---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: text
    dtype: string
  - name: duration
    dtype: float64
  - name: token_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 5374523458.74
    num_examples: 11444
  - name: dev
    num_bytes: 338070338
    num_examples: 648
  - name: test
    num_bytes: 470470140
    num_examples: 899
  download_size: 13334454656
  dataset_size: 6183063936.74
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- automatic-speech-recognition
- text-to-audio
language:
- uz
pretty_name: FeruzaSpeech
size_categories:
- 10K<n<100K
---

# FeruzaSpeech_to_fine_tuning

A speech corpus of **⏱️ ~59.1 total hours** of Uzbek audio paired with Latin‑script transcripts, intended for fine‑tuning ASR / speech‑to‑text models.

---

## Dataset Details

### Dataset Description

This dataset contains recordings of native Uzbek speakers reading a mix of classical literature excerpts and school‑level writing prompts:

- **001**: Choliqushi (a novel by Rashod Nuri Guntekin, trans. by Mirzakalon Ismoiliy; first pub. Sept 1900).  
- **002**: Excerpts from Uzbek secondary‑school essays (“To‘rtinchi sinfda edim…”, “Hayotdagi ilk xotiralaringizni yozing…”).  
- .....

Each line in `text_latin.txt` is of the form:








//


We strip the filename prefix in preprocessing so that the `text` field contains only the spoken words.

### Dataset Statistics

| Split | # Examples | Total Size | Approx. Duration |
|-------|-----------:|-----------:|-----------------:|
| train |     11 444 | 5.37 GB    | 52.09 hours |
| dev   |        648 | 0.34 GB    | 2.93 hours |
| test  |        899 | 0.47 GB    | 4.08 hours |


## Dataset Creation

### Curation Rationale

We aim to provide a high‑quality, publicly available Uzbek ASR dataset combining both literary and educational domains to improve model robustness.

### Source Data

- Audio recorded in a quiet home‑studio environment, 16 kHz mono WAV, 16‑bit PCM.  
- Transcripts created from existing texts (classical novels, school writing prompts).  

### Who Are the Source Data Producers?

- **Recordings & Transcriptions by:** k2speech/FeruzaSpeech  
- **Translators / Editors:** Nickoo 004  

## Uses

### Direct Use

Fine‑tuning or evaluating speech‑to‑text/ASR models for Uzbek. It’s also suitable for speech processing research (voice activity detection, speaker diarization, etc.).

### Out‑of‑Scope Use

- Speaker identification / sensitive demographic inference.  
- Real‑time speech generation.

## Supported Tasks and Leaderboards

- **Task:** Automatic Speech Recognition  

## Dataset Structure

Each example has the following fields:

- `audio`: an `Audio` object (`array` + `sampling_rate`)  
- `text`: Latin‑script transcript, cleaned of filename tokens  
- `duration`: audio length in seconds  
- `token_count`: length of the transcript in raw word‑piece tokens  

## Distribution

- **License:** Apache 2.0  
- **Repository:** https://huggingface.co/datasets/nickoo004/FeruzaSpeech_to_fine_tuning

## Who Maintains This Dataset

- **Created and maintained by:** Nickoo 004  
- **Last updated:** 2025‑05‑02  
- **Contact & Social:**  
  - Email: nursultankoshekbaev477@gmail.com

## Citation

If you use this dataset, please cite:

```bibtex
@misc{feruzaspeech2025,
  title        = {FeruzaSpeech\_to\_fine\_tuning: An Uzbek ASR Dataset},
  author       = {Nickoo\, 004},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/datasets/nickoo004/FeruzaSpeech_to_fine_tuning}},
  license      = {Apache 2.0}
}