đ wav2vec2-xls-r-300m-gn-cv8-3
This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It can be used for automatic speech recognition tasks and has achieved certain results on the evaluation set.
đ Documentation
Model Information
Property |
Details |
Language |
gn |
License |
apache - 2.0 |
Tags |
automatic - speech - recognition, generated_from_trainer, gn, robust - speech - event, hf - asr - leaderboard |
Datasets |
mozilla - foundation/common_voice_8_0 |
Model Results
This model achieves the following results on the evaluation set:
Model Index
- Name: wav2vec2-xls-r-300m-gn-cv8-3
- Results:
- Task:
- Name: Automatic Speech Recognition
- Type: automatic - speech - recognition
- Dataset:
- Name: Common Voice 8.0
- Type: mozilla - foundation/common_voice_8_0
- Args: gn
- Metrics:
- Name: Test WER
- Type: wer
- Value: 76.68
đ§ Technical Details
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: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
19.9125 |
5.54 |
100 |
5.4279 |
1.0 |
3.8031 |
11.11 |
200 |
3.3070 |
1.0 |
3.3783 |
16.65 |
300 |
3.2450 |
1.0 |
3.3472 |
22.22 |
400 |
3.2424 |
1.0 |
3.2714 |
27.76 |
500 |
3.1100 |
1.0 |
3.2367 |
33.32 |
600 |
3.1091 |
1.0 |
3.1968 |
38.86 |
700 |
3.1013 |
1.0 |
3.2004 |
44.43 |
800 |
3.1173 |
1.0 |
3.1656 |
49.97 |
900 |
3.0682 |
1.0 |
3.1563 |
55.54 |
1000 |
3.0457 |
1.0 |
3.1356 |
61.11 |
1100 |
3.0139 |
1.0 |
3.086 |
66.65 |
1200 |
2.8108 |
1.0 |
2.954 |
72.22 |
1300 |
2.3238 |
1.0 |
2.6125 |
77.76 |
1400 |
1.6461 |
1.0 |
2.3296 |
83.32 |
1500 |
1.2834 |
0.9744 |
2.1345 |
88.86 |
1600 |
1.1091 |
0.9693 |
2.0346 |
94.43 |
1700 |
1.0273 |
0.9233 |
1.9611 |
99.97 |
1800 |
0.9642 |
0.9182 |
1.9066 |
105.54 |
1900 |
0.9590 |
0.9105 |
1.8178 |
111.11 |
2000 |
0.9679 |
0.9028 |
1.7799 |
116.65 |
2100 |
0.9007 |
0.8619 |
1.7726 |
122.22 |
2200 |
0.9689 |
0.8951 |
1.7389 |
127.76 |
2300 |
0.8876 |
0.8593 |
1.7151 |
133.32 |
2400 |
0.8716 |
0.8542 |
1.6842 |
138.86 |
2500 |
0.9536 |
0.8772 |
1.6449 |
144.43 |
2600 |
0.9296 |
0.8542 |
1.5978 |
149.97 |
2700 |
0.8895 |
0.8440 |
1.6515 |
155.54 |
2800 |
0.9162 |
0.8568 |
1.6586 |
161.11 |
2900 |
0.9039 |
0.8568 |
1.5966 |
166.65 |
3000 |
0.8627 |
0.8542 |
1.5695 |
172.22 |
3100 |
0.9549 |
0.8824 |
1.5699 |
177.76 |
3200 |
0.9332 |
0.8517 |
1.5297 |
183.32 |
3300 |
0.9163 |
0.8338 |
1.5367 |
188.86 |
3400 |
0.8822 |
0.8312 |
1.5586 |
194.43 |
3500 |
0.9217 |
0.8363 |
1.5429 |
199.97 |
3600 |
0.9564 |
0.8568 |
1.5273 |
205.54 |
3700 |
0.9508 |
0.8542 |
1.5043 |
211.11 |
3800 |
0.9374 |
0.8542 |
1.4724 |
216.65 |
3900 |
0.9622 |
0.8619 |
1.4794 |
222.22 |
4000 |
0.9550 |
0.8363 |
1.4843 |
227.76 |
4100 |
0.9577 |
0.8465 |
1.4781 |
233.32 |
4200 |
0.9543 |
0.8440 |
1.4507 |
238.86 |
4300 |
0.9553 |
0.8491 |
1.4997 |
244.43 |
4400 |
0.9728 |
0.8491 |
1.4371 |
249.97 |
4500 |
0.9543 |
0.8670 |
1.4825 |
255.54 |
4600 |
0.9636 |
0.8619 |
1.4187 |
261.11 |
4700 |
0.9609 |
0.8440 |
1.4363 |
266.65 |
4800 |
0.9567 |
0.8593 |
1.4463 |
272.22 |
4900 |
0.9581 |
0.8542 |
1.4117 |
277.76 |
5000 |
0.9517 |
0.8542 |
Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0
đ License
This model is released under the apache - 2.0 license.