Datasets:
File size: 5,428 Bytes
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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 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}
}
```
--- |