Instructions to use aaiiaxom/bodo-tts-aaii-fs2-mb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use aaiiaxom/bodo-tts-aaii-fs2-mb with ESPnet:
from espnet2.bin.tts_inference import Text2Speech model = Text2Speech.from_pretrained("aaiiaxom/bodo-tts-aaii-fs2-mb") speech, *_ = model("text to generate speech from") - Notebooks
- Google Colab
- Kaggle
Upload 8 files
Browse files- README.md +59 -0
- config.json +5 -0
- decoder.onnx +3 -0
- encoder.onnx +3 -0
- metadata.json +11 -0
- token_list.json +81 -0
- variance_predictor.onnx +3 -0
- vocoder.onnx +3 -0
README.md
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---
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license: apache-2.0
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---
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---
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language:
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- brx
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tags:
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- text-to-speech
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- bodo
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- tts
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- espnet
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- fastspeech2
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- multiband-melgan
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license: apache-2.0
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library_name: espnet
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pipeline_tag: text-to-speech
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---
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# AAII - Bodo Neural TTS Model
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This model has been trained on the female voice subset of the **[AI4Bharat Rasa Bodo TTS dataset](https://huggingface.co/datasets/ai4bharat/Rasa)** and is built using an **ESPnet-based FastSpeech2 + Multi-band MelGAN** TTS pipeline.
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---
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## Model Overview
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The TTS system is based on:
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- **Acoustic model:** FastSpeech2
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- **Vocoder:** Multi-band MelGAN
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- **Toolkit:** ESPnet
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- **Language:** Bodo (`brx`)
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- **Training data:** [AI4Bharat Rasa Bodo TTS Dataset](https://huggingface.co/datasets/ai4bharat/Rasa) (Female Voice)
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- **Input:** Bodo Devanagari text
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- **Output:** Bodo speech waveform
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The goal of this model is to provide a lightweight and usable Bodo neural TTS system for browser-based and community-focused language technology applications.
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---
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## Applications Built Using This Model
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Using this Bodo model, we have developed two free public-facing Bodo reading tools.
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### 1. Bodo Text-to-Speech Web App
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A browser-based Bodo TTS web application that can read Bodo Devanagari text directly from webpages, pasted text, or uploaded photos/PDFs (via on-device OCR).
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**Key features:**
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- Reads Bodo Devanagari text
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- Supports webpage URL input
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- Supports direct text input
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- Supports Photo and PDF text extraction (OCR)
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- Runs speech synthesis inside the browser using WebAssembly / WebGPU
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- No server-side API call required for synthesis
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- Model is cached in the browser after first load
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- Can work offline after the model is loaded
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- Useful for accessibility, reading support, education, and Bodo digital content consumption
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---
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## Acknowledgement
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We express our sincere gratitude to **[AI4Bharat](https://ai4bharat.iitm.ac.in/)** for creating and open-sourcing the **[Rasa Dataset](https://huggingface.co/datasets/ai4bharat/Rasa)**. The training of this model relies on the female Bodo voice subset provided in the Rasa corpus.
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---
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## Disclaimer
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This is an early version of the Bodo TTS model. The model is designed to generate clear and natural Bodo speech, but pronunciation, prosody, and expressiveness may still improve with further data tuning and future model updates.
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config.json
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{
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"model_type": "bodo-tts",
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"language": "brx",
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"task": "text-to-speech"
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}
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decoder.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:5767d1c0fcabe5fd7c19b61b560e53ab17d4b30616e75c3902d9c403442051d0
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size 78723849
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encoder.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:ca93e307b0058bba4789dd532c2f1673647a75bffbe1c2d2c5b3322e12c36c73
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size 73966547
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metadata.json
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{
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"idim": 79,
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"odim": 80,
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"adim": 384,
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"eos": 78,
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"padding_idx": 0,
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"reduction_factor": 1,
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"fs": 16000,
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"export_type": "split_v2",
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"vocab_size": 79
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}
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token_list.json
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}
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variance_predictor.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:7fc7c6ea7a0c068ebfee175587c454aaf64d37bed6d307112346af77470318ac
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size 11188900
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vocoder.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:224a36a869d48b7c7dbe4353d31fec20b153a8806b992f11ec0b56a8035f2db8
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size 10164577
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