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    Recognizers

    Speech-to-Text V2 supports a Google Cloud resource called recognizers . Recognizers represent stored and reusable recognition configuration. You can use them to logically group together transcriptions or traffic for your application.

    Before you begin

    1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

    2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Go to project selector

    3. Verify that billing is enabled for your Google Cloud project .

    4. Enable the Speech-to-Text APIs.

      Enable the APIs

    5. Make sure that you have the following role or roles on the project: Cloud Speech Administrator

      Check for the roles

      1. In the Google Cloud console, go to the IAM page.

        Go to IAM
      2. Select the project.

      3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

      4. For all rows that specify or include you, check the Role column to see whether the list of roles includes the required roles.

      Grant the roles

      1. In the Google Cloud console, go to the IAM page.

        Go to IAM
      2. Select the project.
      3. Click Grant access .

      4. In the New principals field, enter your user identifier. This is typically the email address for a Google Account.

      5. In the Select a role list, select a role.
      6. To grant additional roles, click Add another role and add each additional role.
      7. Click Save .

    6. Install the Google Cloud CLI.

    7. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

    8. To initialize the gcloud CLI, run the following command:

      gcloud  
init

    9. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Go to project selector

    10. Verify that billing is enabled for your Google Cloud project .

    11. Enable the Speech-to-Text APIs.

      Enable the APIs

    12. Make sure that you have the following role or roles on the project: Cloud Speech Administrator

      Check for the roles

      1. In the Google Cloud console, go to the IAM page.

        Go to IAM
      2. Select the project.

      3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

      4. For all rows that specify or include you, check the Role column to see whether the list of roles includes the required roles.

      Grant the roles

      1. In the Google Cloud console, go to the IAM page.

        Go to IAM
      2. Select the project.
      3. Click Grant access .

      4. In the New principals field, enter your user identifier. This is typically the email address for a Google Account.

      5. In the Select a role list, select a role.
      6. To grant additional roles, click Add another role and add each additional role.
      7. Click Save .

    13. Install the Google Cloud CLI.

    14. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

    15. To initialize the gcloud CLI, run the following command:

      gcloud  
init

      16. Client libraries can use Application Default Credentials to easily authenticate with Google APIs and send requests to those APIs. With Application Default Credentials, you can test your application locally and deploy it without changing the underlying code. For more information, see Authenticate for using client libraries .

    16. If you're using a local shell, then create local authentication credentials for your user account:

      gcloud  
auth  
application-default  
login

      You don't need to do this if you're using Cloud Shell.

      If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

    Also ensure you have installed the client library .

    Understand recognizers

    Recognizers are configurable, reusable recognition configurations. Creating recognizers with frequently used recognition configuration helps to simplify and reduce the size of recognition requests.

    The core element of a recognizer is its default configuration . This is the configuration for every recognition request that this recognizer performs. You can override this default per request. Keep the default configuration for features you need across requests for a given recognizer, while overriding specific features for specific requests.

    Reuse recognizers as often as possible. Creating one for each request dramatically increases the latency of your application and consumes your resource quotas . Create them infrequently during integration and setup, then reuse them for recognition requests.

    Create recognizers

    Here is an example of creating a recognizer that can be used to send recognition requests:

    Python

      import 
  
 os 
 from 
  
 google.cloud.speech_v2 
  
 import 
 SpeechClient 
 from 
  
 google.cloud.speech_v2.types 
  
 import 
 cloud_speech 
 PROJECT_ID 
 = 
 os 
 . 
 getenv 
 ( 
 "GOOGLE_CLOUD_PROJECT" 
 ) 
 def 
  
 create_recognizer 
 ( 
 recognizer_id 
 : 
 str 
 ) 
 - 
> cloud_speech 
 . 
 Recognizer 
 : 
  
