# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Offline inference example for MOSS-TTS-Nano via vLLM-Omni. Single-stage pipeline: the 0.1B AR LM and MOSS-Audio-Tokenizer-Nano codec both run inside one generation stage. Output is 48 kHz stereo WAV. Supports: - Built-in voice presets (--voice) - Custom voice cloning via reference audio (--prompt-audio) - Batch synthesis of multiple texts - Reproducible output via --seed Usage examples: # Built-in voice python end2end.py --text "Hello from MOSS-TTS-Nano." # Voice clone with reference audio python end2end.py --text "Hello!" --prompt-audio /path/to/ref.wav # Batch with different voices python end2end.py --batch --output-dir /tmp/moss_output """ from __future__ import annotations import os from pathlib import Path import soundfile as sf import torch from vllm import SamplingParams from vllm.utils.argparse_utils import FlexibleArgumentParser # Prevent multiprocessing from re-importing CUDA in the wrong context. os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn") from vllm_omni import Omni # noqa: E402 MODEL = "OpenMOSS-Team/MOSS-TTS-Nano" BATCH_SAMPLES = [ {"text": "Hello, this is a test of MOSS-TTS-Nano.", "voice": "Ava", "label": "en_hello"}, {"text": "你好，这是 MOSS-TTS-Nano 的语音合成测试。", "voice": "Junhao", "label": "zh_hello"}, {"text": "Bonjour, ceci est un test de synthèse vocale.", "voice": "Bella", "label": "fr_hello"}, ] def build_request( text: str, voice: str = "Junhao", mode: str = "voice_clone", prompt_audio_path: str | None = None, prompt_text: str | None = None, max_new_frames: int = 375, seed: int | None = None, audio_temperature: float = 0.8, audio_top_k: int = 25, audio_top_p: float = 0.95, text_temperature: float = 1.0, ) -> dict: """Build an Omni request payload for MOSS-TTS-Nano.""" additional: dict = { "text": [text], "voice": [voice], "mode": [mode], "max_new_frames": [max_new_frames], "audio_temperature": [audio_temperature], "audio_top_k": [audio_top_k], "audio_top_p": [audio_top_p], "text_temperature": [text_temperature], } if prompt_audio_path: additional["prompt_audio_path"] = [str(prompt_audio_path)] if prompt_text: additional["prompt_text"] = [prompt_text] if seed is not None: additional["seed"] = [seed] return { "prompt": "<|im_start|>assistant\n", # minimal placeholder prompt "additional_information": additional, } def save_audio(waveform: torch.Tensor, path: str, sample_rate: int = 48000) -> None: audio_np = waveform.float().numpy() # Reshape stereo: inference_stream yields interleaved [samples*2]; reshape to [samples, 2] if audio_np.ndim == 1 and audio_np.shape[0] % 2 == 0: audio_np = audio_np.reshape(-1, 2) sf.write(path, audio_np, sample_rate) print(f" Saved {path} ({audio_np.shape}, {sample_rate} Hz)") def main(args) -> None: omni = Omni( model=MODEL, deploy_config=args.deploy_config, stage_init_timeout=args.stage_init_timeout, ) sampling_params = SamplingParams( temperature=1.0, top_p=1.0, top_k=50, max_tokens=4096, seed=args.seed if args.seed is not None else 42, detokenize=False, ) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) if args.batch: print(f"Running batch synthesis ({len(BATCH_SAMPLES)} samples)...") inputs = [build_request(s["text"], voice=s["voice"], seed=args.seed) for s in BATCH_SAMPLES] params_list = [sampling_params] * len(inputs) else: print(f"Synthesizing: {args.text!r}") inputs = build_request( text=args.text, voice=args.voice, mode=args.mode, prompt_audio_path=args.prompt_audio, prompt_text=args.prompt_text, max_new_frames=args.max_new_frames, seed=args.seed, audio_temperature=args.audio_temperature, audio_top_k=args.audio_top_k, audio_top_p=args.audio_top_p, text_temperature=args.text_temperature, ) params_list = sampling_params for stage_outputs in omni.generate(inputs, params_list): for i, req_output in enumerate(stage_outputs.request_output): for j, out in enumerate(req_output.outputs): mm = out.multimodal_output if mm is None: print(f" [req {i}] No audio output.") continue audio = mm.get("audio") sr_tensor = mm.get("sr") if audio is None: print(f" [req {i}] No waveform in multimodal_output.") continue sr = int(sr_tensor.item()) if sr_tensor is not None else 48000 label = BATCH_SAMPLES[i]["label"] if args.batch else f"output_{i}_{j}" out_path = str(output_dir / f"{label}.wav") save_audio(audio.cpu(), out_path, sr) print("Done.") def parse_args(): parser = FlexibleArgumentParser(description="MOSS-TTS-Nano offline inference") parser.add_argument("--text", default="Hello, this is MOSS-TTS-Nano speaking.", help="Text to synthesize.") parser.add_argument("--voice", default="Junhao", help="Built-in voice preset name.") parser.add_argument("--mode", default="voice_clone", choices=["voice_clone", "continuation"]) parser.add_argument("--prompt-audio", default=None, help="Path to reference audio for voice cloning.") parser.add_argument("--prompt-text", default=None, help="Reference transcript (continuation mode).") parser.add_argument("--max-new-frames", type=int, default=375, help="Max AR frames (~14s at default).") parser.add_argument("--seed", type=int, default=None, help="Random seed.") parser.add_argument("--audio-temperature", type=float, default=0.8) parser.add_argument("--audio-top-k", type=int, default=25) parser.add_argument("--audio-top-p", type=float, default=0.95) parser.add_argument("--text-temperature", type=float, default=1.0) parser.add_argument("--batch", action="store_true", help="Run built-in batch of diverse samples.") parser.add_argument("--output-dir", default="/tmp/moss_tts_nano_output", help="Directory for WAV outputs.") parser.add_argument( "--deploy-config", default=None, help="Path to a deploy YAML; leave unset to auto-load vllm_omni/deploy/moss_tts_nano.yaml.", ) parser.add_argument("--stage-init-timeout", type=int, default=120) return parser.parse_args() if __name__ == "__main__": main(parse_args())