---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper small Sindhi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs 
      type: google/fleurs
      config: sd_in
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 39.360351975632454
---

<!-- 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. -->

# Whisper small Sindhi

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs sd_in dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8761
- Wer: 39.3604

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0125        | 30.74  | 400  | 0.7639          | 43.5485 |
| 0.0007        | 61.52  | 800  | 0.8301          | 39.4873 |
| 0.0003        | 92.3   | 1200 | 0.8761          | 39.3604 |
| 0.0002        | 123.07 | 1600 | 0.8949          | 39.3604 |
| 0.0002        | 153.81 | 2000 | 0.9013          | 39.4196 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2