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transcribe-dg-025	transcribe-dg.pdf	25	Amazon Resource Names (ARNs) uniquely identify all AWS resources, such as an Amazon S3 bucket. You can use ARNs in your policy to grant permissions for speciﬁc actions to use speciﬁc resources. For example, if you want to grant read access to an Amazon S3 bucket and its sub-folders, you can add the following code to your trust policy's Statement section: { Creating an IAM policy 128 Amazon Transcribe Developer Guide "Effect": "Allow", "Action": [ "s3:GetObject", "s3:ListBucket" ], "Resource": [ "arn:aws:s3:::amzn-s3-demo-bucket", "arn:aws:s3:::amzn-s3-demo-bucket/" ] } Here's an example policy that grants Amazon Transcribe read (GetObject, ListBucket) and write (PutObject) permissions to an Amazon S3 bucket, amzn-s3-demo-bucket, and its sub-folders: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:ListBucket" ], "Resource": [ "arn:aws:s3:::amzn-s3-demo-bucket", "arn:aws:s3:::amzn-s3-demo-bucket/" ] }, { "Effect": "Allow", "Action": [ "s3:PutObject" ], "Resource": [ "arn:aws:s3:::amzn-s3-demo-bucket", "arn:aws:s3:::amzn-s3-demo-bucket/*" ] } ] } Creating an IAM policy 129 Amazon Transcribe Developer Guide Transcribing with the AWS Management Console You can use the AWS console for batch and streaming transcriptions. If you're transcribing a media ﬁle located in an Amazon S3 bucket, you're performing a batch transcription. If you're transcribing a real-time stream of audio data, you're performing a streaming transcription. Before starting a batch transcription, you must ﬁrst upload your media ﬁle to an Amazon S3 bucket. For streaming transcriptions using the AWS Management Console, you must use your computer microphone. To view supported media formats and other media requirements and constraints, see Data input and output. Expand the following sections for short walkthroughs of each transcription method. Batch transcriptions First make sure that you've uploaded the media ﬁle you want to transcribe into an Amazon S3 bucket. If you're unsure how to do this, refer to the Amazon S3 User Guide: Upload an object to your bucket. 1. From the AWS Management Console, select Transcription jobs in the left navigation pane. This takes you to a list of your transcription jobs. Select Create job. 2. Complete the ﬁelds on the Specify job details page. Transcribing with the AWS Management Console 130 Amazon Transcribe Developer Guide The input location must be an object within an Amazon S3 bucket. For output location, you can choose a secure Amazon S3 service-managed bucket or you can specify your own Amazon S3 bucket. If you choose a service-managed bucket, you can view a transcript preview in the AWS Management Console and you can download your transcript from the job details page (see below). If you choose your own Amazon S3 bucket, you cannot see a preview in the AWS Management Console and must go to the Amazon S3 bucket to download your transcript. Transcribing with the AWS Management Console 131 Amazon Transcribe Developer Guide Select Next. 3. Select any desired options on the Conﬁgure job page. If you want to use Custom vocabularies or Custom language models with your transcription, you must create these before starting your transcription job. Transcribing with the AWS Management Console 132 Amazon Transcribe Developer Guide Select Create job. 4. You're now on the Transcription jobs page. Here you can see the status of the transcription job. Once complete, select your transcription. Transcribing with the AWS Management Console 133 Amazon Transcribe Developer Guide 5. You're now viewing the Job details page for your transcription. Here you can view all of the options you speciﬁed when setting up your transcription job. To view your transcript, select the linked ﬁlepath in the right column under Output data location. This takes you to the Amazon S3 output folder you speciﬁed. Select your output ﬁle, which now has a .json extension. 6. How you download your transcript depends on whether you chose a service-managed Amazon S3 bucket or your own Amazon S3 bucket. a. If you chose a service-managed bucket, you can see a Transcription preview pane on your transcription job's information page, along with a Download button. Transcribing with the AWS Management Console 134 Amazon Transcribe Developer Guide Select Download and choose Download transcript. b. If you chose your own Amazon S3 bucket, you don't see any text in the Transcription preview pane on your transcription job's information page. Instead, you see a blue information box with a link to the Amazon S3 bucket you chose. Transcribing with the AWS Management Console 135 Amazon Transcribe Developer Guide To access your transcript, go to the speciﬁed Amazon S3 bucket using the link under Output data location in the Job details pane or the S3 Bucket link within the blue information box in the Transcription preview pane. Streaming transcriptions 1. From the AWS Management Console, select Real-time transcription in the left navigation pane. This takes you to the main streaming page where you can select options before starting your stream. Transcribing with the AWS Management Console 136 Amazon Transcribe Developer Guide 2. Below the Transcription output
