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Datasets:
srinathnr
/
TTS_DATASET

Tasks:
Automatic Speech Recognition
Audio-to-Audio
Audio Classification
Modalities:
Text
Languages:
English
Size:
10K<n<100K
License:
Dataset card Data Studio Files
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Srinath N Ramalingam commited on
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  3. README.md +112 -0
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+ ---
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+ license: cc
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+ task_categories:
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+ - automatic-speech-recognition
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+ - audio-to-audio
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+ - audio-classification
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+ language:
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+ - en
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+ pretty_name: Phonemized-VCTK (speech + features)
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+
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+ # Phonemized-VCTK (speech + features)
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+
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+ **Phonemized-VCTK** is a light-repack of the VCTK corpus that bundles—per utterance—
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+
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+ * the raw audio (`wav/`)
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+ * the plain transcript (`txt/`)
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+ * the IPA phoneme string (`phonemized/`)
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+ * frame-level pitch-aligned segments (`segments/`)
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+ * sentence-level context embeddings (`context_embeddings/`)
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+ * speaker-level embeddings (`speaker_embeddings/`)
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+
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+ The goal is to provide a *turn-key* dataset for
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+ *forced alignment*, *prosody modelling*, *TTS*, and *speaker adaptation* experiments without having to regenerate these side-products every time.
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+
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+ ---
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+
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+ ## Folder layout
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+
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+ | Folder | Contents | Shape / format |
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+ | ------ | -------- | -------------- |
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+ | `wav/<spk>/` | 48 kHz 16‑bit mono `.wav` files | `p225_001.wav`, … |
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+ | `txt/<spk>/` | original plain‑text transcript | `p225_001.txt`, … |
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+ | `phonemized/<spk>/` | whitespace‑separated IPA symbols, **`#h`** = word boundary | `p225_001.txt`, … |
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+ | `segments/<spk>/` | JSON with per‑phoneme timing & mean pitch | `p225_001.json`, … |
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+ | `context_embeddings/<spk>/` | NumPy float32 `.npy`, sentence embedding of the utterance | `p225_001.npy`, … |
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+ | `speaker_embeddings/` | NumPy float32 `.npy`, *one* vector per speaker, generated from **NVIDIA** `TitaNet-Large` model | `p225.npy`, … |
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+
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+ <details>
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+ <summary>Example <code>segments</code> entry</summary>
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+
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+ ```json
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+ {
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+ "0": ["h#", {"start_sec":0.0,"end_sec":0.10,"duration_sec":0.10,"mean_pitch":0.0}],
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+ "1": ["p", {"start_sec":0.10,"end_sec":0.18,"duration_sec":0.08,"mean_pitch":0.0}],
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+ "2": ["l", {"start_sec":0.18,"end_sec":1.32,"duration_sec":1.14,"mean_pitch":1377.16}]
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+ }
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+ ```
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+ </details>
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+
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+ ---
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+
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+
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+ ---
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+
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+ ## Quick start
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+
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+ ```python
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+ from pathlib import Path
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+ import json, soundfile as sf
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+ import numpy as np
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+
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+ root = Path("Phonemized-VCTK")
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+
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+ wav, sr = sf.read(root/"wav/p225/p225_001.wav")
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+ text = (root/"txt/p225/p225_001.txt").read_text().strip()
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+ ipa = (root/"phonemized/p225/p225_001.txt").read_text().strip()
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+ segs = json.loads((root/"segments/p225/p225_001.json").read_text())
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+ ctx = np.load(root/"context_embeddings/p225/p225_001.npy")
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+
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+ print(text)
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+ print(ipa.split()) # IPA tokens
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+ print(ctx.shape) # (384,)
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+ ```
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+
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+ ---
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+
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+
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+ ---
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+
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+ ## Known limitations
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+
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+ * The phone set is plain IPA—no stress or intonation markers.
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+ * English only (≈109 speakers, various accents).
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+ * Pitch = 0 on unvoiced phones; interpolate if needed.
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+ * Embedding models were chosen for convenience—swap as you like.
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+
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+ ---
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+
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+ ## Citation
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+
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+ Please cite **both** VCTK and this derivative if you use the corpus:
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+
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+ ```bibtex
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+ @misc{yours2025phonvctk,
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+ title = {Phonemized-VCTK: An enriched version of VCTK with IPA, alignments and embeddings},
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+ author = {Your Name},
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+ year = {2025},
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+ howpublished = {\url{https://huggingface.co/datasets/your-handle/phonemized-vctk}}
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+ }
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+
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+ @inproceedings{yamagishi2019cstr,
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+ title={The CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
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+ author={Yamagishi, Junichi et al.},
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+ booktitle={Proc. LREC},
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+ year={2019}
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+ }
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+ ```
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+
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+ ---
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