lct-02: замер задержки голосового контура, make latency
Замер на машине разработки (Ryzen 7 8845HS, WSL2), не на демо-машине — там make latency надо прогнать трижды, разброс между прогонами до 20%. Распознавание — GigaAM v3 RNNT int8: фраза на 2 с за ~285 мс, WER 0% на 99 словах живой речи Golos. CTC не быстрее ни в одном из двух прогонов и ошибается чаще: время съедает общий энкодер, поэтому ступень деградации «RNNT не успевает → CTC» по скорости ничего не даёт. Синтез — Silero v5: в 10 раз быстрее реального времени, а не в 40, как заложено в STACK.md; первая фраза 100–250 мс в зависимости от длины. Частота синтеза на скорость не влияет, без профилирующего компилятора — на 13% быстрее. Сквозной бюджет: 600 мс endpointing + ~285 STT + ~150 TTS = ~1 с без LLM. Цель ≤ 1.5 с держится, только если LLM отдаёт первое предложение быстрее ~465 мс. Вопрос, считается ли филлер «алло?..» первым звуком, записан в LATENCY.md для людей. make models качает GigaAM и Silero VAD с Hugging Face. Silero TTS лежит на российском хосте, недоступном из-под VPN: скрипт не лезет на зеркала (.pt грузится через pickle — это чужой код) и честно говорит, откуда скачать вручную.
This commit is contained in:
Ivan Gerasimov
parent
f24031b5b2
commit
dcdec56779
5 changed files with 335 additions and 256 deletions
7
Makefile
7
Makefile
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@ -30,7 +30,7 @@ test: ## Тесты бэкенда
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typecheck: ## Проверить фронтенд компилятором
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npm --prefix frontend run typecheck
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models: ## Скачать модели в backend/models/ (сейчас — эмбеддинги; GigaAM и Silero — lct-02)
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models: ## Скачать модели в backend/models/: эмбеддинги, GigaAM, Silero VAD; Silero TTS — вручную
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cd backend && $(UV) run python scripts/models.py
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seed: ## Залить сценарии из /scenarios в БД
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@ -39,6 +39,9 @@ seed: ## Залить сценарии из /scenarios в БД
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lesson: ## Запустить занятие и напечатать ссылки: make lesson s=<сценарий> m=<режим>
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cd backend && $(UV) run --no-project --with websockets python scripts/start_lesson.py "$(s)" "$(m)"
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latency: ## Замер задержки голосового контура по этапам — запускать на демо-машине
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cd backend && $(UV) run --extra voice python scripts/latency.py
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migrate: ## Накатить миграции
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cd backend && $(UV) run alembic upgrade head
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@ -54,4 +57,4 @@ pregen: ## Дерево диалога и WAV первых реплик для
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demo: ## Поднять всё в демо-режиме: офлайн, прогретые модели
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@echo "не реализовано — карточка tasks/lct-21-demo-readiness.md"; exit 1
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.PHONY: help dev down back front types test typecheck lesson migrate revision models seed repl pregen demo
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.PHONY: help dev down back front types test typecheck lesson latency migrate revision models seed repl pregen demo
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@ -26,12 +26,24 @@ dependencies = [
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voice = [
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"onnx-asr>=0.6",
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"torch>=2.2",
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# Нужен пакету Silero TTS: импорт зашит внутри v5_ru.pt.
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"scipy>=1.11",
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]
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dev = [
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"pytest>=8.2",
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"pytest-asyncio>=0.23",
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]
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# torch — только CPU-сборка. Из обычного PyPI он тянет CUDA-библиотеки
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# на несколько гигабайт, а GPU на стенде нет (docs/arch/STACK.md).
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[tool.uv.sources]
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torch = { index = "pytorch-cpu" }
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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222
backend/scripts/latency.py
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222
backend/scripts/latency.py
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@ -0,0 +1,222 @@
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"""make latency: замер бюджета задержки голосового контура по этапам.
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Запускать **на той машине, которая поедет на занятие** (docs/arch/BACKEND.md).
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Цифры пишутся в docs/LATENCY.md руками вместе с решениями — скрипт только меряет.
