README.md

---
dataset_name: IndicST
configs:
  - config_name: ast
    description: "Dataset for AST (Automated Speech Transcription)"
    data_files:
      - split: train
        path: ast/train.json
      - split: dev
        path: ast/dev.json
      - split: test
        path: ast/test.json
      - split: kathbath
        path: ast/kathbath.json
      - split: svarah
        path: ast/svarah.json
    features:
      - name: path
        dtype: string
      - name: text
        dtype: string
      - name: task
        dtype: string
      - name: source_text
        dtype: string
      - name: source_language
        dtype: string
      - name: tar_lang
        dtype: string
                                       
  - config_name: asr
    description: "Dataset for ASR (Automatic Speech Recognition)"
    data_files:
      - split: train
        path: asr/train.json
      - split: dev
        path: asr/dev.json
      - split: test
        path: asr/test.json
      - split: kathbath
        path: asr/kathbath.json
      - split: svarah
        path: asr/svarah.json
    features:
      - name: path
        dtype: string
      - name: text
        dtype: string
      - name: task
        dtype: string
language:
  - hi
  - kn
  - mr
  - ta
  - te
  - bn
  - ml
  - en
  - gu
license: other
license_name: krutrim-community-license-agreement-version-1.0
license_link: LICENSE.md
extra_gated_heading: Acknowledge license to accept the repository
extra_gated_button_content: Acknowledge license
---

# IndicST: Indian Multilingual Translation Corpus For Evaluating Speech Large Language Models

## Introduction
IndicST, a new dataset tailored for training and evaluating Speech LLMs for AST tasks (including ASR and TTS), featuring meticulously curated, automatically, and manually verified synthetic data. The dataset offers 10.8k hrs of training data and 1.13k hrs of evaluation data. 

## Use-Cases
### ASR (Speech-to-Text)
- Transcribing Indic languages
- Handling accents and noisy environments
- Supporting low-resource language ASR

### Automatic Speech Translation (AST)
- Speech-to-speech and speech-to-text translation
- Real-time multilingual communication

## Dataset Details
Training data: We utilized ASR data from 14 open-source datasets available publicly, collectively 10.8k hrs spread over nine languages, and Table 1 provides more details. Each dataset consists of input speech audio along with transcription. To synthetically generate the translation for input speech audio and transcription, we used 
[IndicTrans2](https://github.com/AI4Bharat/IndicTrans2) tool. we consider two translation directions: one-to-many, where English (source) transcription is translated to text in 8 Indian languages (target), represented as en → X, and many-to-one, where transcription in 8 Indian languages (source) is translated to English (target), represented as X →en. 
- **One-to-many**: English (source) transcription is translated to text in 8 Indian languages (target).
- **Many-to-one**: Transcription in 8 Indian languages (source) is translated to English (target).


### Table1: Summary of ASR Datasets for various Indian Languages used for training Speech LLM. The Duration is mentioned in K Hrs. 

| Datasets               | en | hi | mr | gu | bn | ta | te | ml | kn | Duration (k hrs) |
|------------------------|----|----|----|----|----|----|----|----|----|------------------|
| **Spring Labs**        | ✔  | ✔  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | 2.2              |
| **Common accent**      | ✔  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | 0.01             |
| **MUCS**               | ✘  | ✔  | ✔  | ✘  | ✔  | ✘  | ✘  | ✘  | ✘  | 0.22             |
| **CMU**                | ✘  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✘  | ✔  | 0.06             |
| **CommonVoice**        | ✘  | ✔  | ✔  | ✘  | ✔  | ✔  | ✔  | ✔  | ✘  | 1.6              |
| **Gramavaani**         | ✘  | ✔  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | 0.095            |
| **Vaani**              | ✘  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✘  | ✔  | 0.074            |
| **Lahaja**             | ✘  | ✔  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | ✘  | 0.011            |
| **Shrutilipi**         | ✘  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | 5.319            |
| **Google Corpus**      | ✘  | ✘  | ✔  | ✔  | ✘  | ✔  | ✔  | ✔  | ✔  | 0.034            |
| **Google Fleurs**      | ✘  | ✘  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | 0.087            |
| **Microsoft Speech Corpus** | ✘ | ✘ | ✘ | ✔ | ✘ | ✔ | ✔ | ✘ | ✘ | 0.12             |
| **IISc MILE**          | ✘  | ✘  | ✘  | ✘  | ✘  | ✔  | ✘  | ✘  | ✔  | 0.45             |
| **IndicVoices**        | ✘  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | ✔  | 0.52             |
| **Total Duration**     | 1.4 | 3  | 1.1 | 0.5 | 1.7 | 1.4 | 0.5 | 0.4 | 0.8 | **10.8k hrs**  |

The table includes indicators for language availability in datasets, where a check mark (✔) represents availability and a cross (✘) indicates the absence of support for that language. 

