# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """GLM-TTS End-to-End Offline Inference Example. GLM-TTS is a two-stage TTS system: - Stage 0 (AR): Llama-based model generates speech tokens from text - Stage 1 (DiT): Flow matching model converts speech tokens to audio Usage: # Sync two-stage (default) python examples/offline_inference/text_to_speech/glm_tts/end2end.py \ --model /path/to/GLM-TTS \ --text "你好，这是一个语音合成测试。" \ --ref-audio /path/to/reference.wav \ --ref-text "参考音频的转录文本。" \ --output-dir ./output # Async chunk mode (streaming DiT) python examples/offline_inference/text_to_speech/glm_tts/end2end.py \ --model /path/to/GLM-TTS --async-chunk \ --text "你好，这是一个语音合成测试。" \ --ref-audio /path/to/reference.wav \ --ref-text "参考音频的转录文本。" \ --output-dir ./output """ import base64 import io import logging import os import tempfile import time from typing import Any from urllib.request import urlopen import numpy as np import soundfile as sf import torch import yaml os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" from vllm.utils.argparse_utils import FlexibleArgumentParser from vllm_omni import Omni from vllm_omni.model_executor.models.glm_tts.glm_tts import build_glm_tts_prefill_metadata logger = logging.getLogger(__name__) DEFAULT_DEPLOY_CONFIG = os.path.join( os.path.dirname(__file__), "..", "..", "..", "..", "vllm_omni", "deploy", "glm_tts.yaml", ) SAMPLE_RATE = 24000 def _load_ref_audio(ref_audio: str) -> tuple[torch.Tensor, int]: """Load reference audio from file path, URL, or data URI.""" if ref_audio.startswith(("http://", "https://")): with urlopen(ref_audio, timeout=60) as response: audio_obj: Any = io.BytesIO(response.read()) elif ref_audio.startswith("data:"): _, _, encoded = ref_audio.partition(",") audio_obj = io.BytesIO(base64.b64decode(encoded)) else: audio_obj = ref_audio wav_np, sr = sf.read(audio_obj, dtype="float32") if wav_np.ndim > 1: wav_np = wav_np.mean(axis=1) return torch.from_numpy(wav_np), int(sr) def _concat_audio(audio_val: Any) -> np.ndarray: """Concatenate audio tensors from multimodal output.""" if isinstance(audio_val, list): tensors = [torch.as_tensor(t).float().reshape(-1) for t in audio_val if t is not None] if not tensors: return np.zeros((0,), dtype=np.float32) return torch.cat(tensors, dim=-1).cpu().numpy().astype(np.float32, copy=False) if isinstance(audio_val, torch.Tensor): return audio_val.float().cpu().numpy().reshape(-1) return np.asarray(audio_val, dtype=np.float32).reshape(-1) def _extract_sample_rate(audio_mm: dict) -> int: """Extract sample rate from multimodal output dict.""" sr_raw = audio_mm.get("sr", SAMPLE_RATE) if isinstance(sr_raw, list): sr_raw = sr_raw[-1] if sr_raw else SAMPLE_RATE if hasattr(sr_raw, "item"): return int(sr_raw.item()) return int(sr_raw) def _modify_deploy_config(base_path: str, async_chunk: bool) -> str: """Build deploy config with explicit sync/async mode and eager execution. Mirrors the logic in ``tests/e2e/offline_inference/test_glm_tts_expansion.py`` (``_get_deploy_config``) so that example runs match CI behavior. """ with open(base_path) as f: cfg = yaml.safe_load(f) cfg["async_chunk"] = async_chunk for stage in cfg.get("stages", []): stage["enforce_eager"] = True if stage.get("stage_id") == 0: stage["async_scheduling"] = bool(async_chunk) tmp = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False, prefix="glm_tts_") yaml.dump(cfg, tmp) tmp.close() return tmp.name def main(args): """Run offline GLM-TTS inference.""" os.makedirs(args.output_dir, exist_ok=True) base_deploy_config = args.deploy_config or DEFAULT_DEPLOY_CONFIG deploy_config_path = _modify_deploy_config(base_deploy_config, args.async_chunk) ref_audio_wav, ref_audio_sr = _load_ref_audio(args.ref_audio) if not args.ref_text: raise ValueError("GLM-TTS requires --ref-audio and --ref-text.") inputs = [ { "prompt": args.text, "multi_modal_data": { "audio": (ref_audio_wav.float().cpu().numpy(), ref_audio_sr), }, "modalities": ["audio"], "mm_processor_kwargs": {"prompt_text": args.ref_text}, "additional_information": build_glm_tts_prefill_metadata( args.model, args.text, args.ref_text, ), } ] omni = Omni( model=args.model, deploy_config=deploy_config_path, log_stats=args.log_stats, stage_init_timeout=args.stage_init_timeout, ) t_start = time.perf_counter() outputs = omni.generate(inputs) elapsed = (time.perf_counter() - t_start) * 1000 assert outputs, "No outputs returned" audio_mm = outputs[0].multimodal_output assert "audio" in audio_mm, "No audio output found" audio = _concat_audio(audio_mm["audio"]) sr = _extract_sample_rate(audio_mm) out_path = os.path.join(args.output_dir, "output.wav") sf.write(out_path, audio, samplerate=sr, format="WAV") logger.info("Saved %s (%.2fs @ %dHz)", out_path, len(audio) / sr, sr) logger.info("Total inference: %.1f ms", elapsed) def parse_args(): parser = FlexibleArgumentParser(description="GLM-TTS Text-to-Speech Example") parser.add_argument("--model", type=str, required=True, help="Model path") parser.add_argument("--text", type=str, default="你好，这是一个语音合成测试。") parser.add_argument("--output-dir", type=str, default="./output") parser.add_argument("--ref-audio", type=str, required=True, help="Reference WAV path/URL") parser.add_argument("--ref-text", type=str, required=True, help="Transcript of ref audio") parser.add_argument("--deploy-config", type=str, default=None) parser.add_argument( "--async-chunk", action="store_true", default=False, help="Enable async_chunk mode (streaming DiT). Default: sync two-stage.", ) parser.add_argument("--log-stats", action="store_true") parser.add_argument("--stage-init-timeout", type=int, default=600) return parser.parse_args() if __name__ == "__main__": logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) main(parse_args())