metadata
language:
- hi
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Custom Hi - Nikhil Bhargava
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 hi
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 0.24870904935240837
Whisper Medium Custom Hi - Nikhil Bhargava
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0 hi dataset. It achieves the following results on the evaluation set:
- Loss: 0.3972
- Wer: 0.2487
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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0282 | 4.89 | 1000 | 0.2700 | 0.2647 |
| 0.0025 | 9.78 | 2000 | 0.3434 | 0.2554 |
| 0.0001 | 14.67 | 3000 | 0.3640 | 0.2471 |
| 0.0 | 19.56 | 4000 | 0.3902 | 0.2494 |
| 0.0 | 24.45 | 5000 | 0.3972 | 0.2487 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3