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xls-r-300m-fr

Automatic Speech Recognition
Transformers
PyTorch
French
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
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metadata
language:
  - fr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - French
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 24.56
          - name: Test CER
            type: cer
            value: 7.3
      - 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: 63.62
          - name: Test CER
            type: cer
            value: 17.2
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 66.45

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

  1. To evaluate on mozilla-foundation/common_voice_7 with split test
python eval.py --model_id Plim/xls-r-300m-fr --dataset mozilla-foundation/common_voice_7_0 --config fr --split test
  1. 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