Create README.md

README.md

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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