Datasets:

Nanboy
/

RVCBench

The manifest.json file provides a structured mapping of all audio files in the Multispeaker_libri dataset, following the same format as the Bilingual_uedin dataset (MF1.json).

Key Features

800 total entries - Each mixture file at a different SNR level gets its own entry
100 unique groundtruth files - Clean reference audios (10 per target speaker)
8 mixtures per groundtruth - Each groundtruth is shared by:
- 4 SNR levels: -5dB, 0dB, +5dB, +10dB
- 2 interferers: 121 (female), 672 (male)

Structure

Each JSON entry contains:

{
  "ori_pth": "target_61/interferer_121/snr_-5dB/61-70970-0003_mixture.wav",
  "ori_spk": "61",
  "ori_lang": "EN",
  "ori_text": "IF FOR A WHIM YOU BEGGAR YOURSELF I CANNOT STAY YOU",
  "ori_phonemes": "",
  "ori_tone": "",
  "ori_word2ph": "",
  "gt_pth": "target_61/groundtruth/61-70970-0037.wav",
  "gt_spk": "61",
  "gt_lang": "EN",
  "gt_text": "INDEED HE IS INFORMED ON THESE POINTS...",
  "gt_phonemes": "",
  "gt_tone": "",
  "gt_word2ph": "",
  "snr": "-5dB",
  "interferer_id": "121"
}

Fields Explanation

Original (Mixture) Audio:

ori_pth: Path to the mixture file (target + interferer at specific SNR)
ori_spk: Target speaker ID
ori_lang: Language (always "EN" for LibriSpeech)
ori_text: Transcription of the target speaker's utterance
ori_phonemes, ori_tone, ori_word2ph: Empty (can be filled with phonemizer)

Groundtruth (Clean Reference) Audio:

gt_pth: Path to clean groundtruth file (no interference)
gt_spk: Same as target speaker ID
gt_lang: Language (always "EN")
gt_text: Transcription of the groundtruth utterance
gt_phonemes, gt_tone, gt_word2ph: Empty (can be filled with phonemizer)

Additional Metadata:

snr: Signal-to-Noise Ratio level (+10dB, +5dB, +0dB, -5dB)
interferer_id: ID of interfering speaker (121 or 672)

Use Cases

1. Voice Cloning with Noisy References

Use mixture files (ori_pth) as enrollment audio with controlled interference:

# Load mixture at different SNR levels
mixture_5db = load_audio(entry['ori_pth'])  # SNR = +5dB
mixture_0db = load_audio(entry['ori_pth'])  # SNR = 0dB

# Use same clean groundtruth for comparison
clean_ref = load_audio(entry['gt_pth'])

2. Studying SNR Impact

Compare voice cloning quality across SNR levels using the same groundtruth:

# Get all SNR versions of same utterance (share same gt_pth)
entries_for_utterance = [e for e in manifest if e['gt_pth'] == target_gt]
# entries will have -5dB, 0dB, +5dB, +10dB versions

3. Multi-Speaker Interference Analysis

Test how different interferers affect the same target speaker:

# Compare female interferer (121) vs male interferer (672)
female_interferer = [e for e in manifest if e['interferer_id'] == '121']
male_interferer = [e for e in manifest if e['interferer_id'] == '672']

Dataset Statistics

Target Speakers: 10 (5 male, 5 female)
- Male: 61, 908, 2300, 2830, 7729
- Female: 237, 1221, 1284, 4970, 6829
Interferer Speakers: 2
- Female: 121
- Male: 672
SNR Levels: 4 (-5dB, 0dB, +5dB, +10dB)
Audio Files:
- 800 mixture files
- 800 target files (clean segments used in mixtures)
- 100 groundtruth files (separate clean references)

Example: Multiple SNRs → Same Groundtruth

Groundtruth: target_61/groundtruth/61-70970-0037.wav
├── Mixture 1: target_61/interferer_121/snr_-5dB/61-70970-0003_mixture.wav
├── Mixture 2: target_61/interferer_121/snr_+0dB/61-70970-0003_mixture.wav
├── Mixture 3: target_61/interferer_121/snr_+5dB/61-70970-0003_mixture.wav
├── Mixture 4: target_61/interferer_121/snr_+10dB/61-70970-0003_mixture.wav
├── Mixture 5: target_61/interferer_672/snr_-5dB/61-70970-0003_mixture.wav
├── Mixture 6: target_61/interferer_672/snr_+0dB/61-70970-0003_mixture.wav
├── Mixture 7: target_61/interferer_672/snr_+5dB/61-70970-0003_mixture.wav
└── Mixture 8: target_61/interferer_672/snr_+10dB/61-70970-0003_mixture.wav

Loading the Manifest

import json

with open('manifest.json', 'r') as f:
    manifest = json.load(f)

# Access entries
for entry in manifest:
    mixture_path = entry['ori_pth']
    groundtruth_path = entry['gt_pth']
    snr_level = entry['snr']
    interferer = entry['interferer_id']
    
    # Your processing code here

Generation Script

The manifest was generated using scripts/generate_multispeaker_manifest.py.

To regenerate:

python scripts/generate_multispeaker_manifest.py

Notes

Phoneme fields are currently empty. You can populate them using tools like phonemizer.
All audio files are WAV format, 16kHz sample rate, mono.
Transcriptions are extracted from LibriSpeech's .trans.txt files.
Groundtruth files are different utterances than the mixture utterances (separate clean references for testing).

Version

Generated: November 20, 2025
Dataset: Multispeaker_libri v1.0
Based on: LibriSpeech test-clean subset