metadata

language:
  - as
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
metrics:
  - wer
model-index:
  - name: kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn-Assamese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: as
          split: test
          args: as
        metrics:
          - name: Wer
            type: wer
            value: 17.560007218913555
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: FLEURS
          type: google/fleurs
        metrics:
          - name: Wer
            type: wer
            value: null

kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn-Assamese

This model is a fine-tuned version of kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set:

Loss: 0.2486
Wer: 17.5600

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
distributed_type: multi-GPU
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

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1273	0.1	100	0.1737	20.8988
0.0811	0.2	200	0.1739	19.0038
0.0638	0.3	300	0.1823	18.4804
0.0404	1.05	400	0.1893	17.1810
0.0316	1.15	500	0.2067	17.0186
0.027	1.25	600	0.2081	17.7405
0.025	2.01	700	0.2213	17.7585
0.0213	2.11	800	0.2237	17.8488
0.0176	2.21	900	0.2390	16.7479
0.0184	2.31	1000	0.2486	17.5600

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2