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---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
- bleu
model-index:
- name: wav2vec2-mms-1b-CV17.0-training_set_variations
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: ta
split: validation
args: ta
metrics:
- name: Wer
type: wer
value: 0.35028609973948643
- name: Bleu
type: bleu
value: 0.4335646189418293
---
<!-- 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: 0.1970
- Wer: 0.3503
- Cer: 0.0557
- Bleu: 0.4336
## 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 |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| 7.329 | 0.0977 | 100 | 0.3217 | 0.4338 | 0.0716 | 0.3272 |
| 0.2154 | 0.1953 | 200 | 0.2314 | 0.3794 | 0.0634 | 0.3999 |
| 0.189 | 0.2930 | 300 | 0.2188 | 0.3656 | 0.0592 | 0.4166 |
| 0.1975 | 0.3906 | 400 | 0.2212 | 0.3763 | 0.0608 | 0.4032 |
| 0.1813 | 0.4883 | 500 | 0.2117 | 0.3634 | 0.0585 | 0.4171 |
| 0.1791 | 0.5859 | 600 | 0.2074 | 0.3590 | 0.0578 | 0.4220 |
| 0.187 | 0.6836 | 700 | 0.2087 | 0.3607 | 0.0582 | 0.4188 |
| 0.1789 | 0.7812 | 800 | 0.2064 | 0.3542 | 0.0568 | 0.4327 |
| 0.1704 | 0.8789 | 900 | 0.2076 | 0.3661 | 0.0587 | 0.4095 |
| 0.1813 | 0.9766 | 1000 | 0.2044 | 0.3589 | 0.0574 | 0.4200 |
| 0.1633 | 1.0742 | 1100 | 0.2029 | 0.3582 | 0.0575 | 0.4220 |
| 0.1699 | 1.1719 | 1200 | 0.2034 | 0.3537 | 0.0566 | 0.4335 |
| 0.1822 | 1.2695 | 1300 | 0.2037 | 0.3589 | 0.0578 | 0.4227 |
| 0.1654 | 1.3672 | 1400 | 0.2028 | 0.3549 | 0.0568 | 0.4288 |
| 0.1696 | 1.4648 | 1500 | 0.2011 | 0.3579 | 0.0567 | 0.4199 |
| 0.1622 | 1.5625 | 1600 | 0.1999 | 0.3568 | 0.0570 | 0.4228 |
| 0.1742 | 1.6602 | 1700 | 0.1983 | 0.3490 | 0.0559 | 0.4365 |
| 0.1581 | 1.7578 | 1800 | 0.1973 | 0.3511 | 0.0558 | 0.4329 |
| 0.1616 | 1.8555 | 1900 | 0.1970 | 0.3482 | 0.0556 | 0.4381 |
| 0.1607 | 1.9531 | 2000 | 0.1970 | 0.3503 | 0.0557 | 0.4336 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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