|
--- |
|
language: |
|
- ur |
|
license: apache-2.0 |
|
base_model: openai/whisper-tiny |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- mozilla-foundation/common_voice_17_0 |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: Whisper Tiny Urdu |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice 17.0 |
|
type: mozilla-foundation/common_voice_17_0 |
|
config: ur |
|
split: None |
|
args: 'config: ur, split: test' |
|
metrics: |
|
- name: Wer |
|
type: wer |
|
value: 16.033947800693557 |
|
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: 0.1247 |
|
- Wer: 16.0339 |
|
|
|
## Model description |
|
|
|
Whisper Tiny Urdu ASR Model |
|
This Whisper Tiny model has been fine-tuned on the Common Voice 17 dataset, which includes over 55 hours of Urdu speech data. The model was trained twice with different hyperparameters to optimize its performance: |
|
|
|
First Training: The model was trained on the training set and evaluated on the test set for 20 epochs. |
|
Second Training: The model was retrained on the combined train and validation sets, with the test set used for validation, also for 20 epochs. |
|
Despite being the smallest variant in its family, this model achieves state-of-the-art performance for Urdu ASR tasks. It can be used for deployment on small devices, offering an excellent balance between efficiency and accuracy. |
|
|
|
Intended Use: |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
This model is particularly suited for applications on edge devices with limited computational resources. Additionally, it can be converted to a FasterWhisper model using the CTranslate2 library, allowing for even faster inference on devices with lower processing power. |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 4e-05 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 100 |
|
- training_steps: 3000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-------:|:----:|:---------------:|:-------:| |
|
| 0.0057 | 10.1351 | 1500 | 0.1443 | 18.1511 | |
|
| 0.0005 | 20.2703 | 3000 | 0.1247 | 16.0339 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
