Wav2vec2 Large Xls R 300m Bg D2

🚀 wav2vec2-large-xls-r-300m-bg-d2

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - BG dataset, aiming to provide high - quality automatic speech recognition for Bulgarian.

✨ Features

• Multilingual Adaptability: Based on the pre - trained model wav2vec2-xls-r-300m, it has strong adaptability to different languages.
• High - Precision Recognition: Achieved low Word Error Rate (WER) and Character Error Rate (CER) on the evaluation set, demonstrating excellent recognition performance.

📦 Installation

There is no specific installation content provided in the original document, so this section is skipped.

💻 Usage Examples

There is no code example provided in the original document, so this section is skipped.

📚 Documentation

Model Information

Model Evaluation Results

• Common Voice 8 Dataset:
  • Test WER: 0.28775471338792613
  • Test CER: 0.06861971204625049
• Robust Speech Event - Dev Data:
  • Test WER: 0.49783147459727384
  • Test CER: 0.1591062599627158
• Robust Speech Event - Test Data:
  • Test WER: 51.25

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-bg-d2 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs

2. To evaluate on speech - recognition - community - v2/dev_data

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1

Training Hyperparameters

• learning_rate: 0.00025
• 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: 700
• num_epochs: 35
• mixed_precision_training: Native AMP

Training Results

Framework Versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0

🔧 Technical Details

This model is a fine - tuned version of wav2vec2 - large - xls - r - 300m on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - BG dataset. Through a series of training hyperparameter settings and optimization strategies, it has achieved good performance on the Bulgarian automatic speech recognition task.

📄 License

This model is released under the Apache - 2.0 license.