---
language:
- ml
license: apache-2.0
tags:
- whisper-event
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:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: ml
      split: test
      args: ml
    metrics:
    - name: Wer
      type: wer
      value: 42.98850574712644
    - name: Cer
      type: cer
      value: 10.390585878818229
---


# Whisper Medium Malayalam - Thennal D K

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on a combined dataset sourced from IMaSC, 
SMC, Indic TTS, FLEURS (train set), Common Voice 11 (train + other set), OpenSLR, and ULCA.
It achieves the following results on the evaluation set (Common Voice 11 test split):
- Loss: 0.0730
- WER: 42.9886
- CER: 10.3906

## 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: 4000
- 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