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Что меряется и что нет:
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* STT — GigaAM v3 RNNT и CTC (int8) на живой речи Golos: задержка и ошибка
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распознавания (WER) против эталонной расшифровки;
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* VAD — Silero VAD на одном окне;
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* TTS — Silero v5 на коротких репликах паникующего звонящего;
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* эмбеддинги — multilingual-e5-small на реплике оператора.
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* LLM не меряется: нужен ключ и сеть до провайдера.
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Golos — чистая речь со смартфонов, читающих фразы. Паникующий звонящий и оператор
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в стрессе дадут ошибку выше: цифра WER отсюда — нижняя граница.
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"""
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import json
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import os
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import platform
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import statistics
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import sys
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import time
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import wave
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from pathlib import Path
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import numpy as np
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ROOT = Path(__file__).resolve().parents[1]
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MODELS = ROOT / "models"
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sys.path.insert(0, str(ROOT))
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WARMUP = 2
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def machine() -> str:
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cpu = "?"
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try:
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for line in open("/proc/cpuinfo", encoding="utf-8"):
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if line.startswith("model name"):
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cpu = line.split(":", 1)[1].strip()
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break
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except OSError:
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cpu = platform.processor()
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ram = os.sysconf("SC_PAGE_SIZE") * os.sysconf("SC_PHYS_PAGES") / 2**30
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wsl = "WSL2" if "microsoft" in platform.release().lower() else "нативно"
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return f"{cpu}, ядер {os.cpu_count()}, RAM {ram:.0f} ГБ, {platform.system()} {wsl}"
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def stats(values_ms: list[float]) -> str:
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ordered = sorted(values_ms)
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p95 = ordered[min(len(ordered) - 1, int(round(0.95 * (len(ordered) - 1))))]
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digits = 2 if p95 < 10 else 0 # VAD укладывается в доли миллисекунды
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return f"медиана {statistics.median(ordered):.{digits}f} мс, p95 {p95:.{digits}f} мс"
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def read_wav(path: Path) -> tuple[np.ndarray, int]:
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with wave.open(str(path)) as w:
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rate, channels, width = w.getframerate(), w.getnchannels(), w.getsampwidth()
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raw = w.readframes(w.getnframes())
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assert width == 2, f"{path.name}: ожидался PCM16"
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audio = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768
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if channels > 1:
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audio = audio.reshape(-1, channels).mean(axis=1)
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return audio, rate
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def normalize(text: str) -> list[str]:
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text = text.lower().replace("ё", "е")
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return "".join(ch if ch.isalnum() or ch.isspace() else " " for ch in text).split()
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def wer(reference: list[str], hypothesis: list[str]) -> tuple[int, int]:
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"""Расстояние Левенштейна по словам. Возвращает (ошибок, слов в эталоне)."""
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prev = list(range(len(hypothesis) + 1))
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for i, ref_word in enumerate(reference, 1):
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cur = [i] + [0] * len(hypothesis)
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for j, hyp_word in enumerate(hypothesis, 1):
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cur[j] = min(prev[j] + 1, cur[j - 1] + 1, prev[j - 1] + (ref_word != hyp_word))
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prev = cur
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return prev[-1], len(reference)
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def measure_stt() -> None:
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import onnx_asr
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corpus = MODELS / "samples" / "golos"
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manifest = json.load(open(corpus / "manifest.json", encoding="utf-8-sig"))["items"]
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clips = [(read_wav(corpus / item["file"]), item["text"]) for item in manifest]
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total_audio = sum(len(audio) / rate for (audio, rate), _ in clips)
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print(f"\n## STT — Golos, {len(clips)} фраз, {total_audio:.0f} с речи")
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for name in ("gigaam-v3-rnnt", "gigaam-v3-ctc"):
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started = time.monotonic()
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model = onnx_asr.load_model(name, MODELS / "gigaam-v3-onnx", quantization="int8")
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load_s = time.monotonic() - started
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for (audio, rate), _ in clips[:WARMUP]:
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model.recognize(audio, sample_rate=rate)
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latencies, errors, words, audio_s, compute_s = [], 0, 0, 0.0, 0.0
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worst, durations = [], []
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for (audio, rate), reference in clips:
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started = time.monotonic()
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hypothesis = model.recognize(audio, sample_rate=rate)
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elapsed = time.monotonic() - started
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latencies.append(elapsed * 1000)
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durations.append(len(audio) / rate)
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audio_s += len(audio) / rate
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compute_s += elapsed
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e, n = wer(normalize(reference), normalize(hypothesis))
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errors, words = errors + e, words + n
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if e:
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worst.append(f" «{reference}» → «{hypothesis}»")
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print(f"- {name} int8: загрузка {load_s:.1f} с; фраза {stats(latencies)}; "
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f"RTF {compute_s / audio_s:.3f}; WER {100 * errors / words:.1f}% ({errors} из {words} слов)")
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# Время растёт с длиной фразы, а у Golos фразы длиннее вопросов оператора:
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# медиана по корпусу завышает задержку. Считаем зависимость от длительности.
