test: Playwright E2E with real backend + fake TTS engine

e2e/ drives the web build (vite preview) against a per-worker uvicorn:
each Playwright worker boots its own backend on its own port with a
temp cwd, so SQLite and audio files are fully isolated and parallel-
safe. The page fixture seeds the persisted voicebox-server store with
the worker's URL before any script runs.

VOICEBOX_FAKE_TTS=1 short-circuits get_tts_backend_for_engine to a
backend that synthesizes a sine tone sized to the text — the real task
queue, SSE progress, history rows, and audio serving all run, only
inference is fake. backend/requirements-ci.txt is the slim dependency
set validated to boot the app on a CPU-only runner.

Specs: startup, settings layout, seeded profile in /voices, and
generate-to-completed-audio through the full pipeline.

backend/backends/__init__.py

@@ -12,6 +12,7 @@ and a model config registry that eliminates per-engine dispatch maps.
 # HF_HUB_OFFLINE=1 and on network failures.
 from ..utils import hf_offline_patch  # noqa: F401
 
+import os
 import threading
 from dataclasses import dataclass, field
 from typing import Protocol, Optional, Tuple, List
@@ -678,6 +679,13 @@ def get_tts_backend_for_engine(engine: str) -> TTSBackend:
     """
     global _tts_backends
 
+    # Test mode: every engine resolves to the fake backend so the full
+    # generation pipeline runs without model weights (see fake_backend.py).
+    if os.environ.get("VOICEBOX_FAKE_TTS") == "1":
+        from .fake_backend import get_fake_backend
+
+        return get_fake_backend()
+
     # Fast path: check without lock
     if engine in _tts_backends:
         return _tts_backends[engine]

backend/backends/fake_backend.py

@@ -0,0 +1,92 @@
+"""Fake TTS backend for UI and E2E testing.
+
+Activated by ``VOICEBOX_FAKE_TTS=1``. Every engine resolves to this backend,
+which synthesizes a quiet sine tone sized to the input text — so the full
+generation pipeline (task queue, SSE progress, database rows, audio serving)
+runs exactly as in production, minus model weights and GPU time.
+"""
+
+import asyncio
+import logging
+from typing import ClassVar, Optional
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+SAMPLE_RATE = 24_000
+SECONDS_PER_CHAR = 0.02
+MIN_DURATION_S = 0.25
+TONE_HZ = 440.0
+AMPLITUDE = 0.1
+
+
+class FakeTTSBackend:
+    """Implements the TTSBackend protocol without any model."""
+
+    MODEL_CONFIGS: ClassVar[list] = []
+
+    def __init__(self) -> None:
+        self._loaded = False
+
+    async def load_model(self, model_size: str = "default") -> None:
+        if self._loaded:
+            return
+        # Brief pause so the UI's loading_model state is observable.
+        await asyncio.sleep(0.1)
+        self._loaded = True
+        logger.info("Fake TTS backend loaded (VOICEBOX_FAKE_TTS)")
+
+    async def load_model_async(self, model_size: str = "default") -> None:
+        # Qwen engines are loaded through this variant (see load_engine_model).
+        await self.load_model(model_size)
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> tuple[dict, bool]:
+        return ({"fake": True, "audio_path": audio_path, "reference_text": reference_text}, False)
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: list[str],
+        reference_texts: list[str],
+    ) -> tuple[np.ndarray, str]:
+        combined_text = " ".join(reference_texts)
+        return np.zeros(SAMPLE_RATE, dtype=np.float32), combined_text
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> tuple[np.ndarray, int]:
+        duration_s = max(MIN_DURATION_S, len(text) * SECONDS_PER_CHAR)
+        # Yield once so cancellation has a window, mirroring real inference.
+        await asyncio.sleep(0.05)
+        t = np.linspace(0.0, duration_s, int(SAMPLE_RATE * duration_s), endpoint=False)
+        audio = (AMPLITUDE * np.sin(2.0 * np.pi * TONE_HZ * t)).astype(np.float32)
+        return audio, SAMPLE_RATE
+
+    def unload_model(self) -> None:
+        self._loaded = False
+
+    def is_loaded(self) -> bool:
+        return self._loaded
+
+    def _get_model_path(self, model_size: str) -> str:
+        return "fake"
+
+
+_fake_backend: Optional[FakeTTSBackend] = None
+
+
+def get_fake_backend() -> FakeTTSBackend:
+    global _fake_backend
+    if _fake_backend is None:
+        _fake_backend = FakeTTSBackend()
+    return _fake_backend