---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: hindi_fb1mms_timebalancedreg
  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: 0.4259275985404097
---

<!-- 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. -->

# hindi_fb1mms_timebalancedreg

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: 0.7182
- Wer: 0.4259

## 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: 0.0002
- 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_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 4.087         | 1.0191  | 400   | 3.5884          | 0.9998 |
| 3.935         | 2.0382  | 800   | 3.4190          | 0.9959 |
| 3.3712        | 3.0573  | 1200  | 3.3003          | 0.9709 |
| 3.2027        | 4.0764  | 1600  | 2.8687          | 0.9861 |
| 1.4667        | 5.0955  | 2000  | 0.6547          | 0.4129 |
| 1.2468        | 6.1146  | 2400  | 0.6031          | 0.3955 |
| 1.2401        | 7.1338  | 2800  | 0.6334          | 0.4172 |
| 1.2952        | 8.1529  | 3200  | 0.6857          | 0.4238 |
| 1.2466        | 9.1720  | 3600  | 0.7279          | 0.4361 |
| 1.2094        | 10.1911 | 4000  | 0.6768          | 0.4140 |
| 1.1764        | 11.2102 | 4400  | 0.6735          | 0.4234 |
| 1.1491        | 12.2293 | 4800  | 0.7047          | 0.4334 |
| 1.1504        | 13.2484 | 5200  | 0.6704          | 0.4215 |
| 1.1656        | 14.2675 | 5600  | 0.6684          | 0.4207 |
| 1.1666        | 15.2866 | 6000  | 0.7367          | 0.4339 |
| 1.1512        | 16.3057 | 6400  | 0.7384          | 0.4386 |
| 1.1646        | 17.3248 | 6800  | 0.7087          | 0.4251 |
| 1.1407        | 18.3439 | 7200  | 0.7192          | 0.4329 |
| 1.1207        | 19.3631 | 7600  | 0.7141          | 0.4236 |
| 1.1145        | 20.3822 | 8000  | 0.7503          | 0.4374 |
| 1.1138        | 21.4013 | 8400  | 0.7235          | 0.4278 |
| 1.1091        | 22.4204 | 8800  | 0.7468          | 0.4404 |
| 1.1255        | 23.4395 | 9200  | 0.7177          | 0.4264 |
| 1.0959        | 24.4586 | 9600  | 0.7050          | 0.4191 |
| 1.106         | 25.4777 | 10000 | 0.7420          | 0.4337 |
| 1.0949        | 26.4968 | 10400 | 0.7063          | 0.4223 |
| 1.1142        | 27.5159 | 10800 | 0.7170          | 0.4257 |
| 1.1076        | 28.5350 | 11200 | 0.7223          | 0.4267 |
| 1.1028        | 29.5541 | 11600 | 0.7182          | 0.4259 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1