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Swecha Gonthuka Finetuned

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

Sourcehuggingface.co/Sujji77/swecha-gonthuka-finetuned/tree/df6a721fd82be4da4f1164f0ab3048490b90504e
TypeOtherModèle de baseswechatelangana/swecha-gonthuka-asrVersionRevision df6a721BClasse

Source du modèle

Type
Other
Taille du fichier
360 MB
Modèle de base
swechatelangana/swecha-gonthuka-asr
Version
vRevision df6a721
Mis à jour
26 oct. 2026
Téléchargements
11
Source
Hugging Face

Description de la source

Telugu ASR Fine-Tuned Model

Overview

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

The model is based on Swecha Gonthuka ASR and has been fine-tuned using a custom Telugu speech dataset consisting of paired audio recordings and transcriptions.

The model converts spoken Telugu audio into Telugu text and is intended for speech transcription, accessibility, and language technology research.

Base Model

  • Model: swechatelangana/swecha-gonthuka-asr
  • Architecture: Wav2Vec2ForCTC
  • Framework: Hugging Face Transformers
  • Language: Telugu (te)

Fine-Tuning Dataset

The model was fine-tuned on a Telugu speech corpus containing:

  • Telugu audio recordings
  • Corresponding Telugu text transcriptions
  • Audio sampled at 16 kHz

Intended Uses

This model can be used for:

  • Telugu Speech-to-Text
  • Automatic Speech Recognition (ASR)
  • Voice-enabled applications
  • Telugu transcription
  • Language technology research

Usage

import librosa
import torch
from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained(
    "Sujji77/swecha-gonthuka-finetuned"
)

model = AutoModelForCTC.from_pretrained(
    "Sujji77/swecha-gonthuka-finetuned"
)

audio, sr = librosa.load("sample.wav", sr=16000)

inputs = processor(
    audio,
    sampling_rate=16000,
    return_tensors="pt"
)

with torch.no_grad():
    logits = model(**inputs).logits

pred_ids = torch.argmax(logits, dim=-1)

transcription = processor.batch_decode(pred_ids)[0]

print(transcription)

Training Details

ItemValue
Base ModelSwecha Gonthuka ASR
ArchitectureWav2Vec2ForCTC
FrameworkTransformers
LanguageTelugu
TaskAutomatic Speech Recognition

Limitations

  • Performance depends on recording quality.
  • Background noise may reduce transcription accuracy.
  • Best suited for Telugu speech.

Author

Srujana Talari

Frontend Developer • AI Intern

Citation

If you use this model in your research or application, please cite this repository. v

Sources

1 source
Hugging FacePrincipalVersion df6a721fd82be4da4f1164f0ab3048490b90504e
huggingface.co/Sujji77/swecha-gonthuka-finetuned/tree/df6a721fd82be4da4f1164f0ab3048490b90504e

Vérifié 26 oct.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 360 MB · SHA-256 f32643712cc2…5373 · Hugging Face

Download file

Extraits de sources

3 extraits
Readme7 aoûthuggingface.co/Sujji77/swecha-gonthuka-finetuned

--- language: - te license: apache-2.0 library_name: transformers pipeline_tag: automatic-speech-recognition base_model: swechatelangana/swecha-gonthuka-asr tags: - telugu - speech-recognition - automatic-speech-recognition - wav2vec2 - fine-tuning - huggingface --- # Telugu ASR Fine-Tuned Model ## Overview This repository contains a fine-tuned Automatic Speech Recognition (ASR) model for the Telugu language. The model is based on **Swecha Gonthuka ASR** and has been fine-tuned using a custom Telugu speech dataset consisting of paired audio recordings and transcriptions. The model converts spoken Telugu audio into Telugu text and is intended for speech transcription, accessibility, and language technology research. --- ## Base Model - **Model:** `swechatelangana/swecha-gonthuka-asr` - **Architecture:** Wav2Vec2ForCTC - **Framework:** Hugging Face Transformers - **Language:** Telugu (`te`) --- ## Fine-Tuning Dataset The model was fine-tuned on a Telugu speech corpus containing: - Telugu audio recordings - Corresponding Telugu text transcriptions - Audio sampled at 16 kHz --- ## Intended Uses This model can be used for: - Telugu Speech-to-Text - Automatic Speech Recognition (ASR) - Voice-enabled applications - Telugu transcription - Language technology research --- ## Usage ```python import librosa import torch from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained( "Sujji77/swecha-gonthuka-finetuned" ) model = AutoModelForCTC.from_pretrained( "Sujji77/swecha-gonthuka-finetuned" ) audio, sr = librosa.load("sample.wav", sr=16000) inputs = processor( audio, sampling_rate=16000, return_tensors="pt" ) with torch.no_grad(): logits = model(**inputs).logits pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids)[0] print(transcription) ``` --- ## Training Details | Item | Value | |------|-------| | Base Model | Swecha Gonthuka ASR | | Architecture | Wav2Vec2ForCTC | | Framework | Transformers | | Language | Telugu | | Task | Automatic Speech Recognition | --- ## Limitations - Performance depends on recording quality. - Background noise may reduce transcription accuracy. - Best suited for Telugu speech. --- ## Author **Srujana Talari** Frontend Developer • AI Intern --- ## Citation If you use this model in your research or application, plea...

Note26 oct.huggingface.co/Sujji77/swecha-gonthuka-finetuned

Source context: 11 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo Sujji77/swecha-gonthuka-finetuned

Note7 aoûthuggingface.co/Sujji77/swecha-gonthuka-finetuned

Source context: 42 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo Sujji77/swecha-gonthuka-finetuned