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--- |
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language: |
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- ia |
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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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- robust-speech-event |
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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-ia' |
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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: ia |
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metrics: |
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- name: Test WER using LM |
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type: wer |
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value: 8.6074 |
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- name: Test CER using LM |
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type: cer |
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value: 2.4147 |
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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-ia |
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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 common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1452 |
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- Wer: 0.1253 |
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## Training Procedure |
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Training is conducted in Google Colab, the training notebook provided in the repo |
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## Training and evaluation data |
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Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua) |
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Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_ia.ipynb" |
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Test WER without LM |
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wer = 20.1776 % |
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cer = 4.7205 % |
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Test WER using |
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wer = 8.6074 % |
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cer = 2.4147 % |
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evaluation using eval.py |
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``` |
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huggingface-cli login #login to huggingface for getting auth token to access the common voice v8 |
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#running with LM |
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python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test --lm |
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# running without LM |
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python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 4 |
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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: 400 |
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- num_epochs: 30 |
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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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| 7.432 | 1.87 | 400 | 2.9636 | 1.0 | |
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| 2.6922 | 3.74 | 800 | 2.2111 | 0.9977 | |
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| 1.2581 | 5.61 | 1200 | 0.4864 | 0.4028 | |
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| 0.6232 | 7.48 | 1600 | 0.2807 | 0.2413 | |
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| 0.4479 | 9.35 | 2000 | 0.2219 | 0.1885 | |
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| 0.3654 | 11.21 | 2400 | 0.1886 | 0.1606 | |
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| 0.323 | 13.08 | 2800 | 0.1716 | 0.1444 | |
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| 0.2935 | 14.95 | 3200 | 0.1687 | 0.1443 | |
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| 0.2707 | 16.82 | 3600 | 0.1632 | 0.1382 | |
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| 0.2559 | 18.69 | 4000 | 0.1507 | 0.1337 | |
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| 0.2433 | 20.56 | 4400 | 0.1572 | 0.1358 | |
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| 0.2338 | 22.43 | 4800 | 0.1489 | 0.1305 | |
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| 0.2258 | 24.3 | 5200 | 0.1485 | 0.1278 | |
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| 0.2218 | 26.17 | 5600 | 0.1470 | 0.1272 | |
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| 0.2169 | 28.04 | 6000 | 0.1470 | 0.1270 | |
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| 0.2117 | 29.91 | 6400 | 0.1452 | 0.1253 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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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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