Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Telugu
whisper
Eval Results (legacy)
Instructions to use yeshu-09/whisper-small-te with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeshu-09/whisper-small-te with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yeshu-09/whisper-small-te")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("yeshu-09/whisper-small-te") model = AutoModelForSpeechSeq2Seq.from_pretrained("yeshu-09/whisper-small-te") - Notebooks
- Google Colab
- Kaggle
Finetuned openai/whisper-small on 123 telugu training audio samples from AkCodes23/sarvam-tts-in-te-en.
This model was created from the Mozilla.ai Blueprint: speech-to-text-finetune.
Evaluation results on 8 audio samples of telugu:
Baseline model (before finetuning) on telugu
- Word Error Rate (Normalized): 109.73
- Word Error Rate (Orthographic): 124.413
- Character Error Rate (Normalized): 72.428
- Character Error Rate (Orthographic): 98.127
- Loss: 1.815
Finetuned model (after finetuning) on telugu
- Word Error Rate (Normalized): 66.126
- Word Error Rate (Orthographic): 90.141
- Character Error Rate (Normalized): 51.045
- Character Error Rate (Orthographic): 59.465
- Loss: 0.597
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Model tree for yeshu-09/whisper-small-te
Base model
openai/whisper-smallEvaluation results
- wer on Common Voice (telugu)self-reported66.126