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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[:5%]+validation[20%:25%]+validation[60%:65%]+validation[90%:]
args: ta
metrics:
- name: Wer
type: wer
value: 0.5119016249451032
- name: Bleu
type: bleu
value: 0.24178033350654143
---
<!-- 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: 0.4958
- Wer: 0.5119
- Cer: 0.0973
- Bleu: 0.2418
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 6.9079 | 100.0 | 100 | 0.3463 | 0.4393 | 0.0786 | 0.3177 |
| 0.0658 | 200.0 | 200 | 0.3912 | 0.4538 | 0.0839 | 0.3158 |
| 0.0346 | 300.0 | 300 | 0.4707 | 0.5046 | 0.0947 | 0.2477 |
| 0.0265 | 400.0 | 400 | 0.4906 | 0.5137 | 0.0967 | 0.2393 |
| 0.0184 | 500.0 | 500 | 0.5407 | 0.5240 | 0.1005 | 0.2301 |
| 0.0158 | 600.0 | 600 | 0.4958 | 0.5119 | 0.0973 | 0.2418 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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