Stay organized with collections
Save and categorize content based on your preferences.
In this quickstart, learn how to measure and improve the accuracy of the Google Cloud Speech-to-Text for your audio data. Also explore the various models and options available from the API to enhance transcription accuracy. Explore how to use the Speech-to-Text UI in the Google Cloud console and a ground-truth file to measure accuracy and to gain insights into the Speech-to-Text system.
Machine Learning (ML) systems are inherently subject to inaccuracies, and Automatic Speech Recognition (ASR) systems, also known as Speech-to-Text systems, are no exception. Accurate measurement of accuracy is strongly coupled to specific use cases and the systems being evaluated, as differences in audio recording quality and acoustic conditions can significantly impact accuracy. As a result, a singular accuracy score for all customers and use cases is impractical. To ensure reliable performance of ASR systems in critical production-facing systems performance. It is also essential to understand how Speech-to-Text performs within the broader context of your system.
For the purposes of this quickstart guide,use the industry standard method for comparison, Word Error Rate (WER), often abbreviated as WER. For more information on how WER is calculated and interpreted see Measure and improve speech accuracy. Let's start.
Getting started with Speech-to-Text Console
Permissions required for this task
To perform this task, you must have the following
permissions:
storage.buckets.get
storage.buckets.list
At the project or bucket level:
storage.objects.create
storage.objects.get
storage.objects.list
storage.objects.update
Ensure you have signed up for a Google Cloud account and created a project.
1. Go to Speech in Google Cloud console, and navigate to Speech-to-Text UI.
2. Using an audio file that is acoustically representative of your use case and how you are planning to use the ASR system, follow the quickstart instructions for making your first transcription using the Speech-to-Text.
Calculating Transcription Accuracy
After you have successfully transcribed your audio file, use the Transcription Accuracy section. This section remains empty until accuracy is calculated for your transcription.
Using the Upload Ground Truth button at the top of the section, you can begin calculating accuracy.
Specifying ground truth
To calculate the accuracy of the transcription, provide a ground truth file. This is a .txt or .csv file, usually a human-generated transcription file that contains the correct or expected transcriptions for comparison.
Using gs://cloud-samples-data/speech/brooklyn_bridge.wav as an example. The ground truth file contains: How old is the Brooklyn Bridge. If you don't have a ground truth file available, a recommendation is to download the transcription in a text format. Edit the transcription file as needed. Upload the transcription file as the ground truth file.
Using Upload or an existing Cloud Storage file, specify the ground truth file, and click Save.
Confirming ground truth
After clicking Save, a prompt displays to confirm that the specified ground truth file is correct. Verify that the ground truth file accurately represents the correct transcriptions, as it directly affects the accuracy metrics.
Click Confirm to proceed.
Review evaluation results
Depending on the size of the input data, the evaluation process might take some time, and the results are displayed upon completion.
Once the evaluation is complete, the following sections are displayed:
The Transcription Accuracy table, the accuracy metrics, and a link to the ground truth file that were used in the process.
The Transcription with a toggle for comparing to the ground truth file along with a breakdown of accuracy metrics and highlights.
Review and interpret the accuracy results to understand the performance of the Speech-to-Text recognizer that are used to identify areas for improvement, as the results vary depending on the inputs and transcription used. In the following examples, you can see indicative cases of the accuracy results, which provide valuable insights for optimization of the Google Cloud Speech-to-Text system.
An example of 0% WER:
An example of 40% WER:
Optional: updating ground truth
You can test a different ground truth against the existing transcription, by reattaching a different file and then repeating steps three and four with an updated ground truth file.