 """Сreates a recognizer with an unique ID and default recognition configuration. 
 Args: 
 recognizer_id (str): The unique identifier for the recognizer to be created. 
 Returns: 
 cloud_speech.Recognizer: The created recognizer object with configuration. 
 """ 
 # Instantiates a client 
 client 
 = 
 SpeechClient 
 () 
 request 
 = 
 cloud_speech 
 . 
 CreateRecognizerRequest 
 ( 
 parent 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global" 
 , 
 recognizer_id 
 = 
 recognizer_id 
 , 
 recognizer 
 = 
 cloud_speech 
 . 
 Recognizer 
 ( 
 default_recognition_config 
 = 
 cloud_speech 
 . 
 RecognitionConfig 
 ( 
 language_codes 
 = 
 [ 
 "en-US" 
 ], 
 model 
 = 
 "long" 
 ), 
 ), 
 ) 
 # Sends the request to create a recognizer and waits for the operation to complete 
 operation 
 = 
 client 
 . 
 create_recognizer 
 ( 
 request 
 = 
 request 
 ) 
 recognizer 
 = 
 operation 
 . 
 result 
 () 
 print 
 ( 
 "Created Recognizer:" 
 , 
 recognizer 
 . 
 name 
 ) 
 return 
 recognizer

    Use an existing recognizer to send requests

    Here is an example of sending multiple recognition requests using the same recognizer:

    Python

      import 
  
 os 
 from 
  
 google.cloud.speech_v2 
  
 import 
 SpeechClient 
 from 
  
 google.cloud.speech_v2.types 
  
 import 
 cloud_speech 
 PROJECT_ID 
 = 
 os 
 . 
 getenv 
 ( 
 "GOOGLE_CLOUD_PROJECT" 
 ) 
 def 
  
 transcribe_reuse_recognizer 
 ( 
 audio_file 
 : 
 str 
 , 
 recognizer_id 
 : 
 str 
 , 
 ) 
 - 
> cloud_speech 
 . 
 RecognizeResponse 
 : 
  
 """Transcribe an audio file using an existing recognizer. 
 Args: 
 audio_file (str): Path to the local audio file to be transcribed. 
 Example: "resources/audio.wav" 
 recognizer_id (str): The ID of the existing recognizer to be used for transcription. 
 Returns: 
 cloud_speech.RecognizeResponse: The response containing the transcription results. 
 """ 
 # Instantiates a client 
 client 
 = 
 SpeechClient 
 () 
 # Reads a file as bytes 
 with 
 open 
 ( 
 audio_file 
 , 
 "rb" 
 ) 
 as 
 f 
 : 
 audio_content 
 = 
 f 
 . 
 read 
 () 
 request 
 = 
 cloud_speech 
 . 
 RecognizeRequest 
 ( 
 recognizer 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global/recognizers/ 
 { 
 recognizer_id 
 } 
 " 
 , 
 content 
 = 
 audio_content 
 , 
 ) 
 # Transcribes the audio into text 
 response 
 = 
 client 
 . 
  recognize 
 
 ( 
 request 
 = 
 request 
 ) 
 for 
 result 
 in 
 response 
 . 
 results 
 : 
 print 
 ( 
 f 
 "Transcript: 
 { 
 result 
 . 
 alternatives 
 [ 
 0 
 ] 
 . 
 transcript 
 } 
 " 
 ) 
 return 
 response

    Enable features in a recognizer

    Recognizers can be used to enable various features in recognition, such as automatic punctuation or profanity filtering .

    Here is an example of enabling automatic punctuation in a recognizer, which enables automatic punctuation in the recognition request using this recognizer:

    Python

      from 
  
 google.cloud.speech_v2 
  
 import 
 SpeechClient 
 from 
  
 google.cloud.speech_v2.types 
  
 import 
 cloud_speech 
 from 
  
 google.api_core.exceptions 
  
 import 
 NotFound 
 # Instantiates a client 
 client 
 = 
 SpeechClient 
 () 
 # TODO(developer): Update and un-comment below line 
 # PROJECT_ID = "your-project-id" 
 # recognizer_id = "id-recognizer" 
 recognizer_name 
 = 
 ( 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global/recognizers/ 
 { 
 recognizer_id 
 } 
 " 
 ) 
 try 
 : 
 # Use an existing recognizer 
 recognizer 
 = 
 client 
 . 
  get_recognizer 
 
 ( 
 name 
 = 
 recognizer_name 
 ) 
 print 
 ( 
 "Using existing Recognizer:" 
 , 
 recognizer 
 . 
 name 
 ) 
 except 
 NotFound 
 : 
 # Create a new recognizer 
 request 
 = 
 cloud_speech 
 . 
  CreateRecognizerRequest 
 
 ( 
 parent 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global" 
 , 
 recognizer_id 
 = 
 recognizer_id 
 , 
 recognizer 
 = 
 cloud_speech 
 . 
  Recognizer 
 
