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README.md

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+---
+language:
+- te
+license: apache-2.0
+tags:
+- whisper-event
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: Whisper Telugu Tiny - Vasista Sai Lodagala
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: google/fleurs
+      type: google/fleurs
+      config: te_in
+      split: test
+    metrics:
+    - type: wer
+      value: 20.0
+      name: WER
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Whisper Telugu Tiny
+
+This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Telugu data available from multiple publicly available ASR corpuses.
+It has been fine-tuned as a part of the Whisper fine-tuning sprint.
+
+## Training and evaluation data at Speech Lab, IITM
+
+Training Data: CSTD IIIT-H ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Microsoft Research Telugu Corpus (Train+Dev), Babel ASR Corpus, Google/Fleurs (Train+Dev) set.
+Evaluation Data: Babel Test, Microsoft Research Telugu Corpus Test, Google/Fleurs Test set, OpenSLR.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 88
+- eval_batch_size: 88
+- seed: 22
+- optimizer: adamw_bnb_8bit
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 15000
+- training_steps: 14652 (terminated upon convergence. Initially set to 85952 steps)
+- mixed_precision_training: True
+
+## Acknowledgement
+This work was done at Speech Lab, IITM. The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India.

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