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slope, intercept = np.polyfit(durations, latencies, 1)
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span = f"{min(durations):.1f}–{max(durations):.1f} с"
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print(f" зависимость: ≈ {intercept:.0f} мс + {slope:.0f} мс × секунд речи (фразы корпуса {span})")
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print(" " + "; ".join(f"{sec} с → ~{intercept + slope * sec:.0f} мс" for sec in (1.5, 2, 3)))
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for line in worst[:5]:
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print(line)
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def measure_vad() -> None:
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import onnxruntime as ort
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path = MODELS / "silero-vad" / "silero_vad.onnx"
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session = ort.InferenceSession(str(path), providers=["CPUExecutionProvider"])
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inputs = {i.name: i for i in session.get_inputs()}
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window = 512 # окно Silero VAD при 16 кГц — 32 мс
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feed = {"input": np.zeros((1, window), dtype=np.float32), "sr": np.array(16000, dtype=np.int64)}
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if "state" in inputs:
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feed["state"] = np.zeros((2, 1, 128), dtype=np.float32)
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else:
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feed["h"] = np.zeros((2, 1, 64), dtype=np.float32)
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feed["c"] = np.zeros((2, 1, 64), dtype=np.float32)
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for _ in range(50):
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session.run(None, feed)
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runs = []
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for _ in range(500):
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started = time.monotonic()
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session.run(None, feed)
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runs.append((time.monotonic() - started) * 1000)
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print(f"\n## VAD — Silero, окно 32 мс\n- на окно: {stats(runs)} (задержка endpointing задана конфигом: 600 мс)")
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def measure_tts() -> None:
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path = MODELS / "silero-tts" / "v5_ru.pt"
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if not path.exists():
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print("\n## TTS\n- не измерено: нет models/silero-tts/v5_ru.pt")
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return
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import torch
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torch.set_num_threads(4)
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# Профилирующий компилятор TorchScript на новых длинах входа ничего не выигрывает:
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# без него синтез на 13% быстрее на тех же фразах.
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torch._C._jit_set_profiling_executor(False)
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started = time.monotonic()
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importer = torch.package.PackageImporter(str(path))
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model = importer.load_pickle("tts_models", "model")
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model.to(torch.device("cpu"))
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load_s = time.monotonic() - started
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speakers = getattr(model, "speakers", [])
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speaker = "xenia" if "xenia" in speakers else speakers[0]
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warmup = ["Алло!", "Горит балкон на пятом этаже!", "Скорее приезжайте, пожалуйста, мы задыхаемся!"]
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for text in warmup:
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model.apply_tts(text=text, speaker=speaker, sample_rate=24000)
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# Реплики паникующего звонящего разной длины: время синтеза растёт с длиной звука,
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# и первая фраза звонящего — самая важная для ощущения «ответил сразу».
