chatterbox-telugu

Chatterbox-Multilingual — Telugu (te) fine-tune

A Telugu fine-tune of Resemble AI's ResembleAI/chatterbox (Chatterbox-Multilingual, checkpoint t3_mtl23ls_v2.safetensors). It adds Telugu — including code-switched (Telugu+English, e.g. "Tenglish") speech — while keeping the base model's English and 23-language ability and its zero-shot voice cloning. This is a derivative of Chatterbox; see the model tree above.

Key details

• Backbone: Chatterbox T3 (~0.5B Llama) text→speech-token model, warm-started from the released 23-language checkpoint (English/cross-lingual ability preserved).
• Adaptation: LoRA (rank 16, merged into the weights) + retrained text embedding/head.
• Tokenizer: the multilingual grapheme tokenizer extended with the Telugu script and a [te] language tag (vocab 2521).
• Training: 10,000 steps on ~34.5k Telugu clips (see Training data).
• Voice cloning: zero-shot from a 6–15s reference clip, same as base.
• Watermark: every output carries Resemble AI's PerTh neural watermark.

What was kept vs. changed

Only the text side is Telugu-specific; the language-agnostic acoustic stack is reused unchanged from the base model.

The bundled s3gen.pt, ve.pt, conds.pt, and Cangjie5_TC.json are Resemble AI's original files, redistributed unchanged under their MIT license (see License & attribution).

Training data

Trained only on CC-BY-4.0 Telugu speech (attribution below). Only model weights are published here — no raw dataset audio is redistributed.

OpenSLR SLR66 (CC-BY-SA-4.0) was deliberately excluded so the training mix stays CC-BY-4.0 and this model can be released under a plain CC-BY-4.0 license (no ShareAlike).

Usage

import torchaudio as ta
from huggingface_hub import snapshot_download
from chatterbox.mtl_tts import ChatterboxMultilingualTTS

ckpt = snapshot_download("shankarpandala/chatterbox-telugu")
model = ChatterboxMultilingualTTS.from_local(ckpt, device="mps", t3_model="t3_mtl_te.safetensors")

# Pure Telugu
wav = model.generate(
    "నమస్కారం, ఈ రోజు ఎలా ఉన్నారు?",
    language_id="te",
    audio_prompt_path="your_reference.wav",   # 6-15s clip of the target voice
)
ta.save("out.wav", wav, model.sr)

# Code-switched (Telugu + English)
wav = model.generate("నేను office కి వెళ్తున్నాను, evening meeting ఉంది.", language_id="te", audio_prompt_path="your_reference.wav")
ta.save("out_codeswitch.wav", wav, model.sr)

device="cuda" on a GPU, "mps" on Apple Silicon, "cpu" otherwise.

Watermarking

Like the base model, every audio file generated carries Resemble AI's PerTh neural watermark — imperceptible marks that survive MP3 compression and common edits — for responsible-AI provenance.

Acknowledgements

• Resemble AI for Chatterbox (which itself builds on CosyVoice, HiFT-GAN, and Llama 3).
• AI4Bharat for IndicVoices-R and the Google FLEURS team for the Telugu data.

License & attribution

• This model: released under CC-BY-4.0 (attribution required; commercial use and derivatives permitted). This carries forward the CC-BY-4.0 attribution required by the training data.

• Base model: ResembleAI/chatterbox — MIT © Resemble AI. The redistributed acoustic files (s3gen.pt, ve.pt, conds.pt, Cangjie5_TC.json) remain under that MIT license:

  Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files... The above copyright notice and this permission notice shall be included in all copies. (MIT, © 2025 Resemble AI — full text: https://github.com/resemble-ai/chatterbox/blob/master/LICENSE)

• Training data: FLEURS and IndicVoices-R, both CC-BY-4.0 (cited below). CC-BY-SA SLR66 was excluded to keep this release CC-BY-4.0.

Citations

@misc{chatterboxtts2025,
  author       = {{Resemble AI}},
  title        = {{Chatterbox-TTS}},
  year         = {2025},
  howpublished = {\url{https://github.com/resemble-ai/chatterbox}},
  note         = {GitHub repository},
}

@inproceedings{conneau2023fleurs,
  title     = {{FLEURS}: Few-Shot Learning Evaluation of Universal Representations of Speech},
  author    = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and
               Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and
               Bapna, Ankur},
  booktitle = {2022 IEEE Spoken Language Technology Workshop (SLT)},
  pages     = {798--805},
  year      = {2023},
  doi       = {10.1109/SLT54892.2023.10023141},
  note      = {arXiv:2205.12446},
}

@inproceedings{sankar2024indicvoicesr,
  title     = {{IndicVoices-R}: Unlocking a Massive Multilingual Multi-speaker Speech
               Corpus for Scaling Indian {TTS}},
  author    = {Sankar, Ashwin and Anand, Srija and Varadhan, Praveen Srinivasa and
               Thomas, Sherry and Singal, Mehak and Kumar, Shridhar and Mehendale, Deovrat and
               Krishana, Aditi and Raju, Giri and Khapra, Mitesh M.},
  booktitle = {Advances in Neural Information Processing Systems 38 (NeurIPS 2024)},
  year      = {2024},
  url       = {http://papers.nips.cc/paper_files/paper/2024/hash/7dfcaf4512bbf2a807a783b90afb6c09-Abstract-Datasets_and_Benchmarks_Track.html},
}

Disclaimer & limitations

Focused on Telugu and Telugu+English code-switching; it does not match Chatterbox's English SOTA quality. Use responsibly — do not use it to impersonate real people without consent or to produce misleading content. All outputs are PerTh-watermarked.