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Datasets:
Noothi
/
telugu-tech-indicf5-custom-voice

Tasks:
Text-to-Speech
Automatic Speech Recognition
Modalities:
Audio
Languages:
Telugu
Size:
100K<n<1M
Tags:
audio
speech
tts
asr
telugu
custom-voice
License:
Dataset card Files
xet
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The dataset could not be loaded because the splits use different data file formats, which is not supported. Read more about the splits configuration. Click for more details.
Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('csv', {}), NamedSplit('validation'): ('csv', {}), NamedSplit('test'): ('audiofolder', {})}
Error code:   FileFormatMismatchBetweenSplitsError

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🎙️ Telugu Tech IndicF5 Custom Voice Dataset

A 100% verified, clean, single-speaker Telugu Speech & Voice dataset specially formatted and phonetically cleaned for training and fine-tuning ai4bharat/IndicF5 and neural Text-to-Speech (TTS) models.

All English technical terms, numbers, acronyms, and ASR mishearings have been converted into native Telugu phonetic script, cleaned of noise/brackets, and validated for optimal IndicF5 fine-tuning performance.

📊 Dataset Statistics

  • Total Clips: 455 audio files (.wav)
  • Train Split: 437 clips (~70 minutes)
  • Validation Split: 18 clips (~3 minutes)
  • Total Audio Duration: 1 Hour 12 Minutes 48.5 Seconds (4,368.5 seconds)
  • Total Dataset Size: ~1.20 GB
  • Language: Telugu (te) with technical terms written in Telugu phonetic script
  • Audio Quality: Clean isolated vocals (background music removed using Demucs htdemucs_ft)
  • Transcriptions: 100% verified Telugu script phonetics, free of foreign artifacts & STT hallucinations.

📁 Source Videos & Breakdown

Video ID Audio Clips Duration Topics Covered
-UMAP97L5q4 113 clips 19m 58.2s Technical Architecture, CPU/IO Heavy Systems
RFAIqyP6mZc 114 clips 19m 50.5s MySQL, MyRocks Engine, Storage Optimization
kb2B9_A3TxE 88 clips 12m 40.8s Microservices, Circuit Breaker, Exponential Jitter
5rTRRIwfKNo 140 clips 20m 18.8s System Design, Entity Layering, Uber Flow

💻 Usage with Hugging Face datasets 

from datasets import load_dataset

# Load dataset directly from Hugging Face Hub
dataset = load_dataset("Noothi/telugu-tech-indicf5-custom-voice")

# Inspect sample entry
print(dataset['train'][0])

🛠️ Dataset Files & Structure

  • metadata_clean.csv: Full cleaned metadata containing file_name,text
  • train.csv: Training split for IndicF5 (437 rows)
  • val.csv: Validation split for IndicF5 (18 rows)
  • wavs/: Isolated 22050Hz mono WAV audio files
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Total file size:
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Models trained or fine-tuned on Noothi/telugu-tech-indicf5-custom-voice