kpriyanshu256's picture
Removed FLUERS WER
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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