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lines = [
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"Алло! Помогите!",
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"Горим!",
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"Дым идёт в подъезд!",
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"Жена с ребёнком в дальней комнате!",
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"Я не знаю, где перекрыть газ!",
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"Быстрее, пожалуйста, дышать нечем!",
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"Муж пытался потушить, но не получилось!",
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"Пятый этаж, подъезд второй!",
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]
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latencies, audio_s, compute_s = [], 0.0, 0.0
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per_line = []
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for text in lines:
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started = time.monotonic()
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audio = model.apply_tts(text=text, speaker=speaker, sample_rate=24000)
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elapsed = time.monotonic() - started
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latencies.append(elapsed * 1000)
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audio_s += len(audio) / 24000
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compute_s += elapsed
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per_line.append(f"«{text}» {len(audio) / 24000:.1f} с звука → {elapsed * 1000:.0f} мс")
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print(f"\n## TTS — Silero v5, голос {speaker}, 24 кГц, 4 потока, без профилирующего компилятора")
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print(f"- загрузка {load_s:.1f} с; реплика {stats(latencies)}; RTF {compute_s / audio_s:.3f} "
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f"(синтез в {audio_s / compute_s:.0f} раз быстрее реального времени)")
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for line in per_line:
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print(f" {line}")
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print(f"- голоса: {', '.join(speakers)}")
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def measure_embeddings() -> None:
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from app.dialog.embeddings import E5Embedder
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embedder = E5Embedder(MODELS / "e5-small")
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embedder.embed(["разогрев"])
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runs = []
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for _ in range(50):
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started = time.monotonic()
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embedder.embed(["На каком этаже пожар?"])
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runs.append((time.monotonic() - started) * 1000)
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print(f"\n## Эмбеддинги — multilingual-e5-small int8\n- реплика: {stats(runs)}")
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if __name__ == "__main__":
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print(f"# Замер задержки\n\nМашина: {machine()}")
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only = set(sys.argv[1:])
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for name, step in [("stt", measure_stt), ("vad", measure_vad), ("tts", measure_tts), ("emb", measure_embeddings)]:
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if not only or name in only:
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step()
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@ -1,7 +1,8 @@
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"""make models: веса моделей в backend/models/.
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Сейчас качает эмбеддинги для слот-автомата. Распознавание (GigaAM) и синтез
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(Silero) добавит карточка lct-02 — после замера на демо-машине.
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Эмбеддинги, распознавание (GigaAM v3, int8) и VAD качаются с Hugging Face.
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Синтез (Silero TTS v5) лежит на models.silero.ai — российском хосте, который
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не отвечает из-под VPN: его скрипт не качает, а проверяет и говорит, что делать.
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Докачка продолжается с места обрыва: сеть на стенде бывает медленной.
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"""
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@ -13,12 +14,27 @@ ROOT = Path(__file__).resolve().parents[1]
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MODELS = ROOT / "models"
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E5 = "https://huggingface.co/Xenova/multilingual-e5-small/resolve/main/"
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GIGAAM = "https://huggingface.co/istupakov/gigaam-v3-onnx/resolve/main/"
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VAD = "https://huggingface.co/istupakov/silero-vad-onnx/resolve/main/"
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FILES = {
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"e5-small/config.json": E5 + "config.json",
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"e5-small/tokenizer.json": E5 + "tokenizer.json",
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"e5-small/model_quantized.onnx": E5 + "onnx/model_quantized.onnx",
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# RNNT — основная модель, CTC — запасная на случай, если RNNT не загрузится
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# (по скорости CTC не выигрывает — docs/LATENCY.md).
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"gigaam-v3-onnx/config.json": GIGAAM + "config.json",
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"gigaam-v3-onnx/v3_vocab.txt": GIGAAM + "v3_vocab.txt",
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"gigaam-v3-onnx/v3_rnnt_encoder.int8.onnx": GIGAAM + "v3_rnnt_encoder.int8.onnx",
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"gigaam-v3-onnx/v3_rnnt_decoder.int8.onnx": GIGAAM + "v3_rnnt_decoder.int8.onnx",
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"gigaam-v3-onnx/v3_rnnt_joint.int8.onnx": GIGAAM + "v3_rnnt_joint.int8.onnx",
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"gigaam-v3-onnx/v3_ctc.int8.onnx": GIGAAM + "v3_ctc.int8.onnx",
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"silero-vad/config.json": VAD + "config.json",
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"silero-vad/silero_vad.onnx": VAD + "silero_vad.onnx",
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}
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||||
|
||||
SILERO_TTS = "silero-tts/v5_ru.pt"
|
||||
SILERO_TTS_URL = "https://models.silero.ai/models/tts/ru/v5_ru.pt"
|
||||
|
||||
|
||||
def fetch(url: str, target: Path) -> None:
|
||||
partial = target.with_suffix(target.suffix + ".part")
|
||||
|
|
@ -43,7 +59,18 @@ def main() -> None:
|
|||
continue
|
||||
target.parent.mkdir(parents=True, exist_ok=True)
|
||||
fetch(url, target)
|
||||
print("эмбеддинги готовы; GigaAM и Silero — карточка tasks/lct-02-latency-baseline.md")
|
||||
tts = MODELS / SILERO_TTS
|
||||
if tts.exists():
|
||||
print(f" {SILERO_TTS}: уже есть")
|
||||
print("все модели на месте")
