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library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- automatic-speech-recognition
- whisper
- urdu
datasets:
- mozilla-foundation/common_voice_17_0
- HowMannyMore/urdu-audiodataset
metrics:
- wer
- cer
- bleu
- chrf
model-index:
- name: whisper-tiny-urdu
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 17.0 (Urdu)
type: mozilla-foundation/common_voice_17_0
config: ur
split: test
args: ur
metrics:
- name: WER on Common Voice 17.0
type: wer
value: 46.908
- name: CER on Common Voice 17.0
type: cer
value: 18.543
- name: BLEU on Common Voice 17.0
type: bleu
value: 32.631
- name: ChrF on Common Voice 17.0
type: chrf
value: 63.988
language:
- ur
pipeline_tag: automatic-speech-recognition
---
<!-- 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-tiny-urdu
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7225
- Wer: 47.8529
## Quick Usage
```python
from transformers import pipeline
transcriber = pipeline(
"automatic-speech-recognition",
model="kingabzpro/whisper-tiny-urdu"
)
transcriber.model.generation_config.forced_decoder_ids = None
transcriber.model.generation_config.language = "ur"
transcription = transcriber("audio2.mp3")
print(transcription)
```
```sh
{'text': 'دیکھیے پانی کب تک بہتا اور مچھلی کب تک تیرتی ہے'}
```
## Evaluation
| **Dataset** | **WER (%)** | **CER (%)** | **BLEU** | **ChrF** |
| ------------------------------ | ----------- | ----------- | -------- | -------- |
| Common Voice 17.0 (Urdu) | 46.908 | 18.543 | 32.631 | 63.988 |
| HowMannyMore/urdu-audiodataset | 51.405 | 21.830 | 31.475 | 64.204 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6808 | 1.6949 | 500 | 0.7403 | 52.6699 |
| 0.3948 | 3.3898 | 1000 | 0.6850 | 47.1247 |
| 0.2873 | 5.0847 | 1500 | 0.6994 | 48.1516 |
| 0.2024 | 6.7797 | 2000 | 0.7169 | 46.7326 |
| 0.183 | 8.4746 | 2500 | 0.7225 | 47.8529 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|