README.md · AlexN/xls-r-300m-pt at f9aacd31c3e0dd229775cb2a64b95ff49d6ab4d4

metadata

language:
  - pt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: xls-r-300m-pt
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8.0 fr
          type: mozilla-foundation/common_voice_8_0
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 19.361
          - name: Test CER
            type: cer
            value: 5.533
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: fr
        metrics:
          - name: Validation WER
            type: wer
            value: 47.812
          - name: Validation CER
            type: cer
            value: 18.805

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset. It achieves the following results on the evaluation set:

Loss: 0.2290
Wer: 0.2382

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1500
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.0952	0.64	500	3.0982	1.0
1.7975	1.29	1000	0.7887	0.5651
1.4138	1.93	1500	0.5238	0.4389
1.344	2.57	2000	0.4775	0.4318
1.2737	3.21	2500	0.4648	0.4075
1.2554	3.86	3000	0.4069	0.3678
1.1996	4.5	3500	0.3914	0.3668
1.1427	5.14	4000	0.3694	0.3572
1.1372	5.78	4500	0.3568	0.3501
1.0831	6.43	5000	0.3331	0.3253
1.1074	7.07	5500	0.3332	0.3352
1.0536	7.71	6000	0.3131	0.3152
1.0248	8.35	6500	0.3024	0.3023
1.0075	9.0	7000	0.2948	0.3028
0.979	9.64	7500	0.2796	0.2853
0.9594	10.28	8000	0.2719	0.2789
0.9172	10.93	8500	0.2620	0.2695
0.9047	11.57	9000	0.2537	0.2596
0.8777	12.21	9500	0.2438	0.2525
0.8629	12.85	10000	0.2409	0.2493
0.8575	13.5	10500	0.2366	0.2440
0.8361	14.14	11000	0.2317	0.2385
0.8126	14.78	11500	0.2290	0.2382

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0