readme: ---
  language:
  - br
  license: apache-2.0
  tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-br-d10
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice 8
        args: br
      metrics:
      - type: wer
        value: 0.5230357484228637
        name: Test WER
      - name: Test CER
        type: cer
        value: 0.1880661144228536
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: br
      metrics:
      - name: Test WER
        type: wer
        value: NA
      - name: Test CER
        type: cer
        value: NA
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-xls-r-300m-br-d10
  
  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 - BR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1382
  - Wer: 0.4895
  
  
  ### Evaluation Commands
  
  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
  
  python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d10 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  Breton language isn't available in speech-recognition-community-v2/dev_data
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0004
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 800
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 13.611        | 0.68  | 100  | 5.8492          | 1.0    |
  | 3.8176        | 1.35  | 200  | 3.2181          | 1.0    |
  | 3.0457        | 2.03  | 300  | 3.0902          | 1.0    |
  | 2.2632        | 2.7   | 400  | 1.4882          | 0.9426 |
  | 1.1965        | 3.38  | 500  | 1.1396          | 0.7950 |
  | 0.984         | 4.05  | 600  | 1.0216          | 0.7583 |
  | 0.8036        | 4.73  | 700  | 1.0258          | 0.7202 |
  | 0.7061        | 5.41  | 800  | 0.9710          | 0.6820 |
  | 0.689         | 6.08  | 900  | 0.9731          | 0.6488 |
  | 0.6063        | 6.76  | 1000 | 0.9442          | 0.6569 |
  | 0.5215        | 7.43  | 1100 | 1.0221          | 0.6671 |
  | 0.4965        | 8.11  | 1200 | 0.9266          | 0.6181 |
  | 0.4321        | 8.78  | 1300 | 0.9050          | 0.5991 |
  | 0.3762        | 9.46  | 1400 | 0.9801          | 0.6134 |
  | 0.3747        | 10.14 | 1500 | 0.9210          | 0.5747 |
  | 0.3554        | 10.81 | 1600 | 0.9720          | 0.6051 |
  | 0.3148        | 11.49 | 1700 | 0.9672          | 0.6099 |
  | 0.3176        | 12.16 | 1800 | 1.0120          | 0.5966 |
  | 0.2915        | 12.84 | 1900 | 0.9490          | 0.5653 |
  | 0.2696        | 13.51 | 2000 | 0.9394          | 0.5819 |
  | 0.2569        | 14.19 | 2100 | 1.0197          | 0.5667 |
  | 0.2395        | 14.86 | 2200 | 0.9771          | 0.5608 |
  | 0.2367        | 15.54 | 2300 | 1.0516          | 0.5678 |
  | 0.2153        | 16.22 | 2400 | 1.0097          | 0.5679 |
  | 0.2092        | 16.89 | 2500 | 1.0143          | 0.5430 |
  | 0.2046        | 17.57 | 2600 | 1.0884          | 0.5631 |
  | 0.1937        | 18.24 | 2700 | 1.0113          | 0.5648 |
  | 0.1752        | 18.92 | 2800 | 1.0056          | 0.5470 |
  | 0.164         | 19.59 | 2900 | 1.0340          | 0.5508 |
  | 0.1723        | 20.27 | 3000 | 1.0743          | 0.5615 |
  | 0.1535        | 20.95 | 3100 | 1.0495          | 0.5465 |
  | 0.1432        | 21.62 | 3200 | 1.0390          | 0.5333 |
  | 0.1561        | 22.3  | 3300 | 1.0798          | 0.5590 |
  | 0.1384        | 22.97 | 3400 | 1.1716          | 0.5449 |
  | 0.1359        | 23.65 | 3500 | 1.1154          | 0.5420 |
  | 0.1356        | 24.32 | 3600 | 1.0883          | 0.5387 |
  | 0.1355        | 25.0  | 3700 | 1.1114          | 0.5504 |
  | 0.1158        | 25.68 | 3800 | 1.1171          | 0.5388 |
  | 0.1166        | 26.35 | 3900 | 1.1335          | 0.5403 |
  | 0.1165        | 27.03 | 4000 | 1.1374          | 0.5248 |
  | 0.1064        | 27.7  | 4100 | 1.0336          | 0.5298 |
  | 0.0987        | 28.38 | 4200 | 1.0407          | 0.5216 |
  | 0.104         | 29.05 | 4300 | 1.1012          | 0.5350 |
  | 0.0894        | 29.73 | 4400 | 1.1016          | 0.5310 |
  | 0.0912    