metadata

language:
  - hi
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
base_model: openai/whisper-tiny
model-index:
  - name: Whisper tiny Hindi
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: hi
          split: test
          args: hi
        metrics:
          - type: wer
            value: 41.54533990599564
            name: Wer
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: FLEURS
          type: google/fleurs
          config: hi_in
          split: test
          args: hi
        metrics:
          - type: wer
            value: 41.63
            name: Wer

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