 ( 
 default_recognition_config 
 = 
 cloud_speech 
 . 
 RecognitionConfig 
 ( 
 auto_decoding_config 
 = 
 cloud_speech 
 . 
  AutoDetectDecodingConfig 
 
 (), 
 language_codes 
 = 
 [ 
 "en-US" 
 ], 
 model 
 = 
 "latest_long" 
 , 
 features 
 = 
 cloud_speech 
 . 
  RecognitionFeatures 
 
 ( 
 enable_automatic_punctuation 
 = 
 True 
 , 
 ), 
 ), 
 ), 
 ) 
 operation 
 = 
 client 
 . 
  create_recognizer 
 
 ( 
 request 
 = 
 request 
 ) 
 recognizer 
 = 
 operation 
 . 
 result 
 () 
 print 
 ( 
 "Created Recognizer:" 
 , 
 recognizer 
 . 
 name 
 ) 
 # Reads a file as bytes 
 with 
 open 
 ( 
 audio_file 
 , 
 "rb" 
 ) 
 as 
 f 
 : 
 audio_content 
 = 
 f 
 . 
 read 
 () 
 request 
 = 
 cloud_speech 
 . 
 RecognizeRequest 
 ( 
 recognizer 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global/recognizers/ 
 { 
 recognizer_id 
 } 
 " 
 , 
 content 
 = 
 audio_content 
 , 
 ) 
 # Transcribes the audio into text 
 response 
 = 
 client 
 . 
  recognize 
 
 ( 
 request 
 = 
 request 
 ) 
 for 
 result 
 in 
 response 
 . 
 results 
 : 
 print 
 ( 
 f 
 "Transcript: 
 { 
 result 
 . 
 alternatives 
 [ 
 0 
 ] 
 . 
 transcript 
 } 
 " 
 )

    Override recognizer features in recognition requests

    Here is an example of enabling multiple features in a recognizer, but disabling automatic punctuation for this recognition request:

    Python

      import 
  
 os 
 from 
  
 google.cloud.speech_v2 
  
 import 
 SpeechClient 
 from 
  
 google.cloud.speech_v2.types 
  
 import 
 cloud_speech 
 from 
  
 google.protobuf.field_mask_pb2 
  
 import 
 FieldMask 
 PROJECT_ID 
 = 
 os 
 . 
 getenv 
 ( 
 "GOOGLE_CLOUD_PROJECT" 
 ) 
 def 
  
 transcribe_override_recognizer 
 ( 
 audio_file 
 : 
 str 
 , 
 recognizer_id 
 : 
 str 
 , 
 ) 
 - 
> cloud_speech 
 . 
 RecognizeResponse 
 : 
  
 """Transcribe an audio file using an existing recognizer with overridden settings for the recognition request. 
 Args: 
 audio_file (str): Path to the local audio file to be transcribed. 
 Example: "resources/audio.wav" 
 recognizer_id (str): The unique ID of the recognizer to be used for transcription. 
 Returns: 
 cloud_speech.RecognizeResponse: The response containing the transcription results. 
 """ 
 # Instantiates a client 
 client 
 = 
 SpeechClient 
 () 
 request 
 = 
 cloud_speech 
 . 
  CreateRecognizerRequest 
 
 ( 
 parent 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global" 
 , 
 recognizer_id 
 = 
 recognizer_id 
 , 
 recognizer 
 = 
 cloud_speech 
 . 
  Recognizer 
 
 ( 
 default_recognition_config 
 = 
 cloud_speech 
 . 
 RecognitionConfig 
 ( 
 auto_decoding_config 
 = 
 cloud_speech 
 . 
  AutoDetectDecodingConfig 
 
 (), 
 language_codes 
 = 
 [ 
 "en-US" 
 ], 
 model 
 = 
 "latest_long" 
 , 
 features 
 = 
 cloud_speech 
 . 
  RecognitionFeatures 
 
 ( 
 enable_automatic_punctuation 
 = 
 True 
 , 
 enable_word_time_offsets 
 = 
 True 
 , 
 ), 
 ), 
 ), 
 ) 
 operation 
 = 
 client 
 . 
  create_recognizer 
 
