README.md

---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-to-speech
base_model: OpenMOSS-Team/MOSS-TTS-Nano
tags:
- text-to-speech
- tts
- moss-tts-nano
- indian-english
- lora
- voice-cloning
---

# Roxi-TTS v2 — Indian-English voice (MOSS-TTS-Nano LoRA fine-tune)

A LoRA fine-tune of [**MOSS-TTS-Nano**](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano)
(0.1B, autoregressive audio-token + LLM, 48 kHz) that speaks **Indian English** as its
**default voice** — no reference clip required. Built for conversational / customer-support use.

> Successor to `IOTEverythin/voxi-tts` (Kokoro-82M, EMNS). This v2 moves to the MOSS-TTS-Nano
> family and adapts the voice with **LoRA** (full fine-tuning catastrophically forgets on a
> 0.1B model; LoRA adapts the voice while preserving the base's intelligibility).

## What it is
- **Base:** OpenMOSS-Team/MOSS-TTS-Nano (Apache-2.0) · audio tokenizer OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano (Apache-2.0)
- **Method:** LoRA (PEFT) — r=16, α=32, targets `c_attn,c_proj,fc_in,fc_out` (2.13% params), BF16, merged into a full checkpoint.
- **Output:** 48 kHz mono.

## Results (measured)
| Metric | Base MOSS | Roxi-TTS v2 (no reference) |
|---|---|---|
| Speaker similarity to target (WavLM-SV cosine) ↑ | 0.52 | **0.96** |
| Intelligibility WER (Whisper, on generated audio) ↓ | 0.26 | **0.26 (preserved)** |

The voice became the target Indian-English speaker **without** a reference clip, with intelligibility unchanged.

## Requirements
This repo's custom modeling code includes a **cross-version compatibility fix**, so it loads on
both `transformers==4.57.1` and **modern Transformers (tested 5.12.1)** — the older
`TypeError: unsupported operand type(s) for |: 'list' and 'set'` is resolved. Install:

```bash
pip install transformers torch torchaudio soundfile sentencepiece numpy huggingface_hub
# GPU (Blackwell/most NVIDIA), if needed:
#   pip install torch==2.7.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
```
`torchaudio` is required (the modeling code imports it). The `MISSING ..._lm_head.weight` line in
the load log is **cosmetic** — those heads are *tied* weights, rebound to the embeddings on load.
For exact parity with the training environment you may still pin `transformers==4.57.1`.

## Usage
```python
import torch
from transformers import AutoModelForCausalLM

device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
    "IOTEverythin/roxi-tts-v2", trust_remote_code=True, torch_dtype=torch.float32,
).to(device).eval()

res = model.inference(
    text="Welcome. Your appointment is confirmed for Monday at ten thirty in the morning.",
    output_audio_path="out.wav", mode="continuation",
    audio_tokenizer_type="moss-audio-tokenizer-nano",
    audio_tokenizer_pretrained_name_or_path="OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano",
    device=device, audio_repetition_penalty=1.1, use_kv_cache=True,
)
# res["sample_rate"] == 48000; audio written to out.wav
```
**Tips:** spell brand names phonetically (e.g. "Voz Vox") and avoid raw abbreviations ("in the
morning", not "A M"); write numbers as words. Trim trailing silence and re-run if a generation
comes out short (autoregressive models occasionally under-generate). Verified working on
`transformers==4.57.1`, `torch==2.7.0`.

## Training data & attribution
- **Dataset:** IIT-Madras **Indic TTS** — English (Indian-English) subset, via the
  `SPRINGLab/IndicTTS-English` Hugging Face mirror (studio 48 kHz read speech).
- The fine-tune was trained on a single-speaker subset of that corpus.

**Required notice (IIT-M Indic TTS End User License Agreement):**
> COPYRIGHT 2016 TTS Consortium, TDIL, Meity — represented by Hema A. Murthy & S. Umesh,
> Department of Computer Science and Engineering and Electrical Engineering, IIT Madras.
> ALL RIGHTS RESERVED.

The Indic TTS EULA grants a royalty-free, worldwide license to create and freely distribute
derivative works (such as this model). See https://www.iitm.ac.in/donlab/indictts/ for the
dataset and full license.

## Limitations & responsible use
- Trained on a single read-speech speaker; **neutral** style. Style/emotion control is **not**
  reliable yet (instruction-conditioning is wired but needs style-labeled training).
- Telephony (8 kHz) quality not separately tuned; evaluate before production.
- **Voice likeness:** this voice is derived from a real dataset speaker. Do **not** use it to
  impersonate any real person, for fraud, deception, or any unlawful/harmful purpose. Disclose
  AI-generated audio where required. The authors provide the weights "as is", without warranty.

## License
- This model's LoRA/code: **Apache-2.0** (matching the base model).
- Derived from MOSS-TTS-Nano (Apache-2.0) and IIT-M Indic TTS data (notice above retained).