fix(l10n): Update translations from Transifex

Signed-off-by: Nextcloud bot <bot@nextcloud.com>

Author: nextcloud-bot

l10n/zh_HK.js

@@ -2,9 +2,27 @@ OC.L10N.register(
     "recognize",
     {
     "Recognize" : "識別",
+    "Audio classification" : "音訊分類",
+    "Classify audios into categories." : "將音訊分類至不同類別。",
+    "Audios" : "音訊",
+    "Provide audios to classify" : "提供要分類的音訊",
     "Categories" : "分類",
+    "The classified categories. Each input audio is mapped to a text containing a comma separated list of categories." : "分類後的類別。每個輸入音訊會對應至一段文字，其中包含以逗號分隔的類別清單。",
+    "Image classification" : "影像分類",
+    "Classify images into categories." : "將影像分類至不同類別。",
     "Images" : "圖像",
+    "Provide images to classify" : "提供要分類的影像",
+    "The classified categories. Each input image is mapped to a text containing a comma separated list of categories." : "分類後的類別。每個輸入影像會對應至一段文字，其中包含以逗號分隔的類別清單。",
+    "Image face recognition" : "影像人臉辨識",
+    "Recognize faces in images and return embedding vectors for each face." : "辨識影像中的人臉，並為每張人臉傳回嵌入向量。",
+    "Provide images to recognize faces in" : "提供要進行人臉辨識的影像",
+    "Faces" : "人臉",
+    "The detected faces. Each input image is mapped to a text containing JSON-encoded face descriptions ({x,y,width,height,score,vector,angle} ) separated by line breaks." : "偵測到的人臉。每個輸入影像會對應至一段文字，其中包含以 JSON 編碼的人臉描述（{x,y,width,height,score,vector,angle}），各描述以換行符號分隔。",
+    "Video classification" : "影片分類",
+    "Classify videos into categories." : "將影片分類至不同類別。",
     "Videos" : "影片",
+    "Provide videos to classify" : "提供要分類的影片",
+    "The classified categories. Each input video is mapped to a text containing a comma separated list of categories." : "分類後的類別。每個輸入影片會對應至一段文字，其中包含以逗號分隔的類別清單。",
     "Smart media tagging and face recognition with on-premises machine learning models" : "使用本地機器學習模型進行智能媒體標記和人臉識別",
     "Smart media tagging and face recognition with on-premises machine learning models.\nThis app goes through your media collection and adds fitting tags, automatically categorizing your photos and music.\n\n* 📷 👪 Recognizes faces from contact photos\n* 📷 🏔 Recognizes animals, landscapes, food, vehicles, buildings and other objects\n* 📷 🗼 Recognizes landmarks and monuments\n* 👂 🎵 Recognizes music genres\n* 🎥 🤸 Recognizes human actions on video\n\n⚡ Tagging works via Nextcloud's Collaborative Tags\n  * 👂 listen to your tagged music with the audioplayer app\n  * 📷 view your tagged photos and videos with the photos app\n\nModel sizes:\n\n * Object recognition: 1GB\n * Landmark recognition: 300MB\n * Video action recognition: 50MB\n * Music genre recognition: 50MB\n\n## Ethical AI Rating\n### Rating for Photo object detection: 🟢\n\nPositive:\n* the software for training and inference of this model is open source\n* the trained model is freely available, and thus can be run on-premises\n* the training data is freely available, making it possible to check or correct for bias or optimise the performance and CO2 usage.\n\n### Rating for Photo face recognition: 🟢\n\nPositive:\n* the software for training and inference of this model is open source\n* the trained model is freely available, and thus can be run on-premises\n* the training data is freely available, making it possible to check or correct for bias or optimise the performance and CO2 usage.