 ( 
 request 
 = 
 request 
 ) 
 recognizer 
 = 
 operation 
 . 
 result 
 () 
 print 
 ( 
 "Created Recognizer:" 
 , 
 recognizer 
 . 
 name 
 ) 
 # Reads a file as bytes 
 with 
 open 
 ( 
 audio_file 
 , 
 "rb" 
 ) 
 as 
 f 
 : 
 audio_content 
 = 
 f 
 . 
 read 
 () 
 request 
 = 
 cloud_speech 
 . 
 RecognizeRequest 
 ( 
 recognizer 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global/recognizers/ 
 { 
 recognizer_id 
 } 
 " 
 , 
 config 
 = 
 cloud_speech 
 . 
 RecognitionConfig 
 ( 
 features 
 = 
 cloud_speech 
 . 
  RecognitionFeatures 
 
 ( 
 enable_word_time_offsets 
 = 
 False 
 , 
 ), 
 ), 
 config_mask 
 = 
 FieldMask 
 ( 
 paths 
 = 
 [ 
 "features.enable_word_time_offsets" 
 ]), 
 content 
 = 
 audio_content 
 , 
 ) 
 # Transcribes the audio into text 
 response 
 = 
 client 
 . 
  recognize 
 
 ( 
 request 
 = 
 request 
 ) 
 for 
 result 
 in 
 response 
 . 
 results 
 : 
 print 
 ( 
 f 
 "Transcript: 
 { 
 result 
 . 
 alternatives 
 [ 
 0 
 ] 
 . 
 transcript 
 } 
 " 
 ) 
 return 
 response

    Send requests without recognizers

    Recognizers are optional in recognition requests. To make a request without a recognizer, simply use the recognizer resource ID _ in the location you are making a request. Here is an example:

    Python

      import 
  
 os 
 from 
  
 google.cloud.speech_v2 
  
 import 
 SpeechClient 
 from 
  
 google.cloud.speech_v2.types 
  
 import 
 cloud_speech 
 PROJECT_ID 
 = 
 os 
 . 
 getenv 
 ( 
 "GOOGLE_CLOUD_PROJECT" 
 ) 
 def 
  
 quickstart_v2 
 ( 
 audio_file 
 : 
 str 
 ) 
 - 
> cloud_speech 
 . 
 RecognizeResponse 
 : 
  
 """Transcribe an audio file. 
 Args: 
 audio_file (str): Path to the local audio file to be transcribed. 
 Returns: 
 cloud_speech.RecognizeResponse: The response from the recognize request, containing 
 the transcription results 
 """ 
 # Reads a file as bytes 
 with 
 open 
 ( 
 audio_file 
 , 
 "rb" 
 ) 
 as 
 f 
 : 
 audio_content 
 = 
 f 
 . 
 read 
 () 
 # Instantiates a client 
 client 
 = 
 SpeechClient 
 () 
 config 
 = 
 cloud_speech 
 . 
 RecognitionConfig 
 ( 
 auto_decoding_config 
 = 
 cloud_speech 
 . 
 AutoDetectDecodingConfig 
 (), 
 language_codes 
 = 
 [ 
 "en-US" 
 ], 
 model 
 = 
 "long" 
 , 
 ) 
 request 
 = 
 cloud_speech 
 . 
 RecognizeRequest 
 ( 
 recognizer 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/global/recognizers/_" 
 , 
 config 
 = 
 config 
 , 
 content 
 = 
 audio_content 
 , 
 ) 
 # Transcribes the audio into text 
 response 
 = 
 client 
 . 
 recognize 
 ( 
 request 
 = 
 request 
 ) 
 for 
 result 
 in 
 response 
 . 
 results 
 : 
 print 
 ( 
 f 
 "Transcript: 
 { 
 result 
 . 
 alternatives 
 [ 
 0 
 ] 
 . 
 transcript 
 } 
 " 
 ) 
 return 
 response

    Clean up

    To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

    1. Optional: Revoke the authentication credentials that you created, and delete the local credential file.

      gcloud  
auth  
application-default  
revoke

    2. Optional: Revoke credentials from the gcloud CLI.

      gcloud  
auth  
revoke

    Console

    • Everything in the project is deleted.If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
    • Custom project IDs are lost.When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

    If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

  • In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  • In the project list, select the project that you want to delete, and then click Delete .
  • In the dialog, type the project ID, and then click Shut down to delete the project.

    • gcloud

    • Everything in the project is deleted.If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
    • Custom project IDs are lost.When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

    If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

  • In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  • In the project list, select the project that you want to delete, and then click Delete .
  • In the dialog, type the project ID, and then click Shut down to delete the project.

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