|
||||
return 0
|
||||
# Не качаем с зеркал: .pt грузится через pickle, файл из непроверенного
|
||||
# источника — это чужой код на стенде.
|
||||
print(f"""
|
||||
{SILERO_TTS}: НЕТ. Хост models.silero.ai не отвечает из-под VPN — скачайте вручную:
|
||||
{SILERO_TTS_URL}
|
||||
и положите в backend/models/{SILERO_TTS}""")
|
||||
return 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
317
backend/uv.lock
generated
317
backend/uv.lock
generated
|
|
@ -1,6 +1,10 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
requires-python = "==3.11.*"
|
||||
resolution-markers = [
|
||||
"sys_platform != 'darwin'",
|
||||
"sys_platform == 'darwin'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "alembic"
|
||||
|
|
@ -90,75 +94,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cuda-bindings"
|
||||
version = "13.4.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "cuda-pathfinder" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/45/57fb37fea9200d854960e36c2181675f92d16bfd7012cc5a95b153e782f0/cuda_bindings-13.4.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56c1552e207b291c321cef9952fbfaf8591c2a14d9c6e5215071020f541a04e5", size = 6484208, upload-time = "2026-09-10T01:16:46.069Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ff/00/15dc1b8f2e5e98975107c366ec5671256d32b8e5c5ed6c1bb3a90a64f035/cuda_bindings-13.4.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e5dc0f13cfd14cd62206fede462f91c497d0c484b8236f4158531d20377066cd", size = 7159324, upload-time = "2026-09-10T01:16:48.109Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cuda-pathfinder"
|
||||
version = "1.8.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/9d/e6/22df83f82f9bc26cb1c42265cf14d34d4908dba2a0f261bd7b28244acb00/cuda_pathfinder-1.8.1-py3-none-any.whl", hash = "sha256:ae0137ff9e56ea97499bcbf54f5f2778ec25f3266715ac86da192a795af982a8", size = 62552, upload-time = "2026-09-02T16:55:28.64Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cuda-toolkit"
|
||||
version = "13.0.3.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/c7/a79086a62c98befcdb8349656c6f114e2db3b8b2422f6e25c97a7f2a9a3c/cuda_toolkit-13.0.3.0-py2.py3-none-any.whl", hash = "sha256:d693caaa261214ddd7dbb60d68e71cbed884e68c2be7509778f3051da0b91c3f", size = 2512, upload-time = "2026-04-14T00:50:08.173Z" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
cublas = [
|
||||
{ name = "nvidia-cublas", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
{ name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
cudart = [
|
||||
{ name = "nvidia-cuda-runtime", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
cufft = [
|
||||
{ name = "nvidia-cufft", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
{ name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
cufile = [
|
||||
{ name = "nvidia-cufile", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
|
||||
]
|
||||
cupti = [
|
||||
{ name = "nvidia-cuda-cupti", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
curand = [
|
||||
{ name = "nvidia-curand", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
cusolver = [
|
||||
{ name = "nvidia-cublas", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
{ name = "nvidia-cusolver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
{ name = "nvidia-cusparse", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
{ name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
cusparse = [
|
||||
{ name = "nvidia-cusparse", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
{ name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
nvjitlink = [
|
||||
{ name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
nvrtc = [
|
||||
{ name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
nvtx = [
|
||||
{ name = "nvidia-nvtx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.141.1"
|
||||
|
|
@ -364,7 +299,9 @@ dev = [
|
|||
]
|
||||
voice = [
|
||||
{ name = "onnx-asr" },
|
||||
{ name = "torch" },
|
||||
{ name = "scipy" },
|
||||
{ name = "torch", version = "2.14.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
|
||||
{ name = "torch", version = "2.14.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
|
