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README.md

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----
-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`, … |
-
-<details>
-<summary>Example <code>segments</code> entry</summary>
-
-```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}]
-}
-```
-</details>
-
----
-
-
----
-
-## Quick start
-
-```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}
-}
-```
-
----