---
language:
- id
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_16_0
- mms
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: breeze-listen-w2v2-id
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - ID
      type: common_voice_16_0
      config: id
      split: test
      args: 'Config: id, Training split: train+validation, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.145808188654721
---

<!-- 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. -->

# breeze-listen-w2v2-id

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - ID dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1253
- Wer: 0.1458

## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.1   | 200  | 3.2671          | 1.0    |
| No log        | 0.19  | 400  | 2.8741          | 1.0007 |
| 3.8381        | 0.29  | 600  | 2.7612          | 0.9955 |
| 3.8381        | 0.38  | 800  | 2.6333          | 0.9981 |
| 2.6996        | 0.48  | 1000 | 2.3074          | 0.9771 |
| 2.6996        | 0.58  | 1200 | 2.0155          | 0.9286 |
| 2.6996        | 0.67  | 1400 | 1.9155          | 0.8947 |
| 2.2919        | 0.77  | 1600 | 1.6412          | 0.8814 |
| 2.2919        | 0.87  | 1800 | 1.4531          | 0.8285 |
| 1.5872        | 0.96  | 2000 | 0.1813          | 0.2060 |
| 1.5872        | 1.06  | 2200 | 0.1636          | 0.1806 |
| 1.5872        | 1.15  | 2400 | 0.1558          | 0.1744 |
| 0.2659        | 1.25  | 2600 | 0.1522          | 0.1647 |
| 0.2659        | 1.35  | 2800 | 0.1553          | 0.1664 |
| 0.2436        | 1.44  | 3000 | 0.1841          | 0.1961 |
| 0.2436        | 1.54  | 3200 | 0.1419          | 0.1640 |
| 0.2436        | 1.64  | 3400 | 0.1456          | 0.1714 |
| 0.2464        | 1.73  | 3600 | 0.1402          | 0.1607 |
| 0.2464        | 1.83  | 3800 | 0.1345          | 0.1528 |
| 0.2292        | 1.92  | 4000 | 0.1342          | 0.1556 |
| 0.2292        | 2.02  | 4200 | 0.1334          | 0.1552 |
| 0.2292        | 2.12  | 4400 | 0.1352          | 0.1543 |
| 0.2209        | 2.21  | 4600 | 0.1350          | 0.1538 |
| 0.2209        | 2.31  | 4800 | 0.1342          | 0.1530 |
| 0.2136        | 2.41  | 5000 | 0.1320          | 0.1540 |
| 0.2136        | 2.5   | 5200 | 0.1369          | 0.1569 |
| 0.2136        | 2.6   | 5400 | 0.1314          | 0.1517 |
| 0.2154        | 2.69  | 5600 | 0.1304          | 0.1506 |
| 0.2154        | 2.79  | 5800 | 0.1320          | 0.1507 |
| 0.2123        | 2.89  | 6000 | 0.1319          | 0.1524 |
| 0.2123        | 2.98  | 6200 | 0.1292          | 0.1524 |
| 0.2123        | 3.08  | 6400 | 0.1283          | 0.1488 |
| 0.2109        | 3.18  | 6600 | 0.1258          | 0.1492 |
| 0.2109        | 3.27  | 6800 | 0.1291          | 0.1488 |
| 0.2103        | 3.37  | 7000 | 0.1278          | 0.1484 |
| 0.2103        | 3.46  | 7200 | 0.1250          | 0.1478 |
| 0.2103        | 3.56  | 7400 | 0.1277          | 0.1482 |
| 0.1986        | 3.66  | 7600 | 0.1256          | 0.1476 |
| 0.1986        | 3.75  | 7800 | 0.1258          | 0.1468 |
| 0.1954        | 3.85  | 8000 | 0.1256          | 0.1465 |
| 0.1954        | 3.95  | 8200 | 0.1253          | 0.1456 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1