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How to use describe_language_model method in localstack

Best Python code snippet using localstack_python

conftest.py

Source:conftest.py

...179        headers = {"Content-Type": "application/json"}180        print("GET /service/transcribe/list_language_models")181        list_language_models_response = requests.get(self.stack_resources["WorkflowApiEndpoint"]+'/service/transcribe/list_language_models', headers=headers, verify=True, auth=self.auth)182        return list_language_models_response183    def describe_language_model(self, body):184        headers = {"Content-Type": "application/json"}185        print("POST /service/transcribe/describe_language_model")186        describe_language_model_response = requests.post(self.stack_resources["WorkflowApiEndpoint"]+'/service/transcribe/describe_language_model', headers=headers, json=body, verify=True, auth=self.auth)187        return describe_language_model_response188@pytest.fixture(scope='session', autouse=True)189def upload_media(testing_env_variables, stack_resources):190    print('Uploading Test Media')191    s3 = boto3.client('s3', region_name=testing_env_variables['REGION'])192    # Upload test media files193    s3.upload_file(testing_env_variables['MEDIA_PATH'] + testing_env_variables['TEST_PARALLEL_DATA'], stack_resources['DataplaneBucket'], testing_env_variables['TEST_PARALLEL_DATA'])194    # Wait for fixture to go out of scope:195    yield upload_media196@pytest.fixture(scope='session')197def terminology(workflow_api, stack_resources, testing_env_variables):...

transcribe.py

Source:transcribe.py

...43        44    # Checks training status45    def __check_clm_status(self):46        client = boto3.client('transcribe')47        status = client.describe_language_model(48            ModelName=self.clm_model_name49        )50        return status["LanguageModel"]['ModelStatus']51    52    # Method to call Transcribe's custom language model (CLM), when a trained custom language model is already available53    def __clmTranscribe(self, mediaS3, outBucket, outKey, jobName, clmModelName, langCode='en-US'):54        # Start transcription55        client = boto3.client('transcribe')56        response = client.start_transcription_job(57            TranscriptionJobName = jobName,58            LanguageCode = langCode, #'en-US'59            ModelSettings={60                'LanguageModelName': clmModelName61            },...

test_transcribe_proxy.py

Source:test_transcribe_proxy.py

...24    # Describe a custom language model if any exist.25    if len(response["Models"]) > 0:26        model_name = response["Models"][0]["ModelName"]27        body = {'ModelName': model_name}28        describe_language_model_response = workflow_api.describe_language_model(body)29        response = describe_language_model_response.json()30        assert "ModelName" in response["LanguageModel"]...

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