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---
language:
- hi
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 hi
type: mozilla-foundation/common_voice_16_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 28.648953267516852
---
<!-- 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 Base Hindi
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co./openai/whisper-base) on the mozilla-foundation/common_voice_16_0 hi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4679
- Wer: 28.6490
## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6425 | 6.01 | 500 | 0.7025 | 41.4477 |
| 0.3973 | 13.0 | 1000 | 0.5367 | 33.9692 |
| 0.3125 | 19.01 | 1500 | 0.4927 | 31.4458 |
| 0.2848 | 26.0 | 2000 | 0.4739 | 30.1037 |
| 0.2201 | 32.01 | 2500 | 0.4675 | 29.4859 |
| 0.2257 | 39.01 | 3000 | 0.4637 | 28.9933 |
| 0.1837 | 46.0 | 3500 | 0.4657 | 28.9140 |
| 0.1897 | 52.01 | 4000 | 0.4658 | 28.7450 |
| 0.1764 | 59.0 | 4500 | 0.4676 | 28.7178 |
| 0.1681 | 65.01 | 5000 | 0.4679 | 28.6490 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
|