---
language:
- te
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- INDIC_SUPERB_MUCS_OPENSLR
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Swecha Gonthuka - Limited Release
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Crowed sourced dataset
      type: INDIC SUPERB, MUCS, OPENSLR
      config: None
      split: None
      args: 'config: te, split: test'
    metrics:
    - type: wer
      value: 28.59758159493464
      name: Wer
---

<!-- 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. -->

# Swecha Gonthuka - Limited Release

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Crowed sourced dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Wer: 28.5976

# Collaborators
Trained by Naga Budigam

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 3000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0729        | 1.08  | 5000  | 0.0934          | 33.3306 |
| 0.0519        | 2.16  | 10000 | 0.0768          | 28.5976 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2