Instructions to use dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune") model = AutoModelForCTC.from_pretrained("dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune") - Notebooks
- Google Colab
- Kaggle
SWECHA GONTHUKA ASR Fine-tuned
This model is fine-tuned from swechatelangana/swecha-gonthuka-asr on a custom Telugu speech dataset.
Dataset
- 4911 Telugu audio recordings
- 16 kHz WAV files
- Multiple speakers
Base Model
swechatelangana/swecha-gonthuka-asr
Training
- Framework: Hugging Face Transformers
- Model: Wav2Vec2ForCTC
Usage
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
processor = Wav2Vec2Processor.from_pretrained(
"dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune"
)
model = Wav2Vec2ForCTC.from_pretrained(
"dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune"
)
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Model tree for dalavaihemanth/SWECHA_GONTHUKA_ASR_finetune
Finetuned
swechatelangana/swecha-gonthuka-asr