Datasets:
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---
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}") |