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---
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: 0.4250355227574827
name: Wer
- type: bleu
value: 0.3461897882903843
name: Bleu
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-mms-1b-CV17.0-training_set_variations
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3320
- Wer: 0.4250
- Cer: 0.0720
- Bleu: 0.3462
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 11.5156 | 6.25 | 50 | 5.5076 | 1.0 | 0.9701 | 0.0 |
| 1.8133 | 12.5 | 100 | 0.2759 | 0.4067 | 0.0674 | 0.3610 |
| 0.1751 | 18.75 | 150 | 0.2414 | 0.3828 | 0.0639 | 0.3924 |
| 0.1315 | 25.0 | 200 | 0.2556 | 0.3887 | 0.0649 | 0.3901 |
| 0.1 | 31.25 | 250 | 0.2842 | 0.4168 | 0.0700 | 0.3520 |
| 0.0842 | 37.5 | 300 | 0.2997 | 0.4133 | 0.0699 | 0.3571 |
| 0.0717 | 43.75 | 350 | 0.3210 | 0.4260 | 0.0732 | 0.3431 |
| 0.0608 | 50.0 | 400 | 0.3320 | 0.4250 | 0.0720 | 0.3462 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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