alakxender/dhivehi-audio-kn

Dhivehi Audio Dataset

This is a Dhivehi (Maldivian) speech synthesis dataset with audio recordings, text transcriptions, and phonetic annotations. All recordings are by one speaker.

Dataset Overview

This dataset provides 4,170 high-quality audio samples in Dhivehi.

Key Statistics

Metric	Value
Total Audio Files	4,170
Total Duration	5.87 hours (352.0 minutes)
Average Clip Length	5.06 seconds
Total Words	30,068
Unique Phonemes	14190
Unique Characters	62

Audio Specifications

• Sample Rate: 22050 Hz
• Channels: 1 (Mono/Stereo)
• Bit Depth: 16 bit
• Format: WAV (uncompressed)

Data Distribution

Duration Analysis

• Shortest clip: 1.18 seconds
• Longest clip: 19.08 seconds
• Standard deviation: 2.41 seconds

Text Characteristics

• Average sentence length: 65.7 characters
• Average words per sentence: 7.2
• Average phonemes per sentence: 7.2
• Sentence length range: 6 - 198 characters

Most Common Phonemes

Phoneme	Count	Frequency
ʋes̺	221	0.73%
e	212	0.70%
eːn̪aː	205	0.68%
ahaɾeŋ	170	0.56%
ad̪i	160	0.53%
ʋaɾaʔ	155	0.52%
mi	149	0.50%
eiː	146	0.49%
n̪amaʋes̺	144	0.48%
n̪uːŋ	140	0.47%

Common Voice 17.0 Comparison

This dataset was evaluated against the Dhivehi portion of Mozilla’s Common Voice 17.0 to assess overlap and uniqueness. The results are as follows:

Metric	Value
Common Voice Sentences	7,117
This Dataset Sentences	4,170
Overlapping Sentences	3,443 (82.6%)
Unique to This Dataset	727 (17.4%)

Although a substantial number of sentences are shared between the two datasets, each sentence in this dataset is recorded by a different speaker. This contributes to greater speaker diversity, making the dataset particularly suitable for training and evaluating speaker-independent models.

Key Insights

• 17.4% Unique Sentences: A notable portion of this dataset comprises sentences not present in Common Voice 17.0, contributing new linguistic content to the Dhivehi speech domain.
• Speaker and Quality Variation: Shared sentences offer a basis for evaluating speaker diversity and audio quality differences across datasets.
• Complementary Value: Serves as a valuable supplement to Common Voice 17.0 by providing additional recordings and speaker variability, enhancing the robustness of Dhivehi speech model training.

Example Sentence from Common Voice 17.0 Compared to This Dataset

Common Voice 17.0	This Dataset

Use Cases

This dataset is suitable for:

• Text-to-Speech (TTS) synthesis - Training neural speech synthesis models
• Automatic Speech Recognition (ASR) - Dhivehi speech recognition systems
• Phonetic research - Analysis of Dhivehi phonology and pronunciation
• Language preservation - Digital archiving of Dhivehi speech patterns
• Cross-lingual studies - Comparative phonetic analysis

Loading the Dataset

Using 🤗 Datasets

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("alakxender/dhivehi-audio-kn")

# Access training split
train_data = dataset["train"]

# Example: Get first sample
sample = train_data[0]
print(f"Text: {sample['sentence']}")
print(f"Phonemes: {sample['phonemes']}")
# Audio is available in sample['audio']

Acknowledgments

Special thanks to @kudanai for providing the dataset used as the source. This dataset retains the same speaker, offering consistent voice characteristics across all recordings.