### Test set: For evaluation, we created a test set for Two scenarios 
- Input Speech audio available: We used the [Kathbath](https://huggingface.co/datasets/ai4bharat/kathbath) ASR dataset for this scenario to get the X → en translation pair (more details in Table 2) and the [Svarah](https://github.com/AI4Bharat/Svarah) dataset en → X translation pair.
- No input speech audio is available: For this case, we used the AI4Bharat Conv text-to-text translation dataset and speech audio for the source text pair generated using the [TTS](https://github.com/yl4579/StyleTTS2) model. The duration of this test set is available in Table III. More details about this dataset can be found in [IndicST](https://cdn.olaelectric.com/krutrim/IndicST_ICASSP2025.pdf) paper. 

### Table 2: Language-wise duration (hrs) of audios in Kathbath.

| Language | Duration (hrs) |
|----------|----------------|
| Hindi (hi) | 137.1          |
| Marathi (mr) | 166.5        |
| Gujarati (gu) | 116.2       |
| Bengali (bn) | 104.2        |
| Tamil (ta) | 166.3          |
| Telugu (te) | 139.2         |
| Malayalam (ml) | 132.2      |
| Kannada (kn) | 149.2        |


### Table 3: Language-wise duration (min) of audio in AI4Bharath

| Language | Duration (mins) |
|----------|-----------------|
| English (en) | 28.9          |
| Hindi (hi) | 36.1            |
| Marathi (mr) | 40.0          |
| Gujarati (gu) | 36.0         |
| Bengali (bn) | 44.3          |
| Tamil (ta) | 39.9            |
| Telugu (te) | 45.2           |
| Malayalam (ml) | 33.1        |
| Kannada (kn) | 35.3          |

## Evaluation Results
We have benchmarked the dataset for ASR and AST tasks using audio-llm (whisper + llama based LLM). We use whisper-large-v2 as a baseline for both the tasks. Results are given in Table IV and V, respectively, for ASR and AST tasks. 

### Table 4: erformance metric with TP1 (ASR-only) across different models on in-domain Generic-ASR and out-of-domain Svarah and Kathbath test sets. All values are in percentage.


| Languages | Baseline    | Baseline | Baseline | M1 (TP1)    | M1 (TP1) | M1 (TP1) | M2 (TP1)     | M2 (TP1) | M2 (TP1)  |
| --------- | ----------- | -------- | -------- | ----------- | -------- | -------- | ----------- | ------- | -------- |
| Languages | Generic-ASR | Svarah   | Kathbath | Generic-ASR | Svarah   | Kathbath | Generic-ASR | Svarah  | Kathbath |
| en        | 23.3        | 25.6     |          | 17.7        | 32       |          | 16.5        | 26.4    |          |
| hi        | 63.7        |          | 44.5     | 34.3        |          | 14.6     | 27.3        |         | 9.9      |
| mr        | 99.7        |          | 91       | 29.5        |          | 31.9     | 24.2        |         | 29.7     |
| gu        | 109.4       |          | 109.9    | 56.3        |          | 34.2     | 41.3        |         | 25.9     |
| bn        | 116.6       |          | 110.9    | 69.4        |          | 26.8     | 63.2        |         | 26.9     |
| ta        | 66.6        |          | 59.1     | 37.1        |          | 39.3     | 38          |         | 34.6     |
| te        | 111.3       |          | 112.7    | 75.4        |          | 51.1     | 68.5        |         | 37.1     |
| ml        | 111.7       |          | 117.5    | 47.6        |          | 47.2     | 47.4        |         | 46.6     |
| kn        | 87.7        |          | 82.4     | 56.9        |          | 44.2     | 42.1        |         | 30.4     |

### Table 5: Performance metric (BLEU) with TP2 (AST-only) and TP3 (ASR + AST) across different models on in-domain Generic-AST and out-of-domain Svarah, Kathbath, and AI4Bharat test sets. All values are in percentage.

