🚀 wav2vec2-xls-r-300m-rm-vallader-d1
This is a fine - tuned model for Automatic Speech Recognition on the RM - Vallader language, achieving good performance on Common Voice 8 and related datasets.
✨ Features
- Based on the pre - trained model facebook/wav2vec2-xls-r-300m, fine - tuned on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - RM - VALLADER dataset.
- Suitable for Automatic Speech Recognition tasks in the RM - Vallader language.
📚 Documentation
Model Information
Property |
Details |
Model Type |
wav2vec2-xls-r-300m-rm-vallader-d1 |
Training Datasets |
mozilla-foundation/common_voice_8_0 |
License |
Apache-2.0 |
Tags |
automatic-speech-recognition, mozilla-foundation/common_voice_8_0, generated_from_trainer, rm-vallader, robust-speech-event, model_for_talk, hf-asr-leaderboard |
Evaluation Results
This model achieves the following results on the evaluation set:
The detailed evaluation results on different datasets are as follows:
Task |
Dataset |
Test WER |
Test CER |
Automatic Speech Recognition |
Common Voice 8 (rm - vallader) |
0.26472007722007723 |
0.05860608074430969 |
Automatic Speech Recognition |
Robust Speech Event - Dev Data (vot) |
NA |
NA |
Evaluation Commands
Evaluate on mozilla - foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1 --dataset mozilla-foundation/common_voice_8_0 --config rm-vallader --split test --log_outputs
Evaluate on speech - recognition - community - v2/dev_data
Romansh - Vallader language not found in speech - recognition - community - v2/dev_data
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e - 05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training Results
Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
2.927 |
15.15 |
500 |
2.9196 |
1.0 |
1.3835 |
30.3 |
1000 |
0.5879 |
0.5866 |
0.7415 |
45.45 |
1500 |
0.3077 |
0.3316 |
0.5575 |
60.61 |
2000 |
0.2735 |
0.2954 |
0.4581 |
75.76 |
2500 |
0.2707 |
0.2802 |
0.3977 |
90.91 |
3000 |
0.2785 |
0.2809 |
Framework Versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
📄 License
This model is licensed under the Apache - 2.0 license.