transformers safetensors whisper automatic-speech-recognition ml dataset:thennal/IMaSC license:mit model-index endpoints_compatible region:us
---
license: mit
datasets:
- thennal/IMaSC
language:
- ml
model-index:
- name: Malwhisper-v1-medium
  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: 61.84
      name: WER
    - type: cer
      value: 15.41
      name: CER
library_name: transformers
---

# Malwhisper-v1-medium

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) fine-tuned on [IMASc dataset](https://www.kaggle.com/datasets/thennal/imasc). 

## About Dataset

IMaSC is a Malayalam text and speech corpus made available by ICFOSS for the purpose of developing speech technology for Malayalam, particularly text-to-speech. The corpus contains 34,473 text-audio pairs of Malayalam sentences spoken by 8 speakers, totalling in approximately 50 hours of audio.

## Training

[Script Used for training](https://github.com/kurianbenoy/Keyword_generator_project/blob/main/Whisper_IMASC_final_e2eofficerun.ipynb)

[Training run](https://wandb.ai/hello34/wandb_whisper_e2e/runs/q2xlvbw5)

[Experiment Tracking with Weights and Biases](https://wandb.ai/hello34/wandb_whisper_e2e)

- GPUs used: A100 - 80 GB

- Training Time: 16 hours

- This project was build with A100 80GB GPU provided by [E2E Cloud during their open hack day](https://www.eventbrite.com/e/open-hack-day-tickets-783582435157)

## Evaluation
The fine-tuned model on evaluating in the following dataset:

**In Mozilla CommonVoice 11.0 dataset (Malayalam subset):**

WER - 61.84

CER - 15.41

**In SMC Malayalam Speech Corpus dataset:**

WER - 70.49

CER - 17.0