metadata
base_model: facebook/mms-1b-all
datasets:
- common_voice_17_0
library_name: transformers
license: cc-by-nc-4.0
metrics:
- wer
- bleu
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-mms-1b-CV17.0-training_set_variations
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: ta
split: validation
args: ta
metrics:
- type: wer
value: 0.3699525493114998
name: Wer
- type: bleu
value: 0.4072321954028345
name: Bleu
wav2vec2-mms-1b-CV17.0-training_set_variations
This model is a fine-tuned version of facebook/mms-1b-all on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2281
- Wer: 0.3700
- Cer: 0.0598
- Bleu: 0.4072
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Bleu |
---|---|---|---|---|---|---|
7.0089 | 1.5625 | 100 | 0.2991 | 0.4260 | 0.0693 | 0.3354 |
0.2087 | 3.125 | 200 | 0.2305 | 0.3968 | 0.0634 | 0.3678 |
0.1924 | 4.6875 | 300 | 0.2291 | 0.3879 | 0.0624 | 0.3799 |
0.1799 | 6.25 | 400 | 0.2290 | 0.3859 | 0.0629 | 0.3830 |
0.1698 | 7.8125 | 500 | 0.2224 | 0.3700 | 0.0600 | 0.4119 |
0.1587 | 9.375 | 600 | 0.2246 | 0.3672 | 0.0601 | 0.4129 |
0.1547 | 10.9375 | 700 | 0.2176 | 0.3855 | 0.0604 | 0.3820 |
0.1446 | 12.5 | 800 | 0.2273 | 0.3907 | 0.0619 | 0.3755 |
0.1404 | 14.0625 | 900 | 0.2239 | 0.3713 | 0.0605 | 0.4035 |
0.1333 | 15.625 | 1000 | 0.2261 | 0.3699 | 0.0602 | 0.4123 |
0.1251 | 17.1875 | 1100 | 0.2281 | 0.3700 | 0.0598 | 0.4072 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1