---
library_name: transformers
language:
- mr
license: apache-2.0
base_model: Viraj008/whisper-small-mr_v4
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
- fsicoli/common_voice_19_0
- ylacombe/google-marathi
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small MR v5 - Viraj Patil
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: 'Common Voice 17.0, google/fleurs '
      type: mozilla-foundation/common_voice_17_0
      config: mr
      split: None
      args: 'config: mr, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 34.12423353772328
---

<!-- 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 MR v5 - Viraj Patil

This model is a fine-tuned version of [Viraj008/whisper-small-mr_v4](https://huggingface.co/Viraj008/whisper-small-mr_v4) on the Common Voice 17.0, google/fleurs  dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2496
- Wer: 34.1242

## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0573        | 0.5355 | 1000 | 0.2405          | 36.3370 |
| 0.0314        | 1.0710 | 2000 | 0.2484          | 35.4172 |
| 0.0298        | 1.6064 | 3000 | 0.2410          | 35.1640 |
| 0.0182        | 2.1419 | 4000 | 0.2496          | 34.1242 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0