Datasets:
metadata
language:
- fa
license: cc-by-nc-4.0
tags:
- speech
- audio
- persian
- asr
- aligned
pretty_name: PersianVox_HB
task_categories:
- automatic-speech-recognition
extra_gated_prompt: >-
You agree to not use the dataset to conduct experiments that mimic or clone
the speaker for unethical purposes. I will review the requests manually and
accept only if it is for research purposes. So please take the time to fill in
the necessary fields especially the affiliation.
extra_gated_fields:
First Name: text
Last Name: text
Country: country
Affiliation: text
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dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 24000
- name: audio_filepath
dtype: string
- name: duration
dtype: float64
- name: text_no_preprocessing
dtype: string
- name: text_normalized
dtype: string
- name: text
dtype: string
- name: score
dtype: float64
- name: start_abs
dtype: float64
- name: end_abs
dtype: float64
- name: tokens
dtype: int64
- name: pred_text_nvidia_fastconformer
dtype: string
- name: cer_nvidia_fastconformer
dtype: float64
- name: ins_rate_nvidia_fastconformer
dtype: float64
- name: del_rate_nvidia_fastconformer
dtype: float64
- name: sub_rate_nvidia_fastconformer
dtype: float64
- name: wer_nvidia_fastconformer
dtype: float64
- name: pred_text_normalized_nvidia_fastconformer
dtype: string
- name: pred_text_wav2vec2_v3
dtype: string
- name: cer_wav2vec2_v3
dtype: float64
- name: ins_rate_wav2vec2_v3
dtype: float64
- name: del_rate_wav2vec2_v3
dtype: float64
- name: sub_rate_wav2vec2_v3
dtype: float64
- name: wer_wav2vec2_v3
dtype: float64
- name: pred_text_normalized_wav2vec2_v3
dtype: string
- name: pred_text_faster_whisper_large
dtype: string
- name: cer_faster_whisper_large
dtype: float64
- name: ins_rate_faster_whisper_large
dtype: float64
- name: del_rate_faster_whisper_large
dtype: float64
- name: sub_rate_faster_whisper_large
dtype: float64
- name: wer_faster_whisper_large
dtype: float64
- name: pred_text_normalized_faster_whisper_large
dtype: string
splits:
- name: train
num_bytes: 14785911739.44
num_examples: 89597
download_size: 16233557395
dataset_size: 14785911739.44
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
PersianVox_HB
PersianVox_HB is a high-quality, multispeaker Persian speech dataset derived from audio recordings of the Holy Bible. The dataset is sourced from bible.com (PCB=49.85 hours, TPV=71.4 hours), bible.is (NMV=24.17 hours), and wordproject.org (PHB=81.92 hours).
📚 Dataset Summary
- Language: Persian (Farsi)
- Speakers: Multiple speakers
- Total Duration: 227.34 hours
- Recording Sources:
- bible.com (PCB: 49.85 hours, TPV: 71.4 hours)
- bible.is (NMV: 24.17 hours)
- wordproject.org (PHB: 81.92 hours)
- Domain: Religious texts (Holy Bible)
- Alignment Checked With:
- In-house Whisper model fine-tuned from whisper-large-v3
- In-house NVIDIA FastConformer fine-tuned from NVIDIA FastConformer-Hybrid Large (fa)
- Public wav2vec2_v3
🔑 Key Descriptions
The dataset entries are stored as dictionaries with the following keys:
🧩 Common Keys
| Key | Description |
|---|---|
audio_filepath |
Path to the audio file (usually relative to the dataset root) |
duration |
Length of the audio in seconds |
text |
Normalized transcript with normalization, space correction that is used to calculate metrics |
text_no_preprocessing |
Transcript before any text cleaning, normalization, or punctuation adjustments |
text_normalized |
Normalized version of the transcript, suitable for ASR training or evaluation |
score |
Average confidence score indicating alignment quality in the log space. Utterances with scores lower than -2 are dropped |
start_abs |
Average absolute amplitude of the beginning portion of the audio segment (used to evaluate energy or silence at the start) |
end_abs |
Average absolute amplitude of the ending portion of the audio segment (used to evaluate trailing silence or noise) |
tokens |
Number of tokens in text (BPE) |
🤖 Model-Specific Keys
For each ASR model used to evaluate the audio, the following metrics are added, prefixed by the model name.
For example, for the NVIDIA FastConformer model, you'd have keys like cer_nvidia_fastconformer, wer_nvidia_fastconformer, etc.
| Key Template | Description |
|---|---|
pred_text_{model} |
Raw predicted transcript from the ASR model |
pred_text_normalized_{model} |
Normalized-space-corrected version of the predicted transcript used to calculate metrics |
cer_{model} |
Character Error Rate between prediction and ground truth |
wer_{model} |
Word Error Rate between prediction and ground truth |
ins_rate_{model} |
Insertion rate — proportion of extra characters inserted |
del_rate_{model} |
Deletion rate — proportion of characters the model missed |
sub_rate_{model} |
Substitution rate — proportion of characters wrongly predicted |
🔄 Licensing
This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License, as the source materials do not permit commercial use. You must credit the original sources (bible.com, bible.is, wordproject.org) when using or sharing this dataset.
🧠 Intended Use and Limitations
This dataset is ideal for:
- Training and benchmarking ASR systems in Persian
- Research on model robustness via ASR scoring
- Speech alignment evaluation for religious texts
🚫 Ethical Notice
Please do not use this dataset to clone or imitate the speakers’ voices for malicious or unethical purposes. Respect the non-commercial restrictions of the source materials.
✉️ Citation
To cite this dataset:
@misc{persianvox_hb,
title = {PersianVox_HB: A multispeaker Persian speech dataset from the Holy Bible},
author = {Saeedreza Zouashkiani},
year = 2025,
url = {https://huggingface.co/datasets/persianvox_hb},
note = {Derived from bible.com, bible.is, and wordproject.org, licensed under CC BY-NC 4.0}
}