|
@ -381,10 +318,11 @@ requires-dist = [
|
|||
{ name = "pytest", marker = "extra == 'dev'", specifier = ">=8.2" },
|
||||
{ name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23" },
|
||||
{ name = "pyyaml", specifier = ">=6.0" },
|
||||
{ name = "scipy", marker = "extra == 'voice'", specifier = ">=1.11" },
|
||||
{ name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0" },
|
||||
{ name = "structlog", specifier = ">=24.1" },
|
||||
{ name = "tokenizers", specifier = ">=0.19" },
|
||||
{ name = "torch", marker = "extra == 'voice'", specifier = ">=2.2" },
|
||||
{ name = "torch", marker = "extra == 'voice'", specifier = ">=2.2", index = "https://download.pytorch.org/whl/cpu" },
|
||||
{ name = "uvicorn", extras = ["standard"], specifier = ">=0.30" },
|
||||
]
|
||||
provides-extras = ["voice", "dev"]
|
||||
|
|
@ -464,158 +402,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/15/ce/e5ec180bc41812edcd8daeb8639d205622c0e8c02259d8ab25a0201b3c2a/numpy-2.4.6-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73", size = 12504263, upload-time = "2026-05-18T23:37:09.715Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cublas"
|
||||
version = "13.1.1.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-cuda-nvrtc" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a7/a1/0bd24ee8c8d03adac032fd2909426a00c88f8c57961b1277ded97f91119f/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b7a210458267ac818974c53038fbec2e969d5c99f305ab15c72522fa9f001dd5", size = 542848918, upload-time = "2026-04-08T18:46:22.985Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3b/cd/154ca20c38269e05eff77c1464e6c1da89f50a6390b565e9d82e06bc11e1/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:37936a16db8fe4ac1f065c2139360608a543a09275cb1a1af612e08cfa065436", size = 423138758, upload-time = "2026-04-08T18:46:58.655Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-cupti"
|
||||
version = "13.0.85"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/2a/2a/80353b103fc20ce05ef51e928daed4b6015db4aaa9162ed0997090fe2250/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_aarch64.whl", hash = "sha256:796bd679890ee55fb14a94629b698b6db54bcfd833d391d5e94017dd9d7d3151", size = 10310827, upload-time = "2025-09-04T08:26:42.012Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/33/6d/737d164b4837a9bbd202f5ae3078975f0525a55730fe871d8ed4e3b952b0/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_x86_64.whl", hash = "sha256:4eb01c08e859bf924d222250d2e8f8b8ff6d3db4721288cf35d14252a4d933c8", size = 10715597, upload-time = "2025-09-04T08:26:51.312Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-nvrtc"
|
||||
version = "13.0.88"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c3/68/483a78f5e8f31b08fb1bb671559968c0ca3a065ac7acabfc7cee55214fd6/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:ad9b6d2ead2435f11cbb6868809d2adeeee302e9bb94bcf0539c7a40d80e8575", size = 90215200, upload-time = "2025-09-04T08:28:44.204Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b7/dc/6bb80850e0b7edd6588d560758f17e0550893a1feaf436807d64d2da040f/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d27f20a0ca67a4bb34268a5e951033496c5b74870b868bacd046b1b8e0c3267b", size = 43015449, upload-time = "2025-09-04T08:28:20.239Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cuda-runtime"
|
||||
version = "13.0.96"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/87/4f/17d7b9b8e285199c58ce28e31b5c5bbaa4d8271af06a89b6405258245de2/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ef9bcbe90493a2b9d810e43d249adb3d02e98dd30200d86607d8d02687c43f55", size = 2261060, upload-time = "2025-10-09T08:55:15.78Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/2e/24/d1558f3b68b1d26e706813b1d10aa1d785e4698c425af8db8edc3dced472/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f82250d7782aa23b6cfe765ecc7db554bd3c2870c43f3d1821f1d18aebf0548", size = 2243632, upload-time = "2025-10-09T08:55:36.117Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cudnn-cu13"
|
||||
