Datasetpublic

Thomcles/YodaLingua-Farsi

YodaLingua-Farsi YodaLingua is a high-quality speech dataset designed for training text-to-speech (TTS) systems, ASR models, and any application requiring clean, well-aligned audio–text pairs.This release contains the Farsi portion of the multilingual YodaLingua collection. 🧾 Dataset Overview Property Value Total clips 23,419 audio–transcription pairs Total duration 72 hours Speakers 678 distinct speakers Audio format MP3 • mono • 24 kHz • 16-bit… See the full description on the dataset page: https://huggingface.co/datasets/Thomcles/YodaLingua-Farsi.

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Dataset Card

YodaLingua-Farsi

YodaLingua is a high-quality speech dataset designed for training text-to-speech (TTS) systems, ASR models, and any application requiring clean, well-aligned audio–text pairs. This release contains the Farsi portion of the multilingual YodaLingua collection.

🧾 Dataset Overview

Property	Value
Total clips	23,419 audio–transcription pairs
Total duration	72 hours
Speakers	678 distinct speakers
Audio format	MP3 • mono • 24 kHz • 16-bit
License	Permissive — commercial use allowed

All audio clips are noise-reduced, normalized, and matched with accurate transcriptions.

Data Fields

Each entry in the dataset contains the following fields:

Field	Description
`__key__`	Unique identifier for each sample.
`audio`	Path to the audio file associated with the sample (MP3 format).
`text`	Ground-truth transcription of the audio segment.
`language`	Language code following ISO 639 standards.
`speaker_id`	Unique identifier assigned to each speaker. Multiple audio can share the same speaker ID.
`dnsmos`	DNSMOS P.835 Overall (OVRL) score estimating perceptual speech quality; higher values indicate cleaner and more intelligible audio.

🌍 Multilingual Versions

Other languages are available in the YodaLingua multilingual collection: 👉 https://huggingface.co/collections/Thomcles/yodalingua

We apply a multi-stage pipeline to ensure maximum data quality:

1. Standardization

—Convert to WAV
—Mono channel
—Resample to 24 kHz
—16-bit sample width
—Normalize to –20 dBFS (with volume correction between –3 and +3 dB)

2. Noise Reduction

Advanced denoising applied to improve clarity and remove background artifacts.

3. Speaker Diarization

Segment long recordings by speaker to improve diversity and ensure speaker-consistent utterances.

4. Voice Activity Detection (VAD)

Merge consecutive VAD segments from the same speaker into clean utterances of 3–30 s.

5. Transcription

State-of-the-art ASR models produce accurate text transcripts.

6. Quality Filtering

Clips are filtered using DNSMOS P.835 OVRL; only samples with a score > 3.0 are retained.

📚 Loading the Dataset

python

from datasets import load_dataset

ds = load_dataset("Thomcles/YodaLingua-Farsi")

Contact

e-mail : cyprienoucortex@gmail.com