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Telugu Whisper Finetuned

This repository contains a fine-tuned Automatic Speech Recognition (ASR) model for the Telugu language.

ArtOtherBasismodellswechatelangana/swecha-gonthuka-asrVersionRevision 64950e2BBewertung

Modellquelle

Art
Other
Dateigröße
360 MB
Basismodell
swechatelangana/swecha-gonthuka-asr
Version
vRevision 64950e2
Aktualisiert
26. Okt. 2026
Downloads
7
Quelle
Hugging Face

Quellenbeschreibung

Telugu ASR Fine-Tuned Model

Overview

This repository contains a fine-tuned Automatic Speech Recognition (ASR) model for the Telugu language.

The model is built upon Swecha Gonthuka ASR and further fine-tuned using a custom Telugu speech dataset to improve transcription quality on domain-specific Telugu speech.

The model converts spoken Telugu audio into Telugu Unicode text and can be used for:

  • Automatic Speech Recognition (ASR)
  • Speech-to-Text applications
  • Voice transcription
  • Voice assistants
  • Educational applications
  • Telugu speech processing research

Model Details

PropertyValue
Model TypeWav2Vec2ForCTC
LanguageTelugu
FrameworkHugging Face Transformers
Base Modelswechatelangana/swecha-gonthuka-asr
TaskAutomatic Speech Recognition
Fine-tuned ByDinesh

Base Model

This model has been fine-tuned from:

swechatelangana/swecha-gonthuka-asr

Original architecture:

Harveenchadha/wav2vec2-pretrained-clsril-23-10k
            ↓
swechatelangana/swecha-gonthuka-asr
            ↓
dinesh480/telugu-asr-finetuned

Fine-Tuning Dataset

The model was fine-tuned on a custom Telugu speech dataset.

Dataset Characteristics:

  • Language: Telugu
  • Audio Format: WAV
  • Sampling Rate: 16 kHz
  • Mono Audio
  • UTF-8 Telugu Transcriptions

Training Configuration

HyperparameterValue
FrameworkTransformers
ArchitectureWav2Vec2ForCTC
OptimizerAdamW
Learning Rate1e-4
Epochs10
Batch Size8
Mixed PrecisionFP16
LossCTC Loss

Usage

Install dependencies

pip install transformers torchaudio librosa

Load the model

from transformers import AutoProcessor
from transformers import AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "dinesh480/telugu-asr-finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "dinesh480/telugu-asr-finetuned"
)

Or use the pipeline

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="dinesh480/telugu-asr-finetuned"
)

result = pipe("audio.wav")

print(result["text"])

Example

Input Audio

audio.wav

Predicted Output

ఈ రోజు వాతావరణం చాలా బాగుంది.

Performance

The model has been fine-tuned to improve Telugu speech transcription quality over the base model on the custom training dataset.

Evaluation metrics may include:

  • Word Error Rate (WER)
  • Character Error Rate (CER)

Applications

This model can be used for:

  • Telugu Speech Recognition
  • Audio Transcription
  • Educational Projects
  • Research
  • Voice Assistants
  • Speech Analytics
  • Subtitle Generation

Limitations

Performance may decrease for:

  • Heavy background noise
  • Multiple speakers
  • Very low-quality recordings
  • Strong regional accents not present in the training data

Citation

If you use this model in your research, please cite:

@misc{telugu_asr_finetuned,
  title={Telugu ASR Fine-Tuned Model},
  author={K. Dinesh},
  year={2026},
  publisher={Hugging Face}
}

Acknowledgements

This project is based on:

  • Swecha Telangana
  • Hugging Face Transformers
  • PyTorch
  • Wav2Vec2
  • Hugging Face Hub

Special thanks to the developers of Swecha Gonthuka ASR for releasing the original Telugu ASR model.


License

This model follows the Apache-2.0 License inherited from the base model.

Quellen

1 Quelle
Hugging FacePrimärVersion 64950e2a592f072e34c6c1a8c048b345c90f4c65
huggingface.co/dinesh480/telugu-whisper-finetuned/tree/64950e2a592f072e34c6c1a8c048b345c90f4c65

Verifiziert 26. Okt.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 360 MB · SHA-256 e9df26af8b99…12b6 · Hugging Face

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Quellenauszüge

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--- language: - te license: apache-2.0 pipeline_tag: automatic-speech-recognition base_model: swechatelangana/swecha-gonthuka-asr library_name: transformers tags: - automatic-speech-recognition - speech-recognition - wav2vec2 - telugu - asr - speech-to-text - fine-tuning - huggingface metrics: - wer --- # Telugu ASR Fine-Tuned Model ## Overview This repository contains a fine-tuned Automatic Speech Recognition (ASR) model for the Telugu language. The model is built upon **Swecha Gonthuka ASR** and further fine-tuned using a custom Telugu speech dataset to improve transcription quality on domain-specific Telugu speech. The model converts spoken Telugu audio into Telugu Unicode text and can be used for: - Automatic Speech Recognition (ASR) - Speech-to-Text applications - Voice transcription - Voice assistants - Educational applications - Telugu speech processing research --- # Model Details | Property | Value | |----------|-------| | Model Type | Wav2Vec2ForCTC | | Language | Telugu | | Framework | Hugging Face Transformers | | Base Model | swechatelangana/swecha-gonthuka-asr | | Task | Automatic Speech Recognition | | Fine-tuned By | Dinesh | --- # Base Model This model has been fine-tuned from: **swechatelangana/swecha-gonthuka-asr** Original architecture: ``` Harveenchadha/wav2vec2-pretrained-clsril-23-10k ↓ swechatelangana/swecha-gonthuka-asr ↓ dinesh480/telugu-asr-finetuned ``` --- # Fine-Tuning Dataset The model was fine-tuned on a custom Telugu speech dataset. Dataset Characteristics: - Language: Telugu - Audio Format: WAV - Sampling Rate: 16 kHz - Mono Audio - UTF-8 Telugu Transcriptions --- # Training Configuration | Hyperparameter | Value | |---------------|-------| | Framework | Transformers | | Architecture | Wav2Vec2ForCTC | | Optimizer | AdamW | | Learning Rate | 1e-4 | | Epochs | 10 | | Batch Size | 8 | | Mixed Precision | FP16 | | Loss | CTC Loss | --- # Usage Install dependencies ```bash pip install transformers torchaudio librosa ``` Load the model ```python from transformers import AutoProcessor from transformers import AutoModelForCTC processor = AutoProcessor.from_pretrained( "dinesh480/telugu-asr-finetuned" ) model = AutoModelForCTC.from_pretrained( "dinesh480/telugu-asr-finetuned" ) ``` Or use the pipeline ```python from transformers import pipeline pipe = pipeline( "automatic-speech-recognit...

Source context: 7 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo dinesh480/telugu-whisper-finetuned

Source context: 56 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo dinesh480/telugu-whisper-finetuned