Xlsr Hungarian New

🚀 Akashpb13/xlsr_hungarian_new

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

🚀 Quick Start

Evaluation Commands

1. To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id Akashpb13/xlsr_hungarian_new --dataset mozilla-foundation/common_voice_8_0 --config hu --split test

✨ Features

• Multilingual Adaptability: Based on the pre - trained model facebook/wav2vec2-xls-r-300m, it can be well adapted to the Hungarian language.
• High - Quality Performance: Achieves good results in multiple evaluation metrics such as WER and CER.

📦 Installation

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

💻 Usage Examples

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

📚 Documentation

Model Description

"facebook/wav2vec2-xls-r-300m" was finetuned.

Intended Uses & Limitations

More information needed

Training and Evaluation Data

Training data - Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

Training Procedure

For creating the train dataset, all possible datasets were appended and 90 - 10 split was used.

Training Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.000095637994662983496
• train_batch_size: 16
• eval_batch_size: 16
• seed: 13
• gradient_accumulation_steps: 16
• lr_scheduler_type: cosine_with_restarts
• lr_scheduler_warmup_steps: 500
• num_epochs: 100
• mixed_precision_training: Native AMP

Training Results

Framework Versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.0+cu102
• Datasets 1.18.3
• Tokenizers 0.10.3

🔧 Technical Details

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - hu dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets):

• Loss: 0.197464
• Wer: 0.330094

Model Index

• name: Akashpb13/xlsr_hungarian_new results:
  • task: name: Automatic Speech Recognition type: automatic - speech - recognition dataset: name: Common Voice 8 type: mozilla - foundation/common_voice_8_0 args: hu metrics:
    • name: Test WER type: wer value: 0.2851621517163838
    • name: Test CER type: cer value: 0.06112982522287432
  • task: name: Automatic Speech Recognition type: automatic - speech - recognition dataset: name: Robust Speech Event - Dev Data type: speech - recognition - community - v2/dev_data args: hu metrics:
    • name: Test WER type: wer value: 0.2851621517163838
    • name: Test CER type: cer value: 0.06112982522287432
  • task: name: Automatic Speech Recognition type: automatic - speech - recognition dataset: name: Robust Speech Event - Test Data type: speech - recognition - community - v2/eval_data args: hu metrics:
    • name: Test WER type: wer value: 47.15

📄 License

This model is licensed under the Apache 2.0 license.