Model description
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FR dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 2.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
3.495 | 0.16 | 500 | 3.3883 | 1.0 |
2.9095 | 0.32 | 1000 | 2.9152 | 1.0000 |
1.8434 | 0.49 | 1500 | 1.0473 | 0.7446 |
1.4298 | 0.65 | 2000 | 0.5729 | 0.5130 |
1.1937 | 0.81 | 2500 | 0.3795 | 0.3450 |
1.1248 | 0.97 | 3000 | 0.3321 | 0.3052 |
1.0835 | 1.13 | 3500 | 0.3038 | 0.2805 |
1.0479 | 1.3 | 4000 | 0.2910 | 0.2689 |
1.0413 | 1.46 | 4500 | 0.2798 | 0.2593 |
1.014 | 1.62 | 5000 | 0.2727 | 0.2512 |
1.004 | 1.78 | 5500 | 0.2646 | 0.2471 |
0.9949 | 1.94 | 6000 | 0.2619 | 0.2457 |
It achieves the best result on STEP 6000 on the validation set:
- Loss: 0.2619
- Wer: 0.2457
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_7
with splittest
python eval.py --model_id Plim/xls-r-300m-fr --dataset mozilla-foundation/common_voice_7_0 --config fr --split test
- To evaluate on
speech-recognition-community-v2/dev_data
python eval.py --model_id Plim/xls-r-300m-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
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Dataset used to train Plim/xls-r-300m-fr
Evaluation results
- Test WER on Common Voice 7self-reported24.560
- Test CER on Common Voice 7self-reported7.300
- Test WER on Robust Speech Event - Dev Dataself-reported63.620
- Test CER on Robust Speech Event - Dev Dataself-reported17.200
- Test WER on Robust Speech Event - Test Dataself-reported66.450