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---
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 |