### 5a. En -> X

| Models   | Datasets    | en→hi | en→mr | en→gu | en→bn | en→ta | en→te | en→ml | en→kn |
| -------- | ----------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
| Baseline | Generic-AST |       |       |       |       |       |       |       |       |
|| Svarah   |             |       |       |       |       |       |       |       |
|| Kathbath |             |       |       |       |       |       |       |       |
|| AI4B     |             |       |       |       |       |       |       |       |
| M1 (TP2) | Generic-AST | 30.2  | 19.9  | 25.1  | 24.4  | 18.5  | 19    | 16.7  | 18.8  |
|| Svarah   | 20.9        | 10.6  | 14.9  | 14.5  | 7.9   | 10.2  | 7.4   | 11.5  |
|| Kathbath |             |       |       |       |       |       |       |       |
|| AI4B     | 8.8         | 3.8   | 7.2   | 5.3   | 0.9   | 1.9   | 0.6   | 0.8   |
| M2 (TP2) | Generic-AST | 35.6  | 22.1  | 29    | 27.8  | 21.6  | 25    | 20    | 23.9  |
|| Svarah   | 28.9        | 15.1  | 17.7  | 19.2  | 11    | 14.2  | 10.6  | 11    |
|| Kathbath |             |       |       |       |       |       |       |       |
|| AI4B     | 13.4        | 6.9   | 9.5   | 6.3   | 1.6   | 2.1   | 1.2   | 1.2   |
| M2 (TP3) | Generic-AST | 37    | 22.6  | 30.8  | 28.6  | 23    | 25.4  | 20.6  | 23.7  |
|| Svarah   | 23.9        | 14.7  | 19.3  | 18.9  | 11.8  | 14.5  | 10.1  | 15.2  |
|| Kathbath |             |       |       |       |       |       |       |       |
|| AI4B     | 14.9        | 7.3   | 11.7  | 8.7   | 1.6   | 2.9   | 1.2   | 1.3   |

### 5b. X --> En

| Models   | Datasets    | hi→en | mr→en | gu→en | bn→en | ta→en | te→en | ml→en | kn→en |
| -------- | ----------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
| Baseline | Generic-AST | 16.9  | 13.1  | 10.7  | 7.7   | 11    | 7.7   | 11.9  | 8.1   |
|| Svarah   |             |       |       |       |       |       |       |       |
|| Kathbath | 28.1        | 13.9  | 16.8  | 11.8  | 11.1  | 12.8  | 17.6  | 10.1  |
|| AI4B     | 28.8        | 17.1  | 19.3  | 19.7  | 14.5  | 17.1  | 15.7  | 12.7  |
| M1 (TP2) | Generic-AST | 29.2  | 32.4  | 30    | 13    | 24.2  | 14.6  | 29    | 23.8  |
|| Kathbath | 36.6        | 22.3  | 25.3  | 20.8  | 17.7  | 19    | 22    | 15.9  |
|| AI4B     | 26.2        | 18.9  | 19.5  | 21.4  | 14.7  | 16.3  | 15.9  | 12.1  |
| M2 (TP2) | Generic-AST | 31    | 32    | 30.3  | 14.7  | 24.6  | 15    | 29.6  | 24.2  |
|| Kathbath | 37.2        | 23.9  | 25.1  | 20.6  | 17.2  | 19.1  | 22.4  | 16.8  |
|| AI4B     | 26.7        | 19.2  | 19.4  | 22.1  | 14.7  | 17.4  | 16    | 13    |
| M2 (TP3) | Generic-AST | 30.2  | 33    | 32.3  | 15.4  | 24.4  | 16.2  | 30.5  | 26.2  |
|| Kathbath | 38          | 24.2  | 25.6  | 22.3  | 18.4  | 20.2  | 22.5  | 17.3  |
|| AI4B     | 26.1        | 19.6  | 18.8  | 21.2  | 14    | 17.1  | 16.5  | 12.9  |


## Dataset Download
To download the dataset, visit the IndicST Hugging Face Repo:
- [IndicST Dataset on Hugging Face](https://huggingface.co/datasets/krutrim-ai-labs/IndicST/blob/main/ast)

## How to Use and Run
To use this dataset in your project, you can load it using a custom data loading script or directly access the files if integrated with a library that supports JSON. Example usage in Python:

```python
import json

def load_dataset(file_path):
    with open(file_path, 'r') as file:
        data = json.load(file)
    return data

# Load the training data
train_data = load_dataset('path/to/ast/train.json')
```

## License
 TBD

## Citation
```
@inproceedings{
  sanket2025IndicST,
  title={{IndicST}: Indian Multilingual Translation Corpus For Evaluating Speech Large Language Models},
  author={Sanket Shah, Kavya Ranjan Saxena, Kancharana Manideep Bharadwaj, Sharath Adavanne, Nagaraj Adiga},
  booktitle={Proc. ICASSP},
  year={2025},
}
```

## Contact
Contributions are welcome! If you have any improvements or suggestions, feel free to submit a pull request on GitHub.