Datasets:

arvinsingh
/

welsh-speech-dataset

license: cc-by-nc-4.0
task_categories:
  - automatic-speech-recognition
  - text-to-speech
  - text-to-audio
language:
  - cy
tags:
  - speech
  - welsh
  - cymraeg
  - 3d-face
  - facial-landmarks
  - multimodal
  - fluency
  - pronunciation
  - 4d-dataset
size_categories:
  - 100K<n<1M
pretty_name: CymruFluency Welsh Speech Dataset

Welsh Speech Dataset

A multimodal dataset of 33 speakers producing 10 Welsh phrases, captured using 3DMD technology with audio and dense facial landmark annotations.

Dataset Overview

Speakers: 33 participants
Phrases: 10 Welsh phrases per speaker
Sequences: ~330 (33 speakers x 10 phrases)
Modalities:
- Audio recordings (.wav)
- 3D facial reconstructions (.obj meshes + texture maps)
- 68-point facial landmarks (ibug68 template)
Fluency Scores: Each phrase rated 0-5 (5 = perfect, 0 = many errors)

Preview

Subject uttering Welsh phrase “Gwybodaeth angenrheidiol” (Tr. EN: Necessary information; IPA: /ˈɡʊɨ̯bɔðaɪθ aŋɛnˈhreɪ̯djɔl/)

Repository Structure

This dataset is split into 4 repositories for convenience:

welsh-speech-dataset (this repo) - Main hub with sequence-level metadata
welsh-speech-audio - Audio recordings only
welsh-speech-3d-meshes - 3D facial meshes (zipped per sequence)
welsh-speech-landmarks - Facial landmarks (frame-level Parquet)

Metadata

The metadata.csv and metadata.parquet files contain sequence-level data (one row per speaker-phrase):

Column	Description
`speaker_id`	Speaker identifier (1-33)
`phrase_id`	Phrase identifier (1-10)
`audio_path`	Path to audio file
`mesh_zip_path`	Path to 3D mesh zip file
`fluency_score`	Pronunciation quality score (0-5)
`welsh_text`	Welsh phrase text
`english_translation`	English translation
`num_frames`	Number of frames in the sequence
`has_3d`	Boolean indicating 3D data availability
`has_landmark`	Boolean indicating landmark availability

Note: Landmark data is stored in landmarks.parquet in the landmarks repository at frame-level. Join using speaker_id and phrase_id to combine with this sequence-level metadata.

Welsh Phrases

ID	Welsh Text	English Translation
1	Eisteddfod yr Urdd	Welsh Youth Music Competition
2	Prynhawn da bawb	Good afternoon everyone
3	Dyn busnes yw e	It's a businessman
4	Papur a phensil	Paper and pencil
5	Ardderchog	Excellent / Superb
6	Llwyddiant ysgubol	Great success
7	Yng nghanol y dref	In the town center
8	Dwy neuadd gymunedol	Two community halls
9	Llunio rhestr fer	Shortlisted
10	Gwobodaeth angenrheidiol	Necessary information

Usage

Load Metadata

import pandas as pd

# load sequence-level metadata
metadata = pd.read_parquet("metadata.parquet")

# filter by fluency score
high_quality = metadata[metadata['fluency_score'] >= 4]

# get info for specific speaker/phrase
seq = metadata[(metadata['speaker_id'] == 1) & (metadata['phrase_id'] == 1)].iloc[0]
print(f"Frames: {seq['num_frames']}, Fluency: {seq['fluency_score']}")

Access Specific Modalities

Download only what you need:

from huggingface_hub import hf_hub_download
import zipfile

# download audio
audio_file = hf_hub_download(
    repo_id="arvinsingh/welsh-speech-audio",
    filename="audio/speaker_01_phrase_01.wav",
    repo_type="dataset"
)

# download 3D mesh zip for a sequence
mesh_zip = hf_hub_download(
    repo_id="arvinsingh/welsh-speech-3d-meshes",
    filename="meshes/speaker_01_phrase_01.zip",
    repo_type="dataset"
)

# extract meshes
with zipfile.ZipFile(mesh_zip, 'r') as zf:
    zf.extractall("speaker_01_phrase_01")
    # Contains: 001.obj, 001.png, 002.obj, 002.png, ...

# load landmarks (frame-level)
import pandas as pd
landmarks = pd.read_parquet(
    hf_hub_download(
        repo_id="arvinsingh/welsh-speech-landmarks",
        filename="landmarks.parquet",
        repo_type="dataset"
    )
)

# join landmarks with main metadata to get fluency scores
merged = landmarks.merge(metadata, on=['speaker_id', 'phrase_id'])

Citation

If you use this dataset, please cite both the paper and the dataset:

@inproceedings{bali_2026_cymrufluency,
  author       = {Bali, Arvinder Pal Singh and
                  Tam, Gary KL and
                  Siris, Avishek and
                  Andrews, Gareth and
                  Lai, Yukun and
                  Tiddeman, Bernie and
                  Ffrancon, Gwenno},
  title        = {CymruFluency - A Fusion Technique and a 4D Welsh Dataset for Welsh Fluency Analysis},
  booktitle    = {Advanced Concepts for Intelligent Vision Systems},
  pages        = {96--108},
  year         = 2026,
  publisher    = {Springer Nature Switzerland},
  doi          = {10.1007/978-3-032-07343-3_8},
  url          = {https://doi.org/10.1007/978-3-032-07343-3_8},
}

@dataset{bali_2025_dataset,
  author       = {Bali, Arvinder Pal Singh and
                  Tam, Gary KL and
                  Siris, Avishek and
                  Andrews, Gareth and
                  Lai, Yukun and
                  Tiddeman, Bernie and
                  Ffrancon, Gwenno},
  title        = {Dataset and code for "CymruFluency - A fusion technique and a 4D Welsh dataset for Welsh fluency analysis"},
  month        = may,
  year         = 2025,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.15397513},
  url          = {https://doi.org/10.5281/zenodo.15397513},
}

Original Data

The original dataset is published on Zenodo: 10.5281/zenodo.15397513

License

Creative Commons Attribution-NonCommercial 4.0 International License.

Acknowledgments

Dataset collected using 3DMD facial capture technology. All frames manually annotated with ibug68 facial landmarks.