Xls R 1b Cv 8 Fr

🚀 XLS-R-1B - French

This is a fine - tuned model for automatic speech recognition, leveraging the power of the pre - trained facebook/wav2vec2-xls-r-1b on the French dataset MOZILLA - FOUNDATION/COMMON_VOICE_8_0. It offers high - quality speech recognition capabilities with excellent performance metrics.

✨ Features

• Automatic Speech Recognition: Specialized for French speech recognition tasks.
• Fine - Tuned on Quality Data: Trained on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 French dataset.
• Multiple Evaluation Metrics: Evaluated using WER and CER, both with and without a language model.

📦 Installation

No installation steps provided in the original document, so this section is skipped.

💻 Usage Examples

No usage code examples provided in the original document, so this section is skipped.

📚 Documentation

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: 6.0
• mixed_precision_training: Native AMP

Training results

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

• Wer: 0.1834

Some problem occurs 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

2. 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

Evaluation Results

Without LM:

With LM:

🔧 Technical Details

Model Index

Task Results

The model has been evaluated on multiple tasks with different datasets:

• Automatic Speech Recognition on Common Voice 8:
  • Test WER (with LM): 15.4
  • Test CER (with LM): 5.36
• Automatic Speech Recognition on Robust Speech Event - Dev Data:
  • Test WER (with LM): 25.05
  • Test CER (with LM): 12.45
• Automatic Speech Recognition on Robust Speech Event - Test Data:
  • Test WER: 27.1

📄 License

This project is licensed under the Apache - 2.0 license.