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---
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
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