[Model] Add Gepard-1.0 native-AR FSQ/NanoCodec TTS (offline inference) (#5666)

Signed-off-by: Mengjie Zhao <zmj0129@gmail.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>

examples/offline_inference/text_to_speech/README.md

@@ -16,6 +16,7 @@ list of supported architectures across all modalities, see
 |---|---|---|---|---|---|---|
 | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | 2 (talker + code2wav) | ✓ | ✓ | — | 24 kHz |
 | Fish Speech S2 Pro | `fishaudio/s2-pro` | dual-AR | ✓ | ✓ | — | 44.1 kHz |
+| Gepard-1.0 | `nineninesix/gepard-1.0` | single (native AR + NanoCodec) | — (zero-shot; cloning WIP) | — (serving WIP) | zero-shot | 22.05 kHz |
 | GLM-TTS | `zai-org/GLM-TTS` | 2 (AR + DiT) | ✓ (required) | ✓ | — | 24 kHz |
 | Ming-omni-tts | `inclusionAI/Ming-omni-tts-0.5B` | 2 (AR + audio VAE) | ✓ | ✓ | style / IP / dialect / TTA / podcast | 44.1 kHz |
 | Ming-flash-omni-TTS | `Jonathan1909/Ming-flash-omni-2.0` | single (talker only) | — (caption-controlled) | — | style / IP / basic captions | 44.1 kHz |
@@ -395,6 +396,58 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
 
 ---
 
+## Gepard-1.0
+
+Single-stage native AR TTS at 22.05 kHz. Pipeline: `Qwen3.5 backbone → 32 FSQ codebook heads (one frame/step) → NeMo NanoCodec`. Backbone runs under vLLM paged attention; the 32-head sampling + learned embedding feedback ride the native-AR runner hooks. `enforce_eager` (CUDA graph is a perf follow-up).
+
+### Prerequisites
+The NanoCodec decoder needs NeMo (NVIDIA Open Model License), installed separately:
+
+```bash
+uv pip install "nemo_toolkit[tts]==2.7.3" \
+    --overrides <(printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0")
+```
+
+The overrides are what make that command work. `nemo_toolkit[tts]` declares
+`transformers~=4.57.0`, so a plain install downgrades the version
+`requirements/common.txt` pins and takes `huggingface-hub` back to 0.x with it — after
+which `import vllm` fails. Both packages are named here rather than left to resolve on
+their own: transformers 5.x happens to require `huggingface-hub` 1.x, but relying on
+that would make the second repair an accident of the first. `--constraint` cannot be
+used instead, since it narrows a version range rather than overriding a dependency a
+package declares, and the two sets never intersect. NeMo 2.7.3 decodes correctly
+against transformers 5.x, so nothing is lost by holding it there.
+
+(The same clash is why this is documented here rather than declared in
+`pyproject.toml`: an extra resolves together with the base dependencies, so
+`vllm-omni[gepard]` would be unsatisfiable rather than opt-in. SoulX-Singer English
+SVS documents `nemo_toolkit[asr]` the same way.)
+
+On a host whose CUDA toolkit cannot build kernels for the GPU — no `nvcc`/`ninja`, or a
+consumer Blackwell (`sm_120`) card — also set `VLLM_USE_FLASHINFER_SAMPLER=0`. vLLM's
+sampler JIT-compiles through FlashInfer on first use, and where that build fails the engine
+dies inside `profile_run` long after every import has succeeded, which reads as a model
+failure rather than an environment one. The PyTorch sampler it falls back to costs nothing
+here.
+
+### Quick start (zero-shot, default voice)
+```bash
+python examples/offline_inference/text_to_speech/gepard/end2end.py \
+    --text "Hello, this is a Gepard demo."
+```
+
+### Voice cloning
+Not yet — PR1 is zero-shot only (the learned `null_prefix` default voice). Reference-audio cloning (`ref_compressor` speaker prefix) is a follow-up PR.
+
+### Notes
+- Output: 22.05 kHz mono WAV.
+- First run downloads two checkpoints, not one: the model itself, and the NanoCodec decoder it names in `codec_id` (`nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps` by default). Both are fetched automatically; expect the first startup to be correspondingly slower.
+- Offline only for now; the `/v1/audio/speech` adapter is a follow-up PR.
+- Deploy config: `vllm_omni/deploy/gepard.yaml` (the example's default; copy it and pass `--deploy-config` to change it).
+- Generation length and reproducibility are stage settings, not script flags. One output token is one audio frame, so `max_tokens` in the YAML is the frame budget; `seed` makes the in-model 32-head sampling reproducible. The script deliberately passes no `SamplingParams`: one supplied by a caller replaces the stage defaults wholesale rather than merging, which would drop the pipeline's stop token and run every request to `max_tokens`.
+- Text length: short texts are repeated internally to match the training layout (the checkpoint's `text_repetition` block); the upper bound is the stage's `max_model_len`, enforced by the engine. Empty text is rejected rather than voiced.
+- `VLLM_GEPARD_GREEDY=1` swaps the 32-head Gumbel-max sampling for argmax, for reproducible comparisons that do not depend on a seed.
+
 ## VoxCPM2
 
 Single-stage native AR TTS at 48 kHz. Pipeline: `feat_encoder → MiniCPM4 → FSQ → residual_lm → LocDiT → AudioVAE`.

