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---
language:
- te
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- openslr
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-small-telugu-large-data
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: openslr
config: te_in
split: None
metrics:
- name: Wer
type: wer
value: 38.84604916991744
---
<!-- 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-telugu-large-data
This [model](steja/whisper-small-telugu-large-data) is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs and openslr dataset in telugu.
It achieves the following results on the evaluation set (google/fleurs, test set):
- Loss: 0.3310
- Wer: 38.8460
[openai/whisper-small](https://huggingface.co/openai/whisper-small) has the following zero shot performance on google/fleurs test set:
- Wer: 117.91
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.128 | 2.27 | 500 | 0.2015 | 45.1692 |
| 0.0462 | 4.55 | 1000 | 0.1877 | 41.1050 |
| 0.0184 | 6.82 | 1500 | 0.2241 | 40.5153 |
| 0.0045 | 9.09 | 2000 | 0.2590 | 39.7260 |
| 0.0019 | 11.36 | 2500 | 0.2824 | 39.0819 |
| 0.0006 | 13.64 | 3000 | 0.3002 | 38.9096 |
| 0.0002 | 15.91 | 3500 | 0.3141 | 38.5920 |
| 0.0001 | 18.18 | 4000 | 0.3232 | 38.7463 |
| 0.0001 | 20.45 | 4500 | 0.3289 | 38.8370 |
| 0.0001 | 22.73 | 5000 | 0.3310 | 38.8460 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|