Wav2vec2 Xls R 300m Zh HK V2

🚀 Wav2Vec2 XLS-R 300M Cantonese (zh-HK)

Wav2Vec2 XLS-R 300M Cantonese (zh-HK) is an automatic speech recognition model based on the XLS-R architecture, fine-tuned on the zh-HK subset of the Common Voice dataset.

🚀 Quick Start

Wav2Vec2 XLS-R 300M Cantonese (zh-HK) is an automatic speech recognition model based on the XLS-R architecture. This model is a fine-tuned version of Wav2Vec2-XLS-R-300M on the zh-HK subset of the Common Voice dataset.

This model was trained using HuggingFace's PyTorch framework and is part of the Robust Speech Challenge Event organized by HuggingFace. All training was done on a Tesla V100, sponsored by OVH.

All necessary scripts used for training could be found in the Files and versions tab, as well as the Training metrics logged via Tensorboard.

✨ Features

• Based on the XLS-R architecture, suitable for automatic speech recognition tasks.
• Fine-tuned on the zh-HK subset of the Common Voice dataset, optimized for Cantonese speech recognition.
• Trained using HuggingFace's PyTorch framework and participated in the Robust Speech Challenge Event.

📚 Documentation

Model

Evaluation Results

The model achieves the following results on evaluation:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

📄 License

This project is licensed under the Apache-2.0 license.

🔧 Technical Details

Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

Authors

Wav2Vec2 XLS-R 300M Cantonese (zh-HK) was trained and evaluated by Wilson Wongso. All computation and development are done on OVH Cloud.

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.4.dev0
• Tokenizers 0.11.0