# MOSS-TTS-PNY

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tDIYCMumcW5w3JWnQ0tBGyAr-ZpaaXBB)

This repository contains a speaker-conditioned MOSS-TTS Local checkpoint,
a local copy of the MOSS audio tokenizer weights needed for codec feature
reconstruction, exported iSTFTNet3 decoder artifacts, and a portable optimized
runner. It was finetuned on several speakers from the My Little Pony: Friendship is Magic franchise, and Team Fortress 2.

**2026-06-30 update:** the checkpoint has been updated to the Gemini epoch 3
emotion/energy model. The demo and CLI expose 12 emotion IDs plus a continuous
energy value, while preserving the optimized static-local-cache/Triton runtime.

The end-to-end path is:

```text
text prompt -> MOSS-TTS local transformer -> RVQ audio codes
RVQ audio codes -> MOSS audio tokenizer quantizer + decoder[0:5]
decoder[4] 50 Hz features -> iSTFTNet3 vocoder -> 48 kHz waveform
```

The optimized runner is intended for CUDA inference and keeps the full 32 VQ
channels. It uses PyTorch for MOSS-TTS generation and decoder4 feature
extraction, then ONNX Runtime CUDA for the vocoder.


## Contents

```text
moss_tts_local_clipper_checkpoint/
  Gemini epoch 3 MOSS-TTS Local checkpoint, tokenizer, config, and custom HF code.
  It uses style_feature_dim=2: [emotion_id, energy].

moss_audio_tokenizer/
  Local OpenMOSS-Team/MOSS-Audio-Tokenizer copy. The optimized decoder4 path
  uses its quantizer and decoder[0:5] modules.

istftnet2_decoder4_50hz/
  Exported iSTFTNet2 vocoder artifacts:
  - istftnet2_decoder.onnx
  - istftnet2_decoder_cuda.ts
  - istftnet2_decoder_cpu.ts

moss_tts_torchopt_runner_bundle/
  Optimized CLI runner, Gradio demo, runtime helpers, speaker maps, and pinned
  non-Torch requirements.

run_tts_istftnet2.py
  Baseline end-to-end script using the checkpoint processor path.

run_decoder4_features.py
  Decoder-only sanity script for saved decoder[4] features shaped [frames, 768].
```


## Runtime Versions

The runner was validated with:

```text
torch 2.11.0+cu128
transformers 4.55.0
onnxruntime-gpu 1.26.0
gradio 5.49.1
```

`transformers==4.55.0` is pinned. Newer versions of transformers might work, but output gibberish.

PyTorch is not included in `moss_tts_torchopt_runner_bundle/requirements.txt`.
Install a CUDA PyTorch build that matches your GPU architecture first, then
install the non-Torch requirements. 


## Installation

From a fresh environment with CUDA PyTorch already installed:

```bash
cd moss_tts_clipper_istftnet2_release
python -m pip install -r moss_tts_torchopt_runner_bundle/requirements.txt
```

Or just ask your favorite coding agent to install it for you.




## Optimized CLI

We have an optimized runner that uses cudagraphs and other fancy stuff to speed up PyTorch generation to 1.8x realtime.
Run from the repository root:

```bash
python moss_tts_torchopt_runner_bundle/run_tts_torchopt.py \
  --text "The custom decoder is running clearly now." \
  --speaker-id 31 \
  --style-emotion-id 1 \
  --style-energy 0.8 \
  --warmup 1 \
  --repeat 1
```

Gemini emotion IDs:

```text
0 Neutral, 1 Happy, 2 Sad, 3 Angry, 4 Annoyed, 5 Fearful,
6 Surprised, 7 Calm, 8 Disgusted, 9 Whispering, 10 Nonverbal, 11 Shouting
```


Useful options:

```bash
# Disable generation monkeypatch for debugging.
python moss_tts_torchopt_runner_bundle/run_tts_torchopt.py \
  --torch-opt-mode none

# Use CPU ONNX vocoder if CUDA ORT is unavailable.
python moss_tts_torchopt_runner_bundle/run_tts_torchopt.py \
  --decoder-runtime onnx_cpu

# Use explicit paths if the runner is outside this repo layout.
python moss_tts_torchopt_runner_bundle/run_tts_torchopt.py \
  --checkpoint /path/to/moss_tts_local_clipper_checkpoint \
  --codec-path /path/to/moss_audio_tokenizer \
  --decoder-dir /path/to/istftnet2_decoder4_50hz
```


## Gradio Demo

Run:

```bash
python moss_tts_torchopt_runner_bundle/optimized_gradio_demo.py \
  --host 0.0.0.0 \
  --port 7860 \
  --share
```

The Gradio demo includes emotion and energy controls, autoplays generated audio,
and logs request parameters to stdout.



## Limitations
- **This is a beta.** Improved checkpoints will be gradually released.
- This repository is an inference bundle, not a training recipe.
- The model uses custom Python code. Load with `trust_remote_code=True` when using
  Transformers APIs directly.
- The exact licensing and redistribution terms for upstream MOSS-TTS and MOSS
  audio tokenizer components should be checked before public redistribution or
  commercial use.
- Some speakers are less represented in the dataset than others and thus might exhibit lower performance.
- You probably can't use this for commercial purposes without Hasbro's lawyers objecting.


## Contact

For any inquiries, e-mail nika109021@gmail.com.