---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: windanam_mms-1b-tts_v2
  results: []
datasets:
- cawoylel/FulaSpeechCorpora-splited-noise_augmented
- cawoylel/FulaNewsTextCorporaTTS
language:
- ff
pipeline_tag: automatic-speech-recognition
---

<!-- 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. -->

# windanam_mms-1b-tts_v2

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co./facebook/mms-1b-all) on [FulaSpeechCorpora-splited-noi_augmented](https://huggingface.co./datasets/cawoylel/FulaSpeechCorpora-splited-noise_augmented) and [FulaNewsTextCorporaTTS](https://huggingface.co./datasets/cawoylel/FulaNewsTextCorporaTTS) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4020
- Wer: 0.2416

If you want to test quickly the model, instead of using the huggingface `Inference API`, use this space: https://huggingface.co./spaces/cawoylel/MMS-ASR-Fula

## How to use

**Load Model**

```python
pipe = pipeline("automatic-speech-recognition", model="cawoylel/windanam_mms-1b-tts_v2")
```

**Run Model**

```python
transcription = pipe(your_audio_file)
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.8993        | 0.06  | 2500   | 0.6557          | 0.3266 |
| 0.8056        | 0.12  | 5000   | 0.5806          | 0.3130 |
| 0.7663        | 0.17  | 7500   | 0.5514          | 0.2804 |
| 0.736         | 0.23  | 10000  | 0.5333          | 0.2729 |
| 0.7111        | 0.29  | 12500  | 0.5200          | 0.2686 |
| 0.6867        | 0.35  | 15000  | 0.5043          | 0.2657 |
| 0.6942        | 0.41  | 17500  | 0.4933          | 0.2617 |
| 0.6695        | 0.47  | 20000  | 0.4814          | 0.2595 |
| 0.6464        | 0.52  | 22500  | 0.4733          | 0.2576 |
| 0.694         | 0.58  | 25000  | 0.4618          | 0.2553 |
| 0.6223        | 0.64  | 27500  | 0.4574          | 0.2544 |
| 0.6692        | 0.7   | 30000  | 0.4495          | 0.2526 |
| 0.6337        | 0.76  | 32500  | 0.4454          | 0.2518 |
| 0.6055        | 0.81  | 35000  | 0.4396          | 0.2501 |
| 0.6436        | 0.87  | 37500  | 0.4341          | 0.2498 |
| 0.6389        | 0.93  | 40000  | 0.4304          | 0.2488 |
| 0.6308        | 0.99  | 42500  | 0.4260          | 0.2486 |
| 0.6167        | 1.05  | 45000  | 0.4248          | 0.2469 |
| 0.6253        | 1.11  | 47500  | 0.4201          | 0.2467 |
| 0.603         | 1.16  | 50000  | 0.4179          | 0.2457 |
| 0.5864        | 1.22  | 52500  | 0.4187          | 0.2449 |
| 0.6276        | 1.28  | 55000  | 0.4147          | 0.2446 |
| 0.5901        | 1.34  | 57500  | 0.4151          | 0.2439 |
| 0.5877        | 1.4   | 60000  | 0.4126          | 0.2436 |
| 0.604         | 1.45  | 62500  | 0.4111          | 0.2435 |
| 0.6182        | 1.51  | 65000  | 0.4097          | 0.2436 |
| 0.5745        | 1.57  | 67500  | 0.4074          | 0.2433 |
| 0.5843        | 1.63  | 70000  | 0.4076          | 0.2429 |
| 0.578         | 1.69  | 72500  | 0.4062          | 0.2429 |
| 0.5799        | 1.75  | 75000  | 0.4060          | 0.2425 |
| 0.6017        | 1.8   | 77500  | 0.4047          | 0.2423 |
| 0.5688        | 1.86  | 80000  | 0.4052          | 0.2417 |
| 0.5837        | 1.92  | 82500  | 0.4039          | 0.2418 |
| 0.5801        | 1.98  | 85000  | 0.4038          | 0.2414 |
| 0.5965        | 2.04  | 87500  | 0.4030          | 0.2413 |
| 0.5827        | 2.09  | 90000  | 0.4022          | 0.2414 |
| 0.6043        | 2.15  | 92500  | 0.4020          | 0.2414 |
| 0.609         | 2.21  | 95000  | 0.4021          | 0.2414 |
| 0.5827        | 2.27  | 97500  | 0.4021          | 0.2415 |
| 0.5755        | 2.33  | 100000 | 0.4020          | 0.2416 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0