README.md

---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: speaker
    dtype: string
  - name: nano_layer_1
    list: int64
  - name: nano_layer_2
    list: int64
  - name: nano_layer_3
    list: int64
  - name: nano_layer_4
    list: int64
  - name: encoded_len
    dtype: int64
  splits:
  - name: train
    num_bytes: 58048206
    num_examples: 9949
  download_size: 8076967
  dataset_size: 58048206
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
task_categories:
- text-to-speech
language:
- hi
---

# 🗣️ tts-quantized-dataset

This dataset contains **quantized Hindi Text-to-Speech (TTS)** samples generated using NVIDIA’s [`nemo-nano-codec-22khz-0.6kbps-12.5fps`](https://huggingface.co/nvidia/nemo-nano-codec-22khz-0.6kbps-12.5fps) neural audio codec.  
It is designed for **training lightweight speech synthesis models**, such as token-based TTS models, audio language models, or text-to-codec models.

## 📚 Dataset Summary

| Field | Description |
|--------|--------------|
| **text** | The transcription (Hindi text) corresponding to each audio sample. |
| **speaker** | Speaker identity (derived from the file name). |
| **nano_layer_1–4** | Discrete audio tokens from four quantization layers of the NeMo Nano Codec model. |
| **encoded_len** | Length of each encoded token sequence. |

## 🧠 Source Dataset

- **Original dataset:** [`SayantanJoker/original_data_hindi_tts`](https://huggingface.co/datasets/SayantanJoker/original_data_hindi_tts)  
- **Conversion tool:** [NVIDIA NeMo AudioCodecModel](https://docs.nvidia.com/nemo-framework/user-guide/docs/en/stable/tts/overview.html#audio-codec-model)  
- **Sampling rate:** 22.05 kHz  
- **Compression:** 0.6 kbps (4 quantization layers, 12.5 fps)

## ⚙️ Data Preparation Pipeline

The dataset was preprocessed using the following steps:

1. Load original Hindi TTS dataset from Hugging Face.
2. Resample all audio files to **22,050 Hz**.
3. Encode audio waveforms using `AudioCodecModel` from NVIDIA NeMo.
4. Store the resulting discrete codec tokens into 4 quantizer layers.
5. Save as Hugging Face `DatasetDict` and push to Hub.

The exact preprocessing script used:
```python
https://huggingface.co/ArunKr/tts-quantized-dataset/blob/main/data_prep.py
````
## 🧩 Example Entry

```json
{
  "text": "यह एक परीक्षण वाक्य है।",
  "speaker": "sample_001",
  "nano_layer_1": [3, 18, 94, 105, 78, ...],
  "nano_layer_2": [11, 45, 67, 53, 21, ...],
  "nano_layer_3": [32, 98, 76, 12, 43, ...],
  "nano_layer_4": [25, 14, 89, 64, 72, ...],
  "encoded_len": 120
}
```
## 🧰 Usage Example

```python
from datasets import load_dataset
ds = load_dataset("ArunKr/tts-quantized-dataset", split="train")

print(ds[0]["text"])
# "यह एक परीक्षण वाक्य है।"

codes = [ds[0]["nano_layer_1"], ds[0]["nano_layer_2"], ds[0]["nano_layer_3"], ds[0]["nano_layer_4"]]
```

You can easily convert back to waveform using the same NeMo codec:

```python
from nemo.collections.tts.models import AudioCodecModel
import torch

codec = AudioCodecModel.from_pretrained("nvidia/nemo-nano-codec-22khz-0.6kbps-12.5fps").eval()
tokens = torch.tensor([codes])
audio = codec.decode(tokens)
```
## 📦 Dataset Structure

```
tts_quantized_dataset/
├── data_prep.py
├── README.md
├── data/
```

## 🧾 License

* **Source Dataset License:** As provided by the original dataset ([`SayantanJoker/original_data_hindi_tts`](https://huggingface.co/datasets/SayantanJoker/original_data_hindi_tts))
* **Generated Dataset License:** CC BY 4.0
  (Attribution required for use and redistribution)

## 🙏 Acknowledgments

* [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) for the neural codec model.
* [Hugging Face Datasets](https://huggingface.co/docs/datasets) for dataset hosting and preprocessing utilities.
* [SayantanJoker/original_data_hindi_tts](https://huggingface.co/datasets/SayantanJoker/original_data_hindi_tts) for providing the original Hindi speech dataset.

## 💬 Citation

If you use this dataset, please cite:

```bibtex
@dataset{arunkr_tts_quantized_dataset_2025,
  author = {Arun Kumar Tiwary},
  title  = {Hindi TTS Quantized Dataset using NeMo Nano Codec},
  year   = {2025},
  url    = {https://huggingface.co/datasets/ArunKr/tts-quantized-dataset}
}
```