version = "9.24.0.43"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-cublas" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ca/30/7c257e3d5cb4fecb147b93895c66e29c93f8e76d74b45bb418ff0587c4ec/nvidia_cudnn_cu13-9.24.0.43-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:a6812a554a1ff0413e9c52b84c26c050380649ab9615f9c16bded368ce9f421f", size = 650976863, upload-time = "2026-07-02T16:23:39.248Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/ba/791cffd048fe5b044e620df55267e3e95c0e6e07d50b41e377c03dfc910f/nvidia_cudnn_cu13-9.24.0.43-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:71f181cd810e90f9b6023b01186fe82d13d65f0ec098581ee201d39fad769e4b", size = 553099438, upload-time = "2026-07-02T16:27:42.58Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cufft"
|
||||
version = "12.0.0.61"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-nvjitlink" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a8/2f/7b57e29836ea8714f81e9898409196f47d772d5ddedddf1592eadb8ab743/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6c44f692dce8fd5ffd3e3df134b6cdb9c2f72d99cf40b62c32dde45eea9ddad3", size = 214085489, upload-time = "2025-09-04T08:31:56.044Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cufile"
|
||||
version = "1.15.1.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3f/70/4f193de89a48b71714e74602ee14d04e4019ad36a5a9f20c425776e72cd6/nvidia_cufile-1.15.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08a3ecefae5a01c7f5117351c64f17c7c62efa5fffdbe24fc7d298da19cd0b44", size = 1223672, upload-time = "2025-09-04T08:32:22.779Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ab/73/cc4a14c9813a8a0d509417cf5f4bdaba76e924d58beb9864f5a7baceefbf/nvidia_cufile-1.15.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:bdc0deedc61f548bddf7733bdc216456c2fdb101d020e1ab4b88d232d5e2f6d1", size = 1136992, upload-time = "2025-09-04T08:32:14.119Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-curand"
|
||||
version = "10.4.0.35"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
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{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.14.0%2Bcpu-cp311-cp311-linux_s390x.whl", hash = "sha256:3fa39713d13f23563ad11636bfff76b677ab4addbc240c54e6b7e47622e8973d", upload-time = "2026-09-02T18:31:08Z" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.14.0%2Bcpu-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:08894195f84541edcbd09072e6c53b791d4cdd09f650b05473f35718a848fd7b", upload-time = "2026-09-02T18:31:13Z" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.14.0%2Bcpu-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:673dbf5c9bbadfffab7a386b6dd7a0c219f1408a328b7b4e86d0ae551cdafa42", upload-time = "2026-09-02T18:31:19Z" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.14.0%2Bcpu-cp311-cp311-win_amd64.whl", hash = "sha256:8e2c47c6556c7d5a85848634372bb2252907d411e9cad669c99406856d536eb5", upload-time = "2026-09-02T18:31:24Z" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.14.0%2Bcpu-cp311-cp311-win_arm64.whl", hash = "sha256:38f0a0330267b36d76b988754878d94001c1519fe3743bd92f038ce5622bea5a", upload-time = "2026-09-02T18:31:28Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -944,15 +768,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/a7/03/921a3d3c75785aca9ebfbfcabfbc3a1be12e2ab5265deb026d55a5a3f83e/tqdm-4.70.1-py3-none-any.whl", hash = "sha256:c293e525e6fef9c20e8728fd4612df02a0aa31bb5fe91ecd93e123b1b7bffa73", size = 80199, upload-time = "2026-09-11T07:25:14.599Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.8.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a3/cf/d21c9b1a4d1df9ba3aed218069f24f762a4b9565c5a362bef6f110c08e72/triton-3.8.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:372285307d4c44ee74cee32de0b4f04bd157e071427e38c6f4ee3e3beb2194f4", size = 226467015, upload-time = "2026-08-28T16:08:05.871Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6f/9a/2c3d3823726d5ad5359f7065b02b984925158b74611c1e3987f6a37461fb/triton-3.8.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:68988ac85d5e7086baeda0ddc175af9667db7529b3c5e11a5c0601b8bef2200a", size = 247945226, upload-time = "2026-08-28T15:55:43.795Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.16.0"
|
||||
|
|
|
|||
Loading…
Reference in a new issue