mARTin-B78/tts-voice-creator-clone-and-design-2
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Update engine RAM metric chips to measured values from docker stats

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Parakeet RNNT NIM measured at 550–605 MB (unified RAM), Magpie TTS at
982 MB, and Qwen3-TTS model at 4.3 GB on disk — replacing the placeholder
estimates that were too high across the board.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
mARTin-B78 2026-05-27 23:51:15 +02:00
parent 6a78be7a28
commit 49e48eb4e3
1 changed files with 9 additions and 9 deletions
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18
server.py
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@ -1820,7 +1820,7 @@ _STT_BACKEND_METRICS: dict[str, dict] = {
"faster_whisper": {"speed": "~70× RT · GPU", "latency": "0.5–2 s", "quality": "large-v3", "ram": "1.5 GB VRAM"},
"whisper_cpp": {"speed": "~8–15× RT · CPU", "latency": "1–5 s", "quality": "large-v3 Q5", "ram": "~1 GB RAM"},
"groq_whisper": {"speed": "fastest cloud", "latency": "0.5–1 s", "quality": "Whisper Turbo", "ram": "cloud · 0"},
"nvidia_parakeet":{"speed": "~200× RT · GPU", "latency": "<0.3 s", "quality": "Parakeet-TDT", "ram": "2 GB VRAM"},
"nvidia_parakeet":{"speed": "~200× RT · GPU", "latency": "<0.3 s", "quality": "Parakeet-TDT", "ram": "~1 GB"},
"nvidia_router": {"speed": "GPU routed", "latency": "~0.5 s", "quality": "varies", "ram": "varies"},
}
@ -3213,7 +3213,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Weak per-request style; instruct may be ignored.",
"best_for": "Known voices, multilingual cloning, benchmarks, and reliable speaker identity.",
"uses_wav": True, "style_aware": False, "true_streaming": False,
"speed": "~0.3× GPU", "latency": "1–3 s", "quality": "Premium clone", "ram": "6–8 GB VRAM",
"speed": "~0.3× GPU", "latency": "1–3 s", "quality": "Premium clone", "ram": "~5 GB",
},
"voice_design": {
"purpose": "Create or reuse prompt-designed voices from natural-language descriptions.",
@ -3221,7 +3221,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Strong style and emotion control through instruct text.",
"best_for": "New characters, personas, dialogue, and designing reference WAVs to clone later.",
"uses_wav": False, "style_aware": True, "true_streaming": False,
"speed": "~0.4× GPU", "latency": "1–3 s", "quality": "Premium", "ram": "6–8 GB VRAM",
"speed": "~0.4× GPU", "latency": "1–3 s", "quality": "Premium", "ram": "~5 GB",
},
"customvoice": {
"purpose": "Generate speech with the CustomVoice model voices.",
@ -3229,7 +3229,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Good per-request style and emotion control.",
"best_for": "Controlled style with configured CustomVoice speakers.",
"uses_wav": False, "style_aware": True, "true_streaming": False,
"speed": "~0.3× GPU", "latency": "1–3 s", "quality": "Premium", "ram": "6–8 GB VRAM",
"speed": "~0.3× GPU", "latency": "1–3 s", "quality": "Premium", "ram": "~5 GB",
},
"streaming": {
"purpose": "Low-latency playback from saved WAV/reference voices.",
@ -3237,7 +3237,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Weak per-request style in the current streaming server.",
"best_for": "Long text, assistants, Open WebUI/SillyTavern playback that can start before completion.",
"uses_wav": True, "style_aware": False, "true_streaming": True,
"speed": "~0.1× GPU", "latency": "0.5–1 s", "quality": "Premium", "ram": "6–8 GB VRAM",
"speed": "~0.1× GPU", "latency": "0.5–1 s", "quality": "Premium", "ram": "~5 GB",
},
"nvidia_magpie": {
"purpose": "Generate speech with NVIDIA Magpie fixed speaker voices.",
@ -3245,7 +3245,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Language and speaker are controlled by the backend voice config; per-request style text is usually ignored.",
"best_for": "Fast local NVIDIA TTS voices and OpenAI-compatible assistant playback.",
"uses_wav": False, "style_aware": False, "true_streaming": False,
"speed": "~0.05× GPU", "latency": "0.3–0.8 s", "quality": "High", "ram": "4–6 GB VRAM",
"speed": "~0.05× GPU", "latency": "0.3–0.8 s", "quality": "High", "ram": "~1 GB",
},
"nvidia_zeroshot": {
"purpose": "Clone a saved library voice through NVIDIA Magpie TTS Zeroshot NIM.",
@ -3253,7 +3253,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Best with a clear 3-10 second prompt. Optional quality params can be configured in Settings.",
"best_for": "Fast NVIDIA reference-audio cloning, streaming-class use cases, live agents, and games.",
"uses_wav": True, "style_aware": False, "true_streaming": False,
"speed": "~0.1× GPU", "latency": "0.5–1 s", "quality": "High clone", "ram": "4–6 GB VRAM",
"speed": "~0.1× GPU", "latency": "0.5–1 s", "quality": "High clone", "ram": "~1 GB",
},
"nvidia_flow": {
"purpose": "Clone a saved library voice through NVIDIA Magpie TTS Flow NIM.",
@ -3261,7 +3261,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Offline high-fidelity clone path; prompt transcript must match the reference audio.",
"best_for": "Studio-style dubbing, narration, and podcast-quality offline generation.",
"uses_wav": True, "style_aware": False, "true_streaming": False,
"speed": "~0.2× GPU", "latency": "1–2 s", "quality": "Studio", "ram": "4–6 GB VRAM",
"speed": "~0.2× GPU", "latency": "1–2 s", "quality": "Studio", "ram": "~1 GB",
},
"kokoro": {
"purpose": "High-quality English TTS with Kokoro 82M model. OpenAI-compatible endpoint.",
@ -3285,7 +3285,7 @@ def _backend_capabilities(backend: str) -> dict:
"style": "Speaker selected by voice ID. Style instruction not supported.",
"best_for": "Local multi-speaker TTS with XTTS v2 model. Coqui/daswer123 docker setup.",
"uses_wav": False, "style_aware": False, "true_streaming": False,
"speed": "~0.3× GPU", "latency": "1–3 s", "quality": "High", "ram": "4–6 GB VRAM",
"speed": "~0.3× GPU", "latency": "1–3 s", "quality": "High", "ram": "3–4 GB",
},
}
return caps.get(_clean_preview_backend(backend), {})