examples/offline_inference/text_to_speech/gepard/end2end.py

@@ -0,0 +1,139 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline inference example for Gepard-1.0 TTS via vLLM-Omni.
+
+Single-stage native-AR pipeline: a Qwen3.5 backbone samples one 32-code FSQ
+frame per step; the NeMo NanoCodec decodes the frames to a 22.05 kHz mono
+waveform. Zero-shot: the default learned voice, no reference audio.
+
+Generation length and seed are stage settings rather than flags here. One
+output token is one audio frame, so ``max_tokens`` in the deploy YAML is the
+frame budget; pass a copy of the YAML via ``--deploy-config`` to change it.
+
+Usage:
+  python end2end.py --text "Hello, this is Gepard speaking."
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import soundfile as sf
+import torch
+
+import vllm_omni
+from vllm_omni.utils.tracking_parser import TrackingArgumentParser
+
+os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+
+from vllm_omni import Omni  # noqa: E402
+
+MODEL = "nineninesix/gepard-1.0"
+SAMPLE_RATE = 22050
+# Absolute: the loader hands the path straight to OmegaConf, which resolves a
+# bare name against the current working directory, not the package.
+DEFAULT_DEPLOY_CONFIG = str(Path(vllm_omni.__file__).resolve().parent / "deploy" / "gepard.yaml")
+
+
+def build_request(text: str) -> dict:
+    """Build an Omni request payload for Gepard (zero-shot, no ref audio).
+
+    The model only consumes the [speaker slots, SOT, text, EOT, SOS] layout, so
+    a bare text prompt would have no speaker slots and no SOS.
+    """
+    from transformers import AutoTokenizer
+
+    from vllm_omni.model_executor.models.gepard.configuration_gepard import GepardConfig
+    from vllm_omni.model_executor.models.gepard.prompt import build_gepard_prompt_ids
+
+    # From the checkpoint's own sidecar, which is what the worker builds its
+    # config from too. A layout that disagrees with the trained one does not
+    # fail, it just degrades the speech.
+    cfg = GepardConfig.from_checkpoint(MODEL)
+    tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
+    prompt_token_ids = build_gepard_prompt_ids(
+        tokenizer(text, add_special_tokens=False)["input_ids"],
+        config=cfg,
+    )
+    return {
+        "prompt_token_ids": prompt_token_ids,
+        "additional_information": {"text": [text]},
+    }
+
+
+def save_audio(waveform: torch.Tensor, path: str, sample_rate: int = SAMPLE_RATE) -> None:
+    sf.write(path, waveform.float().numpy(), sample_rate)
+    seconds = waveform.numel() / sample_rate
+    print(
+        f"  Saved {path} ({tuple(waveform.shape)}, {sample_rate} Hz, "
+        f"{seconds:.2f}s, peak {float(waveform.abs().max()):.3f})"
+    )
+
+
+def main(args) -> None:
+    omni = Omni(
+        model=MODEL,
+        deploy_config=args.deploy_config,
+        stage_init_timeout=args.stage_init_timeout,
+        init_timeout=args.init_timeout,
+    )
+
+    # No explicit SamplingParams: one supplied here would replace the stage
+    # defaults rather than merge over them, dropping the pipeline's stop token.
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Synthesizing: {args.text!r}")
+    inputs = build_request(text=args.text)
+
+    for i, stage_outputs in enumerate(omni.generate(inputs)):
+        for j, out in enumerate(stage_outputs.outputs):
+            mm = out.multimodal_output
+            if mm is None:
+                print(f"  [req {i}] No audio output.")
+                continue
+            # The consolidation path renames "model_outputs" to "audio".
+            audio = mm.get("audio")
+            if audio is None:
+                audio = mm.get("model_outputs")
+            if isinstance(audio, list):
+                audio = audio[0] if len(audio) else None
+            if audio is None:
+                print(f"  [req {i}] No waveform in multimodal_output (keys: {list(mm)}).")
+                continue
+            sr_tensor = mm.get("sr")
+            sr = int(sr_tensor.item()) if hasattr(sr_tensor, "item") else SAMPLE_RATE
+            save_audio(audio.cpu(), str(output_dir / f"output_{i}_{j}.wav"), sr)
+
+    print("Done.")
+
+
+def parse_args():
+    parser = TrackingArgumentParser(description="Gepard-1.0 offline TTS inference (zero-shot)")
+    parser.add_argument("--text", default="Hello, this is Gepard speaking.", help="Text to synthesize.")
+    parser.add_argument(
+        "--output-dir",
+        default=os.path.join(
+            os.environ.get("XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache")),
+            "gepard_output",
+        ),
+        help="Directory for WAV outputs (default: ~/.cache/gepard_output).",
+    )
+    parser.add_argument(
+        "--deploy-config",
+        default=DEFAULT_DEPLOY_CONFIG,
+        help=(
+            "Path to the deploy YAML (default: the packaged vllm_omni/deploy/gepard.yaml). This must "
+            "stay set: without the YAML's `pipeline: gepard` pin the architectures fallback routes "
+            "Qwen3_5ForCausalLM to the diffusion registry. Copy it to change max_tokens or seed."
+        ),
+    )
+    # The upstream defaults are too tight for a cold start.
+    parser.add_argument("--stage-init-timeout", type=int, default=900)
+    parser.add_argument("--init-timeout", type=int, default=1800)
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    main(parse_args())