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LICENSE.md

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+Copyright 2026 Ashirbad Sahu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

MODEL_CARD.md

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+---
+license: mit
+library_name: "other"
+base_model:
+- Qwen/Qwen3.6-27B
+- k2-fsa/OmniVoice
+pipeline_tag: "text-to-speech"
+tags:
+- banking
+- faq
+- multilingual
+- hindi
+- english
+- speech
+- text-to-speech
+- synthetic-data
+- conversational-ai
+model_name: "Banking FAQ Hindi-English Speech Dataset"
+---
+
+# Model Card: Banking FAQ Hindi-English Speech Dataset
+
+## Model summary
+
+This dataset is a multilingual banking FAQ corpus created by translating English FAQ content into Hindi and generating spoken audio for the translated text. It is designed to support research in multilingual conversational AI, synthetic speech generation, and domain-specific customer support systems.
+
+## Description
+
+The source corpus comes from a Kaggle banking FAQ dataset for chatbot training:
+
+- https://www.kaggle.com/datasets/rudrakumargupta/banking-faq-dataset-for-chatbot-training
+
+The original text was in English. To create a Hindi-language version, the dataset was translated using `Qwen/Qwen3.6-27B`, run locally with `unsloth/Qwen3.6-27B-GGUF` via [llama.cpp](https://github.com/ggml-org/llama.cpp).
+
+After translation, the Hindi text was converted to audio using `k2-fsa/OmniVoice` through [omnivoice.cpp](https://github.com/ServeurpersoCom/omnivoice.cpp). The generated speech has a sample rate of 24 kHz.
+
+## Intended use
+
+This dataset is intended for:
+
+- research in multilingual banking QA
+- customer support and FAQ experimentation
+- TTS and speech synthesis evaluation
+- ASR training on domain-specific spoken banking content
+- conversational AI prototyping for Hindi-English systems
+
+## Out-of-scope use
+
+This dataset should not be used for:
+
+- deceptive voice cloning or impersonation
+- harmful automated customer interactions
+- production customer support without human review
+- safety-critical financial decisions without oversight
+
+## Dataset structure
+
+The dataset contains banking-related FAQ text and associated synthetic speech generated from the translated Hindi content. The primary language pair is English and Hindi, with audio generated in the spoken Hindi domain.
+
+## Training and generation pipeline
+
+1. Collect English FAQ data from the Kaggle source dataset.
+2. Translate questions and answers into Hindi using `Qwen/Qwen3.6-27B`.
+3. Run the model locally using `unsloth/Qwen3.6-27B-GGUF` and `llama.cpp`.
+4. Convert the translated text into speech using `k2-fsa/OmniVoice` via `omnivoice.cpp`.
+5. Store the generated audio at 24 kHz sample rate.
+
+## Limitations
+
+- Machine translation may introduce wording errors, unnatural phrasing, or domain mismatches.
+- Synthetic speech may have pronunciation, prosody, or expressiveness limitations compared to human speech.
+- The model may not reflect real-world speaker variation or emotional nuance.
+- Banking terminology should be manually reviewed before using the data in sensitive or production workflows.
+
+## Bias, risks, and ethical considerations
+
+This dataset is synthetic and domain-specific. It may reflect the translation model's stylistic and lexical preferences and may not generalize equally to all banking scenarios or dialects. Use responsibly and validate results on target populations and use cases before deployment.
+
+## Licensing
+
+This project is released under the MIT License. See [LICENSE.md](LICENSE.md) for details.
+
+The source Kaggle dataset and the underlying model dependencies may have their own licenses and usage terms. Please review those terms separately before redistribution or commercial use.
+
+## Citation
+
+If you use this dataset, please cite the original source dataset and the model dependencies used in its creation.
+
+Suggested attribution:
+
+- Source dataset: Banking FAQ dataset for chatbot training (Kaggle)
+- Translation model: `Qwen/Qwen3.6-27B`
+- Local inference backend: `unsloth/Qwen3.6-27B-GGUF` with `llama.cpp`
+- Speech model: `k2-fsa/OmniVoice`
+- Speech runtime: `omnivoice.cpp`
+
+## Additional notes
+
+This project is intended as a research and prototyping resource for Hindi-English banking speech generation and multilingual customer service workflows.

README.md

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+# Banking FAQ Hindi-English Speech Dataset
+
+A multilingual banking FAQ dataset that combines an English FAQ source with Hindi translation and synthetic speech generation.
+
+## Overview
+
+This project starts from a public English banking FAQ dataset and converts it into a Hindi-English speech dataset for research and experimentation in conversational AI and multilingual speech systems.
+
+### Source dataset
+
+- Kaggle: https://www.kaggle.com/datasets/rudrakumargupta/banking-faq-dataset-for-chatbot-training
+
+### Translation pipeline
+
+The English FAQ data was translated into Hindi using `Qwen/Qwen3.6-27B`, executed locally with `unsloth/Qwen3.6-27B-GGUF` through [llama.cpp](https://github.com/ggml-org/llama.cpp).
+
+### Speech synthesis pipeline
+
+The translated text was converted to speech using `k2-fsa/OmniVoice` via [omnivoice.cpp](https://github.com/ServeurpersoCom/omnivoice.cpp).
+
+### Audio specification
+- Sample rate: 24 kHz
+
+## Data pipeline
+
+1. Collect English FAQ pairs from the Kaggle source.
+2. Translate the English questions and answers into Hindi using the local Qwen inference stack.
+3. Apply post-processing for consistency and quality checks.
+4. Synthesize spoken audio using OmniVoice.
+5. Package the bilingual text and generated speech into a usable research dataset.
+
+## License
+
+This project is licensed under the MIT License. See [LICENSE.md](LICENSE.md) for the full text.
+
+## Limitations
+
+- The Hindi data is generated by machine translation and may contain translation artifacts or wording that is not perfectly natural.
+- The synthesized speech reflects the characteristics of the TTS model and may not match human speaker variability.
+- Banking terminology should be reviewed before use in production or high-stakes systems.
+- The dataset is synthetic and should be validated against real-world customer conversations before deployment.

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