đ wav2vec2-large-xls-r-300m-gn-k1
This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - GN dataset, designed for automatic speech recognition.
đ Quick Start
This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - GN dataset. It achieves the following results on the evaluation set:
⨠Features
- Multilingual Adaptability: Fine - tuned on the GN dataset of MOZILLA - FOUNDATION/COMMON_VOICE_8_0, suitable for speech recognition in the GN language.
- High - Quality Results: Achieved good performance in evaluation metrics such as WER and CER.
đ Documentation
Evaluation Commands
- To evaluate on mozilla - foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs
- To evaluate on speech - recognition - community - v2/dev_data
NA
Training hyperparameters
The following hyperparameters were used during training:
Property |
Details |
learning_rate |
0.00018 |
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 |
600 |
num_epochs |
200 |
mixed_precision_training |
Native AMP |
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
15.9402 |
8.32 |
100 |
6.9185 |
1.0 |
4.6367 |
16.64 |
200 |
3.7416 |
1.0 |
3.4337 |
24.96 |
300 |
3.2581 |
1.0 |
3.2307 |
33.32 |
400 |
2.8008 |
1.0 |
1.3182 |
41.64 |
500 |
0.8359 |
0.8171 |
0.409 |
49.96 |
600 |
0.8470 |
0.8323 |
0.2573 |
58.32 |
700 |
0.7823 |
0.7576 |
0.1969 |
66.64 |
800 |
0.8306 |
0.7424 |
0.1469 |
74.96 |
900 |
0.9225 |
0.7713 |
0.1172 |
83.32 |
1000 |
0.7903 |
0.6951 |
0.1017 |
91.64 |
1100 |
0.8519 |
0.6921 |
0.0851 |
99.96 |
1200 |
0.8129 |
0.6646 |
0.071 |
108.32 |
1300 |
0.8614 |
0.7043 |
0.061 |
116.64 |
1400 |
0.8414 |
0.6921 |
0.0552 |
124.96 |
1500 |
0.8649 |
0.6905 |
0.0465 |
133.32 |
1600 |
0.8575 |
0.6646 |
0.0381 |
141.64 |
1700 |
0.8802 |
0.6723 |
0.0338 |
149.96 |
1800 |
0.8731 |
0.6845 |
0.0306 |
158.32 |
1900 |
0.9003 |
0.6585 |
0.0236 |
166.64 |
2000 |
0.9408 |
0.6616 |
0.021 |
174.96 |
2100 |
0.9353 |
0.6723 |
0.0212 |
183.32 |
2200 |
0.9269 |
0.6570 |
0.0191 |
191.64 |
2300 |
0.9277 |
0.6662 |
0.0161 |
199.96 |
2400 |
0.9220 |
0.6631 |
Framework versions
Property |
Details |
Transformers |
4.16.2 |
Pytorch |
1.10.0+cu111 |
Datasets |
1.18.3 |
Tokenizers |
0.11.0 |
đ License
This project is licensed under the Apache - 2.0 license.