[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[],[],null,["# Evaluate models\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 the benchmarking functionality of the Cloud Speech-to-Text Console to measure the accuracy of any of the [transcription models](/speech-to-text/v2/docs/transcription-model) used in the Speech-to-Text V2 API.\n\nCloud Speech-to-Text Console provides visual benchmarking for pre-trained and Custom Speech-to-Text models. You can inspect the recognition quality by comparing Word-Error-Rate (WER) evaluation metrics across multiple transcription models to help you decide which model best fits your application.\n\nBefore you begin\n----------------\n\nEnsure you have signed up for a Google Cloud account, created a project, trained a custom speech model, and deployed using an endpoint.\n\nCreate a ground-truth dataset\n-----------------------------\n\nTo create a custom benchmarking dataset, gather audio samples that accurately reflect the type of traffic the transcription model will encounter in a production environment. The aggregate duration of these audio files should ideally span a minimum of 30 minutes and not exceed 10 hours. To assemble the dataset, you will need to:\n\n1. Create a directory in a Cloud Storage bucket of your choice to store the audio and text files for the dataset.\n2. For every audio-file in the dataset, create reasonably accurate transcriptions. For each audio file (such as `example_audio_1.wav`), a corresponding ground-truth text file (`example_audio_1.txt`) must be created. This service uses these audio-text pairings in a Cloud Storage bucket to assemble the dataset.\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)."]]
