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Baseline Orpheus TTS Combined Dataset

Overview

This dataset contains synthetic speech generated using the unmodified Orpheus-3B model across all pathological and healthy speakers for baseline comparison.

Statistics

  • Total Samples: 800
  • Total Duration: 1534.04 seconds (0.4 hours)
  • Average Duration: 1.92 seconds
  • Number of Speakers: 8
  • Sample Rate: 24,000 Hz

Corpus Breakdown

  • LibriSpeech_Healthy: 60 samples, 554.4s total, 9.24s avg
  • UA-Speech_Dysarthric: 400 samples, 482.2s total, 1.21s avg
  • TORGO_Healthy: 200 samples, 289.8s total, 1.45s avg
  • TORGO_Dysarthric: 140 samples, 207.6s total, 1.48s avg

Speaker Coverage

  • TORGO Dysarthric: F04, M02
  • TORGO Healthy: FC02, MC01
  • UA-Speech: F02, M04
  • LibriSpeech: 211, 4014

Model Information

  • Base Model: unsloth/orpheus-3b-0.1-ft (unmodified)
  • Purpose: Baseline comparison for pathological speech synthesis evaluation
  • Generation Date: 2025-09-13T14:36:30.220298

Usage

from datasets import Dataset

# Load combined dataset
dataset = Dataset.from_parquet("baseline_orpheus_combined.parquet")

# Filter by corpus
torgo_samples = dataset.filter(lambda x: x['corpus'] == 'TORGO')
dysarthric_samples = dataset.filter(lambda x: x['condition'] == 'Dysarthric')

# Access audio and text
for sample in dataset.select(range(3)):
    print(f"Speaker {sample['speaker_id']}: {sample['text']}")
    audio_array = sample['audio']['array']
    print(f"Audio shape: {audio_array.shape}")
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