Try it for yourself
If you're new to Google Cloud, create an account to evaluate how
Speech-to-Text performs in real-world
scenarios. New customers also get $300 in free credits to run, test, and
deploy workloads.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-28 UTC."],[],[],null,["# Measure and improve accuracy\n\nIn this quickstart, learn how to measure and improve the accuracy of the Google Cloud Speech-to-Text for your audio data. Also explore the various models and options available from the API to enhance transcription accuracy. Explore how to use the Speech-to-Text UI in the Google Cloud console and a ground-truth file to measure accuracy and to gain insights into the Speech-to-Text system.\n\nMachine Learning (ML) systems are inherently subject to inaccuracies, and Automatic Speech Recognition (ASR) systems, also known as Speech-to-Text systems, are no exception. Accurate measurement of accuracy is strongly coupled to specific use cases and the systems being evaluated, as differences in audio recording quality and acoustic conditions can significantly impact accuracy. As a result, a singular accuracy score for all customers and use cases is impractical. To ensure reliable performance of ASR systems in critical production-facing systems performance. It is also essential to understand how Speech-to-Text performs within the broader context of your system.\n\nFor the purposes of this quickstart guide,use the industry standard method for comparison, [Word Error Rate (WER)](https://en.wikipedia.org/wiki/Word_error_rate), often abbreviated as WER. For more information on how WER is calculated and interpreted see [Measure and improve speech accuracy](/speech-to-text/docs/speech-accuracy). Let's start.\n\nGetting started with Speech-to-Text Console\n-------------------------------------------\n\n#### Permissions required for this task\n\nTo perform this task, you must have the following\n[permissions](/iam/docs/overview#permissions):\n\n\n- `storage.buckets.get`\n- `storage.buckets.list`\n\nAt the project or bucket level:\n\n- `storage.objects.create`\n- `storage.objects.get`\n- `storage.objects.list`\n- `storage.objects.update`\n\nEnsure you have signed up for a Google Cloud account and created a project.\n1. Go to Speech in Google Cloud console, and navigate to [Speech-to-Text UI](https://console.cloud.google.com/speech).\n2. Using an audio file that is acoustically representative of your use case and how you are planning to use the ASR system, follow the quickstart instructions for making your first transcription using the [Speech-to-Text](https://cloud.google.com/speech-to-text/docs/transcribe-console).\n\nCalculating Transcription Accuracy\n----------------------------------\n\n1. After you have successfully transcribed your audio file, use the `Transcription Accuracy` section. This section remains empty until accuracy is calculated for your transcription.\n2. Using the **Upload Ground Truth** button at the top of the section, you can begin calculating accuracy.\n\nSpecifying ground truth\n-----------------------\n\n1. To calculate the accuracy of the transcription, provide a ground truth file. This is a `.txt` or `.csv` file, usually a human-generated transcription file that contains the correct or expected transcriptions for comparison.\n2. Using `gs://cloud-samples-data/speech/brooklyn_bridge.wav` as an example. The ground truth file contains: `How old is the Brooklyn Bridge`. If you don't have a ground truth file available, a recommendation is to download the transcription in a text format. Edit the transcription file as needed. Upload the transcription file as the ground truth file.\n3. Using **Upload** or an existing Cloud Storage file, specify the ground truth file, and click **Save** .\n\nConfirming ground truth\n-----------------------\n\n1. After clicking **Save**, a prompt displays to confirm that the specified ground truth file is correct. Verify that the ground truth file accurately represents the correct transcriptions, as it directly affects the accuracy metrics.\n2. Click **Confirm** to proceed.\n\nReview evaluation results\n-------------------------\n\n1. Depending on the size of the input data, the evaluation process might take some time, and the results are displayed upon completion.\n2. Once the evaluation is complete, the following sections are displayed:\n - The **Transcription Accuracy** table, the accuracy metrics, and a link to the ground truth file that were used in the process.\n - The `Transcription` with a toggle for comparing to the ground truth file along with a breakdown of accuracy metrics and highlights.\n3. Review and interpret the accuracy results to understand the performance of the Speech-to-Text recognizer that are used to identify areas for improvement, as the results vary depending on the inputs and transcription used. In the following examples, you can see indicative cases of the accuracy results, which provide valuable insights for optimization of the Google Cloud Speech-to-Text system.\n - An example of 0% WER:\n - An example of 40% WER:\n\nOptional: updating ground truth\n-------------------------------\n\nYou can test a different ground truth against the existing transcription, by reattaching a different file and then repeating steps three and four with an updated ground truth file.\n\nTry it for yourself\n-------------------\n\n\nIf you're new to Google Cloud, create an account to evaluate how\nSpeech-to-Text performs in real-world\nscenarios. New customers also get $300 in free credits to run, test, and\ndeploy workloads.\n[Try Speech-to-Text free](https://console.cloud.google.com/freetrial)"]]
