metadata
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-hi-ast
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice Hi 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 32.603910945568444
whisper-small-hi-ast
This model is a fine-tuned version of openai/whisper-small on the Common Voice Hi 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4409
- Wer: 32.6039
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0916 | 2.44 | 1000 | 0.2983 | 35.0377 |
| 0.0207 | 4.89 | 2000 | 0.3563 | 33.6959 |
| 0.0012 | 7.33 | 3000 | 0.4194 | 32.5489 |
| 0.0004 | 9.78 | 4000 | 0.4409 | 32.6039 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2