README.md

---
dataset_name: indic-text-audio-sample
configs:
  - config_name: default
    data_files:
      - split: samples
        path: data*.parquet
    features:
      - name: language
        dtype: string
      - name: audio
        dtype: audio
      - name: text
        dtype: string
      - name: user_age
        dtype: string
      - name: user_gender
        dtype: string
license: cc-by-4.0
task_categories:
- audio-classification
- text-to-speech
- text-to-audio
- automatic-speech-recognition
language:
- hi
- ta
- te
- pa
- ml
- kn
- bn
- gu
- mr
pretty_name: IndicTextAudio Sample Dataset
size_categories:
- 10K<n<50K
---

# Dataset Card for Indic Text Audio Sample Dataset

## Dataset Details

### Dataset Description

The IndicTextAudioSample Dataset is a multilingual, text-speech pair sample dataset. It features human-voiced recordings of dialogues in nine Indian languages: Hindi, Tamil, Telugu, Punjabi, Malayalam, Kannada, Bengali, Gujarati, and Marathi.

- Curated by: [snorbyte](https://snorbyte.com/)
- Funded by: [snorbyte](https://snorbyte.com/)
- Shared by: [snorbyte](https://snorbyte.com/)
- Language(s) (NLP): hi, ta, te, pa, ml, kn, bn, gu, mr
- License: CC BY 4.0

### Dataset Sources

- Repository: [IndicTextAudioSample](https://huggingface.co/datasets/snorbyte/indic-text-audio-sample)

### Code

```bash
pip install huggingface_hub pandas pyarrow
```

```Python
import base64
import tempfile
import wave

from huggingface_hub import hf_hub_download
import pandas as pd

# Download the dataset file from Hugging Face
repo_id = "snorbyte/indic-text-audio-sample"
filename = "data_shard_000_zstd.parquet"
local_file = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
print("Downloaded to:", local_file)


# Load the Parquet file and get the first row
df = pd.read_parquet(local_file)
row = df.iloc[0]
print(row)

# Save the audio to a temporary WAV file
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
    audio_bytes = row["audio"]["bytes"]  # extract raw bytes
    f.write(audio_bytes)
    temp_audio_path = f.name

print("Audio saved to:", temp_audio_path)
```

## Uses

### Direct Use

The dataset is intended for a wide range of applications, including but not limited to:

- Automatic Speech Recognition (ASR): Training and evaluating systems that transcribe spoken language into text.
- Text-to-Speech (TTS): Synthesizing natural-sounding speech from text prompts and evaluating TTS models.
- Multilingual Modeling: Developing models that generalize across languages for both speech and text processing.
- Demographic-Aware Modeling: Using age and gender metadata to develop or audit models for fairness, personalization, and bias analysis.
- Voice Cloning and Speech Synthesis: Training or evaluating models for voice conversion and synthesis using speaker-specific audio samples.
- Audio Classification: Classifying attributes such as speaker gender, age group from audio signals.
- Language Identification: Determining the spoken language from an audio sample.

### Out-of-Scope Use

- Any use in sensitive applications like medical, legal, or surveillance without rigorous validation.
- Any use that attempts to infer personal attributes beyond what’s provided (age/gender).
- Generation or impersonation of real people using synthesized speech from dataset samples.

## Dataset Structure

Each record in the dataset corresponds to a single text transcript and audio recording pair, along with user metadata. The dataset includes:

### General Information

- language: Language used in the text and audio recording.
- audio: Complete conversation audio file in raw bytes.
- text: Text transcript of the audio.
- user_age: Age of the speaker.
- user_gender: Gender of the speaker.

### Sample

| language | audio | text                       | user_age | user_gender |
| -------- | ----- | -------------------------- | -------- | ----------- |
| hindi    | bytes | जीत-हार के सपने में खोये प्रत्याशी | 20.0     | man         |

The sample dataset includes ~ 100 hours of audio and its equivalent text transcripts.

It comprises approximately 53.8% male and 46.2% female speakers, with 50% of the data contributed by individuals aged 18–30.

The following table shows the number of text-audio-recordings by language.

| Language   | Count |
|------------|-------|
| Hindi      | 5131  |
| Tamil      | 5356  |
| Gujarati   | 5576  |
| Kannada    | 5308  |
| Bengali    | 5752  |
| Punjabi    | 5044  |
| Telugu     | 5259  |
| Marathi    | 5752  |
| Malayalam  | 5148  |


### Source Data

#### Purpose

This dataset aims to accelerate the development of language technologies in the Indic ecosystem by providing accessible and diverse resources.

#### Who are the source data producers?

All speakers voluntarily participated in the project and were compensated for their audio recordings. They represented a diverse range of age groups, genders, and professions.

#### Personal and Sensitive Information

- No personally identifiable information (PII) is present.
- Only age (grouped) and gender metadata are retained.
- All user IDs are anonymized.

### Recommendations

- Supplement with additional datasets to improve dialect and age diversity.
- Validate model behavior across all demographic segments.
- Avoid over-interpreting demographic signals unless explicitly modeled and evaluated.

## Citation

BibTeX:

```bibtex
@misc{indictextaudio2025,
  title={IndicTextAudio Sample Dataset},
  author={snorbyte},
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/snorbyte/indic-text-audio-sample}},
  note={CC-BY 4.0}
}