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xls-r-1b-cv_8-fr

Automatic Speech Recognition
Transformers
PyTorch
French
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
xls-r-1b-cv_8-fr / README.md
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metadata
language:
  - fr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
model-index:
  - name: XLS-R-1B - French
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 18.33
          - name: Test CER
            type: cer
            value: 5.6
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 60.25
          - name: Test CER
            type: cer
            value: 15.68

Model description

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_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: 4.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.9827 0.29 1000 inf 0.2937
1.0203 0.57 2000 inf 0.2711
1.0048 0.86 3000 inf 0.2620
0.9858 1.15 4000 inf 0.2522
0.9709 1.43 5000 inf 0.2365
0.9347 1.72 6000 inf 0.2332
0.9256 2.01 7000 inf 0.2261
0.8936 2.29 8000 inf 0.2203
0.877 2.58 9000 inf 0.2096
0.8393 2.87 10000 inf 0.2017
0.8156 3.15 11000 inf 0.1936
0.8015 3.44 12000 inf 0.1880
0.774 3.73 13000 inf 0.1834

It achieves the best result for STEP 13000 on the validation set:

  • Wer: 0.1834

Some problems occur when calculating the validation loss.

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3.dev0
  • Tokenizers 0.11.0

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8 with split test
python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset mozilla-foundation/common_voice_8_0 --config fr --split test
  1. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0