---
language: fa
license: cc-by-4.0
tags:
- audio
- speech-recognition
- fleurs
- persian
- farsi
pretty_name: FLEURS Farsi (fa_ir) Processed
dataset_info:
  features:
  - name: audio
    dtype: audio
    sampling_rate: 16000
  - name: transcription
    dtype: string
  splits:
    dev:
      name: dev
      num_bytes: 352117826
      num_examples: 369
    test:
      name: test
      num_bytes: 852844223
      num_examples: 871
    train:
      name: train
      num_bytes: 2811193790
      num_examples: 3101
  download_size: 3934177703
  dataset_size: 4016155839
configs:
- config_name: default
  data_files:
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
  - split: train
    path: data/train-*
task_categories:
- automatic-speech-recognition
---

# FLEURS Farsi (fa_ir) - Processed Dataset

## Dataset Description

This dataset contains the Farsi (Persian, `fa_ir`) portion of the **FLEURS (Few-shot Learning Evaluation of Universal Representations of Speech)** dataset, processed into a Hugging Face `datasets` compatible format. FLEURS is a many-language speech dataset created by Google, designed for evaluating speech recognition systems, particularly in low-resource scenarios.

This version includes audio recordings and their corresponding transcriptions, split into train, development (dev), and test sets. The audio is sampled at 16kHz.

**Languages:** Farsi (Persian) - `fa`

## How to Use

You can load this dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset_name = "msghol/fleurs-farsi" # Updated repository name
fleurs_farsi = load_dataset(dataset_name)

# Accessing a split
print(fleurs_farsi["train"])

# Accessing an example
example = fleurs_farsi["train"][0]
audio_sample = example["audio"]
transcription = example["transcription"]

print(f"Audio: {audio_sample}")
print(f"Transcription: {transcription}")