Datasets:

Thanarit
/

Thai-Voice-Test-Viewer-Fix

dataset_info:
  features:
    - name: ID
      dtype: string
    - name: speaker_id
      dtype: string
    - name: Language
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: transcript
      dtype: string
    - name: length
      dtype: float32
    - name: dataset_name
      dtype: string
    - name: confidence_score
      dtype: float64
  splits:
    - name: train
      num_examples: 120
  download_size: 0
  dataset_size: 0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*.parquet

Thanarit/Thai-Voice

Combined Thai audio dataset from multiple sources

Dataset Details

Total samples: 120
Total duration: 0.13 hours
Language: Thai (th)
Audio format: 16kHz mono WAV
Volume normalization: -20dB

Sources

Processed 1 datasets in streaming mode

Source Datasets

GigaSpeech2: Large-scale multilingual speech corpus

Usage

from datasets import load_dataset

# Load with streaming to avoid downloading everything
dataset = load_dataset("Thanarit/Thai-Voice-Test-Viewer-Fix", streaming=True)

# Iterate through samples
for sample in dataset['train']:
    print(sample['ID'], sample['transcript'][:50])
    # Process audio: sample['audio']
    break

Schema

ID: Unique identifier (S1, S2, S3, ...)
speaker_id: Speaker identifier (SPK_00001, SPK_00002, ...)
Language: Language code (always "th" for Thai)
audio: Audio data with 16kHz sampling rate
transcript: Text transcript of the audio
length: Duration in seconds
dataset_name: Source dataset name (e.g., "GigaSpeech2", "ProcessedVoiceTH", "MozillaCommonVoice")
confidence_score: Confidence score of the transcript (0.0-1.0)
- 1.0: Original transcript from source dataset
- <1.0: STT-generated transcript
- 0.0: Fallback transcript (e.g., [NO_TRANSCRIPT])

Processing Details

This dataset was created using streaming processing to handle large-scale data without requiring full downloads. Audio has been standardized to 16kHz mono with -20dB volume normalization.