_train --do_eval \ |
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--push_to_hub |
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``` |
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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: 16 |
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- eval_batch_size: 16 |
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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: 500 |
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- num_epochs: 120.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| | eval_loss | eval_wer | eval_runtime | eval_samples_per_second | eval_steps_per_second | epoch | |
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|---:|------------:|-----------:|---------------:|--------------------------:|------------------------:|--------:| |
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| 0 | 3.35224 | 0.998972 | 5.0475 | 22.189 | 1.387 | 29.41 | |
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| 1 | 1.33679 | 0.938335 | 5.0633 | 22.12 | 1.382 | 58.82 | |
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| 2 | 0.737202 | 0.957862 | 5.0913 | 21.998 | 1.375 | 88.24 | |
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| 3 | 0.658212 | 0.96814 | 5.0953 | 21.981 | 1.374 | 117.65 | |
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| 4 | 0.658 | 0.9712 | 5.0953 | 22.115 | 1.382 | 120 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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