\n\n### Rating for Video action recognition: 🟢\n\nPositive:\n* the software for training and inferencing of this model is open source\n* the trained model is freely available, and thus can be ran on-premises\n* the training data is freely available, making it possible to check or correct for bias or optimise the performance and CO2 usage.\n\n## Ethical AI Rating\n### Rating Music genre recognition: 🟡\n\nPositive:\n* the software for training and inference of this model is open source\n* the trained model is freely available, and thus can be run on-premises\n\nNegative:\n* the training data is not freely available, limiting the ability of external parties to check and correct for bias or optimise the model’s performance and CO2 usage.\n\nLearn more about the Nextcloud Ethical AI Rating [in our blog](https://nextcloud.com/blog/nextcloud-ethical-ai-rating/).\n\nAfter installation, you can enable tagging in the admin settings.\n\nRequirements:\n- php 7.4 and above\n- App \"collaborative tags\" enabled\n- For native speed:\n  - Processor: x86 64-bit (with support for AVX instructions)\n  - System with glibc (usually the norm on Linux; FreeBSD, Alpine linux and thus also the official Nextcloud Docker container and Nextcloud AIO are *not* such systems)\n- For sub-native speed (using WASM mode)\n  - Processor: x86 64-bit, arm64, armv7l (no AVX needed)\n  - System with glibc or musl (incl. Alpine linux and thus also the official Nextcloud Docker container and also Nextcloud AIO)\n- ~4GB of free RAM (if you're cutting it close, make sure you have some swap available)\n\nThe app does not send any sensitive data to cloud providers or similar services. All processing is done on your Nextcloud machine, using Tensorflow.js running in Node.js." : "使用本地機器學習模型進行智能媒體標記和人臉識別\n\n此應用程式會遍歷您收藏的媒體並對其添加合適的標籤，自動對您的照片和音樂進行分類。\n\n* 📷 👪 從聯絡人照片中識別面孔\n* 📷 🏔 識別動物、風景、食物、車輛、建築物和其他物體\n* 📷 🗼 識別地標和紀念碑\n* 👂 🎵 識別音樂流派\n* ⚡ 通過 Nextcloud 的協作標籤進行標記，允許您的任何應用程序進行訪問\n* 👂 使用音頻播放器應用程序聆聽您標記的音樂\n* 📷 使用照片應用程式查看您標記的照片\n\n## 道德人工智能評級\n### 照片對象檢測評級：🟢\n\n積極的：\n* 該模型的訓練和推理軟件是開源的\n* 經過訓練的模型是免費提供的，因此可以在本地運行\n* 訓練數據是免費提供的，可以檢查或糾正偏差或優化性能和二氧化碳的使用。\n\n### 照片人臉識別評分：🟢\n\n積極的：\n* 該模型的訓練和推理軟件是開源的\n* 經過訓練的模型是免費提供的，因此可以在本地運行\n* 訓練數據是免費提供的，可以檢查或糾正偏差或優化性能和二氧化碳的使用。\n\n### 視頻動作識別評級：🟢\n\n積極的：\n* 該模型的訓練和推理軟件是開源的\n* 經過訓練的模型是免費提供的，因此可以在本地運行\n* 訓練數據是免費提供的，可以檢查或糾正偏差或優化性能和二氧化碳的使用。\n\n## 道德人工智能評級\n### 評級音樂流派識別：🟡\n\n積極的：\n* 該模型的訓練和推理軟件是開源的\n* 經過訓練的模型是免費提供的，因此可以在本地運行\n\n消極的：\n* 訓練數據不是免費提供的，這限制了外部各方檢查和糾正偏差或優化模型性能和二氧化碳使用的能力。\n\n了解有關 Nextcloud 道德 AI 評級的更多信息 [在我們的博客中](https://nextcloud.com/blog/nextcloud-ethical-ai- rating/)。\n\n安裝後，您可以在管理設置中啟用標記。\n\n要求：\n- PHP 7.4 及以上版本\n- 啟用應用程序“協作標籤”\n- 對於本機速度：\n- 處理器：x86 64 位（支持 AVX 指令）\n- 具有 glibc 的系統（通常是 Linux 上的標準；FreeBSD、Alpine linux 以及官方 Nextcloud Docker 容器和 Nextcloud AIO *不是*此類系統）\n- 低於本機速度（使用 WASM 模式）\n- 處理器：x86 64 位、arm64、armv7l（無需 AVX）\n- 具有 glibc 或 musl 的系統（包括 Alpine linux，因此也是官方 Nextcloud Docker 容器和 Nextcloud AIO）\n- ~4GB 可用 RAM（如果您要削減它，請確保有一些可用的交換空間）\n\n該應用程式不會向雲提供商或類似服務發送任何敏感數據。 所有處理都是在您的 Nextcloud 計算機上使用在 Node.js 中運行的 Tensorflow.js 完成的。",
     "Your server does not support AVX instructions" : "您的伺服器不支援 AVX 指示",
@@ -23,6 +41,11 @@ OC.L10N.register(
