metadata
language:
- hi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper tiny Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 41.54533990599564
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: FLEURS
type: google/fleurs
config: hi_in
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 41.63
Whisper tiny Hindi
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5538
- Wer: 41.5453
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
- 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: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7718 | 0.73 | 100 | 0.8130 | 55.6890 |
| 0.5169 | 1.47 | 200 | 0.6515 | 48.2517 |
| 0.3986 | 2.21 | 300 | 0.6001 | 44.9931 |
| 0.3824 | 2.94 | 400 | 0.5720 | 43.5171 |
| 0.3328 | 3.67 | 500 | 0.5632 | 42.5112 |
| 0.2919 | 4.41 | 600 | 0.5594 | 42.7863 |
| 0.2654 | 5.15 | 700 | 0.5552 | 41.6428 |
| 0.2618 | 5.88 | 800 | 0.5530 | 41.8893 |
| 0.2442 | 6.62 | 900 | 0.5539 | 41.5740 |
| 0.238 | 7.35 | 1000 | 0.5538 | 41.5453 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2