metadata

language:
  - ml
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  - thennal/IMaSC
  - thennal/ulca_ml
  - thennal/msc
  - thennal/indic_tts_ml
metrics:
  - wer
model-index:
  - name: Whisper Medium Malayalam - Thennal D K
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: ml
          split: test
          args: ml
        metrics:
          - type: wer
            value: 38.62068965517241
            name: WER
          - type: cer
            value: 7.325639739086803
            name: CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: FLEURS
          type: google/fleurs
          config: ml_in
          split: test
          args: ml_in
        metrics:
          - type: wer
            value: 27.850740045862
            name: WER
          - type: cer
            value: 8.821352343856674
            name: CER

Whisper Medium Malayalam

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

WER: 38.6207
CER: 7.3256

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: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 8000
mixed_precision_training: Native AMP

Framework versions

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