     "Background Jobs are not executed via cron. Recognize requires background jobs to be executed via cron." : "後台作業不通過 cron 執行。Recognize 需要通過 cron 執行後台作業。",
     "The app is installed and will automatically classify files in background processes." : "該應用程式已安裝，並將自動對後台進程中的檔案進行分類。",
     "None of the tagging options below are currently selected. The app will currently do nothing." : "目前未選擇以下任何標記選項。該應用程式目前不會執行任何操作。",
+    "Classifier backend" : "分類器後端",
+    "The recognize_backend ExApp is installed; TaskProcessing mode is recommended." : "已安裝 recognize_backend ExApp；建議使用 TaskProcessing 模式。",
+    "TaskProcessing mode is enabled, but no recognize_backend ExApp was detected. Make sure a TaskProcessing provider for the recognize task types is installed and enabled." : "已啟用 TaskProcessing 模式，但未偵測到 recognize_backend ExApp。請確保已安裝並啟用支援 Recognize 任務類型的 TaskProcessing 提供者。",
+    "Use Nextcloud TaskProcessing for classification" : "使用 Nextcloud TaskProcessing 進行分類",
+    "When enabled, Recognize hands files off to a Nextcloud TaskProcessing provider (typically the recognize_backend ExApp) instead of running TensorFlow locally. Hardware checks and Node.js / FFmpeg requirements no longer apply in this mode." : "啟用後，Recognize 會將檔案交由 Nextcloud TaskProcessing 提供者（通常是 recognize_backend ExApp）處理，而不會在本機執行 TensorFlow。在此模式下，不再需要硬件檢查，也毋須符合 Node.js／FFmpeg 要求。",
     "Face recognition" : "人臉識別",
     "Face recognition is working. " : "人臉識別中。",
     "An error occurred during face recognition, please check the Nextcloud logs." : "人臉識別出錯，請查看 Nextcloud 記錄。",
@@ -115,6 +138,8 @@ OC.L10N.register(
     "Nice value to set the priority of the Node.js processes. The value can only be from 0 to 19 since the Node.js process runs without superuser privileges. The higher the nice value, the lower the priority of the process." : "Nice 值設置 Node.js 進程的優先級。該值只能在 0 到 19 之間，因為 Node.js 進程在沒有超級用戶權限的情況下運行。Nice值越高，進程的優先級越低。",
     "Terminal commands" : "終端命令",
     "To download all models preliminary to executing the classification jobs, run the following command on the server terminal." : "要下載執行分類作業之前的所有模型，請在伺服器終端上運行以下命令。",
+    "To trigger a full classification run in the background, run the following command on the server terminal. (The classification will run in multiple background jobs which can run in parallel.)" : "如要在背景觸發完整分類，請在伺服器終端機執行以下指令。（分類會以多個可並行執行的背景工作進行。）",
+    "To run a full classification run on the terminal, run the following. (The classification will run in sequence inside your terminal; doesn't work with task processing mode)" : "如要在終端機執行完整分類，請執行以下指令。（分類會在終端機內依次執行；不適用於 TaskProcessing 模式。）",
     "Before running a full initial classification run on the terminal, you should stop all background processing that Recognize scheduled upon installation to avoid interference." : "在終端上運行完整的初始分類運行之前，您應該停止所有識別安裝時安排的後台處理以避免干擾。",
     "To run a face clustering run on for each user in the terminal, run the following. Consider adding the parameter --batch-size 10000 for large libraries to avoid PHP memory exhaustion. (The clustering will run in sequence inside your terminal.)" : "若要在終端機中為每個用戶運行臉部聚類，請執行以下操作：考慮在大型資料庫中添加參數 --batch-size 10000 以避免 PHP 記憶體耗盡。（聚類將在您的終端機中依序執行。）",
     "To remove all face clusters but keep the raw detected faces run the following on the terminal:" : "要刪除所有面部聚類但保留檢測到的原始面部，請在終端上運行以下命令：",