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Dhwani - Indic Speech To Text Translation

Introduction

Dhwani enables Speech-to-Text Translation for Indic Languages. It supports translation from Indic Language (X) → English and vice-versa.

Model Summary

Current model trained using SALMONN architecture.

PreTraining

• Speech Encoder: Utilizes the Whisper model's speech encoder to process speech inputs.
• Audio Encoder: Employs the BEATs audio encoder for non-speech audio inputs, such as environmental sounds and music.
• Connection Module: Uses the Window-Level Query Transformer (Q-Former) to bridge the audio encoders and the Large Language Model (LLM).
• Large Language Model (LLM): The Krutrim LLM receives the processed tokens, handling audio-derived information.
• Adaptation Mechanism: Low-Rank Adaptation (LoRA) is applied to fine-tune the LLM to align the audio inputs with the model's output.

PostTraining

To adapt Q-former and LoRA weights, we used techniques mentioned in the paper IndicST. Along with the IndicST translation dataset, we also used in-house-collected translation data to further improve the performance of translation results.

Model Downloads

1. Download whisper large v2.
2. Download Fine-tuned BEATs_iter3+ (AS2M) (cpt2).
3. Download krutrim llm.
4. Download ckpt.

Evaluation Results

En → Indic (X) BLEU Scores:

Language Pair	BLEU Score
en → hin	57.7
en → guj	44.3
en → mar	43.3
en → ben	49.0
en → tam	47.0
en → tel	40.8
en → mal	39.0
en → kan	47.0
Average	46.0

Indic (X) → En BLEU Scores:

Language Pair	BLEU Score
hin → en	35.7
guj → en	34.6
mar → en	33.2
ben → en	19.2
tam → en	25.4
tel → en	17.4
mal → en	38.9
kan → en	28.0
Average	30.0

API Platform

Visit Dhwani Online to access the model via the web interface.

How to inference in CLI

1. conda create -n dhwani_env python=3.9.17
2. Our environment: The python version is 3.9.17, and other required packages can be installed with the following command: pip install -r requirements.txt.
3. add whisper large v2 to whisper_path.
4. add Fine-tuned BEATs_iter3+ (AS2M) (cpt2) to beats_path.
5. add krutrim llm to llama_path.
6. add krutrim ckpt path to ckpt.
7. Running with python3 cli_inference.py --cfg-path configs/decode_config.yaml in A100-SXM-80GB. Now you can input wav_path and prompt. Enjoy yourself !

How to infer the model

1. Same as How to inference in CLI: 1-4.
2. add krutrim ckpt path to ckpt.
3. Running with python3 infer.py --cfg-path configs/decode_config.yaml in A100-SXM-80GB.

License

This code repository and the model weights are licensed under the Krutrim Community License.

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.