Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Telugu
whisper
Eval Results (legacy)
Instructions to use Rahul3215/whisper-small-telugu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahul3215/whisper-small-telugu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rahul3215/whisper-small-telugu")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Rahul3215/whisper-small-telugu") model = AutoModelForSpeechSeq2Seq.from_pretrained("Rahul3215/whisper-small-telugu") - Notebooks
- Google Colab
- Kaggle
Finetuned openai/whisper-small on 812 Telugu training audio samples from /content/dataset.
This model was created from the Mozilla.ai Blueprint: speech-to-text-finetune.
Evaluation results on 203 audio samples of Telugu:
Baseline model (before finetuning) on Telugu
- Word Error Rate (Normalized): 173.255
- Word Error Rate (Orthographic): 139.928
- Character Error Rate (Normalized): 142.764
- Character Error Rate (Orthographic): 161.132
- Loss: 2.305
Finetuned model (after finetuning) on Telugu
- Word Error Rate (Normalized): 122.977
- Word Error Rate (Orthographic): 134.42
- Character Error Rate (Normalized): 83.015
- Character Error Rate (Orthographic): 104.111
- Loss: 1.345
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Model tree for Rahul3215/whisper-small-telugu
Base model
openai/whisper-smallEvaluation results
- wer on Common Voice (Telugu)self-reported122.977