Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Gunakan model Speech-to-Text Kustom terlatih dalam alur kerja benchmark atau aplikasi produksi Anda. Anda harus men-deploy dan mengekspos model melalui endpoint khusus, yang dibuat sebagian untuk men-deploy model di region yang Anda pilih. Anda akan otomatis mendapatkan akses terprogram melalui objek pengenal. ID ini digunakan langsung melalui API V2 atau di konsol Google Cloud . Anda dapat men-deploy model di region yang berbeda dari tempat model dilatih, tetapi salinan model akan dibuat di region yang ditentukan oleh endpoint.
Untuk menggunakan model ucapan kustom, Anda harus men-deploy dan mengeksposnya melalui endpoint khusus. Dengan membuat endpoint, Anda men-deploy model di region pilihan Anda. Anda akan otomatis diberi akses terprogram melalui objek pengenal untuk digunakan langsung melalui V2 API untuk inferensi atau di Google Cloud konsol.
Sebelum memulai
Pastikan Anda telah mendaftar ke akun Google Cloud , membuat project, dan melatih model ucapan kustom.
Buka Speech di konsol Google Cloud , lalu buka Speech-to-Text.
Buka bagian Model Kustom pada menu navigasi di sebelah kiri.
Membuat endpoint
Buka tab Endpoint di bagian Custom Models.
Klik Endpoint Baru.
Tentukan nama endpoint Anda. Ini bertindak sebagai ID unik untuk resource endpoint Anda dan digunakan untuk memanggil model ucapan kustom Anda untuk inferensi.
Tentukan region tempat Anda ingin men-deploy model ucapan kustom. Jika model dilatih di region yang berbeda dengan yang ditentukan di konfigurasi endpoint, salinan model baru akan dibuat secara otomatis.
Pilih model ucapan kustom terlatih dari daftar yang ingin Anda tampilkan melalui endpoint.
Klik Create dan setelah beberapa saat model ucapan kustom Anda di-deploy di endpoint, siap digunakan untuk inferensi dan tolok ukur.
Membuat daftar endpoint Anda
Anda dapat mengelola endpoint terkait di konsol dengan memilih tab Endpoints di bagian Custom Models. Anda juga dapat mencantumkan endpoint yang Anda buat di konsol, beserta statusnya saat ini dan model Speech-to-Text kustom terkait.
Menghapus endpoint
Sebelum memulai, pastikan tidak ada traffic yang dirutekan melalui endpoint Anda, karena jika dihapus, endpoint tersebut tidak akan lagi menayangkan permintaan apa pun.
Buka tab Endpoint di bagian Custom Models.
Di tab Endpoint, klik untuk meluaskan opsi, lalu klik Hapus. Dalam beberapa saat, endpoint akan dihapus dan tidak lagi menyalurkan traffic.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 UTC."],[],[],null,["# Deploy and manage endpoints\n\n| **Preview**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nUse a trained Custom Speech-to-Text model in your production application or benchmarking workflows. You must deploy and expose the model through a dedicated endpoint, created in part to deploy the model in your chosen region. You automatically get programmatic access through a recognizer object. It is used directly through the V2 API or in the Google Cloud console. You can deploy your model in a region different from where it was trained, but a copy of the model is created in the region specified by the endpoint.\n\nTo use a custom speech model, you need to deploy and expose it through a dedicated endpoint. By creating an endpoint, you're deploying the model in the region of your choice. You're automatically granted programmatic access through a recognizer object to be used directly through the V2 API for inference or in the Google Cloud console.\n\nBefore you begin\n----------------\n\nEnsure you have signed up for a Google Cloud account, created a project, and trained a custom speech model.\n\n1. Go to **Speech** in the Google Cloud console, and navigate to Speech-to-Text.\n2. Navigate within the **Custom Models** section of the navigation bar on the left.\n\nCreate an endpoint\n------------------\n\n1. Navigate to the **Endpoints** tab of the **Custom Models** section.\n2. Click **New Endpoint**.\n3. Define a name for your endpoint. This acts as a unique identifier for your endpoint resource and is used to invoke your custom speech model for inference.\n4. Define the region where you want your custom speech model to be deployed. If the model was trained in a different region than the one defined in the endpoint configuration, a new model copy is created automatically.\n5. Select the trained custom speech model from the list that you want to expose through the endpoint.\n6. Click **Create** and after a few moments your custom speech model is deployed in your endpoint, ready to be used for inference and benchmarking.\n\nList your endpoints\n-------------------\n\nYou can manage the associated endpoints in the console by selecting the Endpoints tab under the Custom Models section. You can also list the endpoints that you created in the console, along with their current state and associated custom Speech-to-Text model.\n\nDelete an endpoint\n------------------\n\nBefore you start, make sure that there is no traffic routed through your endpoint, because deleting it will stop it from serving any requests.\n\n1. Navigate to the **Endpoints** tab of the **Custom Models** section.\n2. Under the **Endpoints** tab, click to expand options and then click **Delete**. In a few moments, the endpoint is deleted and no longer serves any traffic.\n\nBenchmark the model\n-------------------\n\nUsing the Custom Speech-to-Text model and your benchmarking dataset to assess the accuracy of your model, follow the [Measure and improve accuracy guide](/speech-to-text/docs/measure-accuracy)."]]
