README.md

metadata

license: cc
task_categories:
  - automatic-speech-recognition
  - audio-to-audio
  - audio-classification
language:
  - en
pretty_name: Phonemized-VCTK (speech + features)
size_categories:
  - 10K<n<100K

Phonemized-VCTK (speech + features)

Phonemized-VCTK is a light-repack of the VCTK corpus that bundles—per utterance—

• the raw audio (wav/)
• the plain transcript (txt/)
• the IPA phoneme string (phonemized/)
• frame-level pitch-aligned segments (segments/)
• sentence-level context embeddings (context_embeddings/)
• speaker-level embeddings (speaker_embeddings/)

The goal is to provide a turn-key dataset for
forced alignment, prosody modelling, TTS, and speaker adaptation experiments without having to regenerate these side-products every time.

---

Folder layout

Folder	Contents	Shape / format
wav/<spk>/	48 kHz 16‑bit mono .wav files	p225_001.wav, …
txt/<spk>/	original plain‑text transcript	p225_001.txt, …
phonemized/<spk>/	whitespace‑separated IPA symbols, #h = word boundary	p225_001.txt, …
segments/<spk>/	JSON with per‑phoneme timing & mean pitch	p225_001.json, …
context_embeddings/<spk>/	NumPy float32 .npy, sentence embedding of the utterance	p225_001.npy, …
speaker_embeddings/	NumPy float32 .npy, one vector per speaker, generated from NVIDIA TitaNet-Large model	p225.npy, …

Example segments entry

{
  "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

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

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

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:

@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}
}

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