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Uzbek Speech Recognition (ASR) Dataset

This is an Uzbek language speech recognition dataset created for training ASR models.

Dataset Structure

  • train: Training data (80% of conversations)
  • validation: Validation data (10% of conversations)
  • test: Test data (10% of conversations)

Audio Format

  • Sample Rate: 16 kHz
  • Channels: Mono
  • Bit Depth: 16-bit PCM
  • Format: WAV

Dataset Fields

  • audio: Audio file (Audio feature, directly playable)
  • text: Normalized text transcription in Uzbek (Latin script)
  • duration: Duration of audio in seconds
  • language: Language code (uzn_Latn for Uzbek Latin)
  • id: Unique identifier for the segment
  • audio_id: Identifier for the conversation/recording
  • segment_index: Index of the segment within the conversation

Dataset Statistics

  • Total conversations: 8
  • Total segments: 48
  • Total duration: 0.08 hours
  • Train: 38 segments (0.06 hours)
  • Validation: 0 segments (0.0 hours)
  • Test: 10 segments (0.02 hours)

Important Notes

  • Conversations are NOT split across train/validation/test sets
  • Each conversation (audio_id) belongs entirely to one split
  • This ensures proper model evaluation without data leakage
  • Dataset is updated daily with new recordings (append-only)
  • Once uploaded, segments are marked in database to prevent duplicates

Usage

Load with datasets library:

from datasets import load_dataset

# Load entire dataset
dataset = load_dataset('admin-euphoria/ASR_Dataset')

# Load specific split
train_dataset = load_dataset('admin-euphoria/ASR_Dataset', split='train')

# Access audio and text
for example in train_dataset:
    audio_array = example['audio']['array']
    sampling_rate = example['audio']['sampling_rate']
    text = example['text']
    print(f"Text: {text}, Duration: {example['duration']}s")

Update Strategy

  • Daily building: First script collects data from database daily
  • Incremental upload: Second script uploads only new segments to HF
  • Database tracking: ProcessedDataset table tracks uploaded segment_ids
  • No duplicates: Segment_id uniqueness prevents re-uploading
  • Append-only: Data is only added, never removed or modified

Dataset Information

Contact

[kkadyr039@gmail.com]