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Swecha Gonthuka Finetuned

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

Fuentehuggingface.co/Sujji77/swecha-gonthuka-finetuned/tree/df6a721fd82be4da4f1164f0ab3048490b90504e
TipoOtherModelo baseswechatelangana/swecha-gonthuka-asrVersiónRevision df6a721Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
360 MB
Modelo base
swechatelangana/swecha-gonthuka-asr
Versión
vRevision df6a721
Actualizado
26 oct 2026
Descargas
11
Fuente
Hugging Face

Descripción de la fuente

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

Fuentes

1 fuente
Hugging FacePrincipalVersión df6a721fd82be4da4f1164f0ab3048490b90504e
huggingface.co/Sujji77/swecha-gonthuka-finetuned/tree/df6a721fd82be4da4f1164f0ab3048490b90504e

Verificado 26 oct

Artefactos del modelo

1 artefacto

model.safetensors

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

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Extractos de fuentes

3 extractos
Readme7 agohuggingface.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 octhuggingface.co/Sujji77/swecha-gonthuka-finetuned

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

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

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