--- language: - hi license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large-v2 Hindi results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 hi type: mozilla-foundation/common_voice_11_0 config: hi split: test args: hi metrics: - type: wer value: 10.360772823001557 name: Wer - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: hi_in split: test metrics: - type: wer value: 13.52 name: WER --- # Whisper Large-v2 Hindi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 hi dataset. It achieves the following results on the evaluation set: - Loss: 0.2325 - Wer: 10.3608 ## 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 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0067 | 4.18 | 1000 | 0.2325 | 10.3608 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2