Example segments entry

```json { "0": ["h#", {"start_sec":0.0,"end_sec":0.10,"duration_sec":0.10,"mean_pitch":0.0}], "1": ["p", {"start_sec":0.10,"end_sec":0.18,"duration_sec":0.08,"mean_pitch":0.0}], "2": ["l", {"start_sec":0.18,"end_sec":1.32,"duration_sec":1.14,"mean_pitch":1377.16}] } ```

--- ## Quick start ```python from datasets import load_dataset ds_train = load_dataset("srinathnr/TTS_DATASET", split="train", trust_remote_code=True, streaming=True) ds_val = load_dataset("srinathnr/TTS_DATASET", split="validation", trust_remote_code=True, streaming=True) ds_test = load_dataset("srinathnr/TTS_DATASET", split="test", trust_remote_code=True, streaming=True) ``` --- ## Custom Data Load ```python from pathlib import Path from datasets import Audio from torch.utils.data import Dataset class CustomDataset(Dataset): def __init__(self, dataset_folder): self.dataset_folder = dataset_folder self.audio_files = sorted( [path for path in (Path(dataset_folder) / 'wav').rglob('*.wav') if not path.name.startswith('._')] ) self.phoneme_files = sorted( [path for path in (Path(dataset_folder) / 'phonemized').rglob('*.txt') if not path.name.startswith('._')] ) # Get the base file names (without extensions) for matching audio_basenames = {path.stem for path in self.audio_files} phoneme_basenames = {path.stem for path in self.phoneme_files} # Intersection of all file sets (excluding speaker embeddings) common_basenames = audio_basenames & phoneme_basenames # Filter files to only include common base names self.audio_files = [path for path in self.audio_files if path.stem in common_basenames] self.phoneme_files = [path for path in self.phoneme_files if path.stem in common_basenames] self.audio_feature = Audio(sampling_rate=16000) def __len__(self): return len(self.audio_files) def __getitem__(self, idx): audio_path = str(self.audio_files[idx]) phoneme_path = str(self.phoneme_files[idx]) align_audio = self.audio_feature.decode_example({"path": str(audio_path), "bytes": None}) with open(phoneme_path, 'r') as f: phoneme = f.read() if phoneme is not None: phoneme = phoneme.split() else: phoneme = [] return { 'phoneme': phoneme, 'align_audio': align_audio } ``` --- ## Explore ```python from pathlib import Path import json, soundfile as sf import numpy as np root = Path("Phonemized-VCTK") wav, sr = sf.read(root/"wav/p225/p225_001.wav") text = (root/"txt/p225/p225_001.txt").read_text().strip() ipa = (root/"phonemized/p225/p225_001.txt").read_text().strip() segs = json.loads((root/"segments/p225/p225_001.json").read_text()) ctx = np.load(root/"context_embeddings/p225/p225_001.npy") print(text) print(ipa.split()) # IPA tokens print(ctx.shape) # (384,) ``` --- ## Known limitations * The phone set is plain IPA—no stress or intonation markers. * English only (≈109 speakers, various accents). * Pitch = 0 on unvoiced phones; interpolate if needed. * Embedding models were chosen for convenience—swap as you like. --- ## Citation Please cite **both** VCTK and this derivative if you use the corpus: ```bibtex @misc{yours2025phonvctk, title = {Phonemized-VCTK: An enriched version of VCTK with IPA, alignments and embeddings}, author = {Your Name}, year = {2025}, howpublished = {\url{https://huggingface.co/datasets/your-handle/phonemized-vctk}} } @inproceedings{yamagishi2019cstr, title={The CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit}, author={Yamagishi, Junichi et al.}, booktitle={Proc. LREC}, year={2019} } ``` ---