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wav2vec2-large-mms-1b-nhi-ft-3hrs
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---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-nhi-ft-3hrs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.6467391304347826
---
<!-- 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-large-mms-1b-nhi-ft-3hrs
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7237
- Wer: 0.6467
## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.6463 | 0.4149 | 100 | 1.0969 | 0.7919 |
| 1.1669 | 0.8299 | 200 | 0.8578 | 0.7023 |
| 0.987 | 1.2448 | 300 | 0.7607 | 0.6603 |
| 0.9324 | 1.6598 | 400 | 0.7237 | 0.6467 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.4.0
- Datasets 2.19.1
- Tokenizers 0.19.1