[Doc][TTS] CosyVoice3 online docs + residual TTS yaml cleanup + remove VoxCPM v1 (#3748)

Signed-off-by: Yueqian Lin <linyueqian@outlook.com>
2026-05-20 15:24:06 +08:00
1 parent 1c530d984d
commit fc8486cf20
@@ -1,99 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""E2E test for VoxCPM offline inference."""
-
-from typing import Any
-
-import numpy as np
-import pytest
-import torch
-
-from tests.helpers.mark import hardware_test
-from tests.helpers.runtime import OmniRunner
-from tests.helpers.stage_config import get_deploy_config_path
-
-VOXCPM_MODEL = "OpenBMB/VoxCPM1.5"
-STAGE_CONFIG = get_deploy_config_path("voxcpm.yaml")
-SAMPLE_RATE = 24000
-
-# (model, stage_config_path) for ``@pytest.mark.parametrize("omni_runner", ..., indirect=True)``
-_OMNI_RUNNER_PARAM = (VOXCPM_MODEL, STAGE_CONFIG)
-
-pytestmark = [
-    pytest.mark.full_model,
-    pytest.mark.tts,
-    pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True),
-]
-
-
-def _build_prompt(text: str) -> dict[str, Any]:
-    return {
-        "prompt_token_ids": [1],
-        "additional_information": {
-            "text": [text],
-            "cfg_value": [2.0],
-            "inference_timesteps": [10],
-            "min_len": [2],
-            "max_new_tokens": [1024],
-        },
-    }
-
-
-def _extract_audio_tensor(multimodal_output: dict[str, Any]) -> torch.Tensor:
-    audio = multimodal_output.get("audio", multimodal_output.get("model_outputs"))
-    assert audio is not None, f"No audio output found, keys={list(multimodal_output.keys())}"
-
-    if isinstance(audio, list):
-        parts: list[torch.Tensor] = []
-        for item in audio:
-            if item is None:
-                continue
-            tensor = torch.as_tensor(item)
-            if tensor.numel() == 0:
-                continue
-            parts.append(tensor.float().cpu().reshape(-1))
-        return torch.cat(parts, dim=-1) if parts else torch.zeros((0,), dtype=torch.float32)
-
-    return torch.as_tensor(audio).float().cpu().reshape(-1)
-
-
-def _extract_final_multimodal_output(outputs) -> dict[str, Any]:
-    for item in reversed(outputs):
-        request_output = getattr(item, "request_output", None)
-        if request_output is not None:
-            multimodal_output = getattr(request_output, "multimodal_output", None)
-            if isinstance(multimodal_output, dict):
-                return multimodal_output
-            completions = getattr(request_output, "outputs", None) or []
-            for completion in completions:
-                multimodal_output = getattr(completion, "multimodal_output", None)
-                if isinstance(multimodal_output, dict):
-                    return multimodal_output
-
-        multimodal_output = getattr(item, "multimodal_output", None)
-        if isinstance(multimodal_output, dict):
-            return multimodal_output
-
-    raise AssertionError("No multimodal audio output found in VoxCPM generate results")
-
-
-@hardware_test(res={"cuda": "L4"}, num_cards=1)
-def test_voxcpm_zero_shot_001(omni_runner: OmniRunner) -> None:
-    outputs = list(omni_runner.omni.generate(_build_prompt("Hello, this is a VoxCPM offline inference test.")))
-
-    assert outputs, "No outputs returned"
-
-    multimodal_output = _extract_final_multimodal_output(outputs)
-    audio = _extract_audio_tensor(multimodal_output)
-    assert audio.numel() > SAMPLE_RATE // 2, f"Audio too short: {audio.numel()} samples"
-
-    duration_s = audio.shape[0] / SAMPLE_RATE
-    assert 0.5 < duration_s < 30.0, f"Audio duration out of range: {duration_s:.2f}s"
-
-    peak = float(torch.max(torch.abs(audio)).item()) if audio.numel() > 0 else 0.0
-    assert peak > 0.01, "Generated audio appears to be silence"
-
-    audio_np = audio.numpy()
-    rms = float(np.sqrt(np.mean(np.square(audio_np)))) if audio_np.size else 0.0
-    assert rms > 1e-4, "Generated audio RMS too low"