transcribe-dg-026	transcribe-dg.pdf	26	you chose. Transcribing with the AWS Management Console 135 Amazon Transcribe Developer Guide To access your transcript, go to the speciﬁed Amazon S3 bucket using the link under Output data location in the Job details pane or the S3 Bucket link within the blue information box in the Transcription preview pane. Streaming transcriptions 1. From the AWS Management Console, select Real-time transcription in the left navigation pane. This takes you to the main streaming page where you can select options before starting your stream. Transcribing with the AWS Management Console 136 Amazon Transcribe Developer Guide 2. Below the Transcription output box, you have the option to select various language and audio settings. Transcribing with the AWS Management Console 137 Amazon Transcribe Developer Guide 3. After you've selected the appropriate settings, scroll to the top of the page and choose Start streaming, then begin speaking into your computer microphone. You can see your speech transcribed in real time. 4. When you're ﬁnished, select Stop streaming. You can now download your transcript by selecting Download full transcript. Transcribing with the AWS Management Console 138 Amazon Transcribe Developer Guide Transcribing with the AWS CLI When using the AWS CLI to start a transcription, you can run all commands at the CLI level. Or you can run the command you want to use, followed by the AWS Region and the location of a JSON ﬁle that contains a request body. Examples throughout this guide show both methods; however, this section focuses on the former method. The AWS CLI does not support streaming transcriptions. Before you continue, make sure you've: • Uploaded your media ﬁle into an Amazon S3 bucket. If you're unsure how to create an Amazon S3 bucket or upload your ﬁle, refer to Create your ﬁrst Amazon S3 bucket and Upload an object to your bucket. • Installed the AWS CLI. You can ﬁnd all AWS CLI commands for Amazon Transcribe in the AWS CLI Command Reference. Starting a new transcription job To start a new transcription, use the start-transcription-job command. 1. In a terminal window, type the following: aws transcribe start-transcription-job \ A '>' appears on the next line, and you can now continue adding required parameters, as described in the next step. You can also omit the '\' and append all parameters, separating each with a space. 2. With the start-transcription-job command, you must include region, transcription-job-name, media, and either language-code or identify-language. If you want to specify an output location, include output-bucket-name in your request; if you want to specify a sub-folder of the speciﬁed output bucket, also include output-key. aws transcribe start-transcription-job \ --region us-west-2 \ Transcribing with the AWS CLI 139 Amazon Transcribe Developer Guide --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --language-code en-US If appending all parameters, this request looks like: aws transcribe start-transcription-job --region us-west-2 --transcription-job- name my-first-transcription-job --media MediaFileUri=s3://amzn-s3-demo-bucket/my- input-files/my-media-file.flac --language-code en-US If you choose not to specify an output bucket using output-bucket-name, Amazon Transcribe places your transcription output in a service-managed bucket. Transcripts stored in a service-managed bucket expire after 90 days. Amazon Transcribe responds with: { "TranscriptionJob": { "TranscriptionJobName": "my-first-transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "en-US", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media- file.flac" }, "StartTime": "2022-03-07T15:03:44.246000-08:00", "CreationTime": "2022-03-07T15:03:44.229000-08:00" } } Your transcription job is successful if TranscriptionJobStatus changes from IN_PROGRESS to COMPLETED. To see the updated TranscriptionJobStatus, use the get-transcription-job or list-transcription-job command, as shown in the following section. Getting the status of a transcription job To get information about your transcription job, use the get-transcription-job command. The only required parameters for this command are the AWS Region where the job is located and the name of the job. Getting the status of a transcription job 140 Amazon Transcribe Developer Guide aws transcribe get-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job Amazon Transcribe responds with: { "TranscriptionJob": { "TranscriptionJobName": "my-first-transcription-job", "TranscriptionJobStatus": "COMPLETED", "LanguageCode": "en-US", "MediaSampleRateHertz": 48000, "MediaFormat": "flac", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media- file.flac" }, "Transcript": { "TranscriptFileUri": "https://s3.the-URI-where-your-job-is-located.json" }, "StartTime": "2022-03-07T15:03:44.246000-08:00", "CreationTime": "2022-03-07T15:03:44.229000-08:00", "CompletionTime": "2022-03-07T15:04:01.158000-08:00", "Settings": { "ChannelIdentification": false, "ShowAlternatives": false } } } If you've selected your own Amazon S3 bucket for your transcription output, this bucket is listed with TranscriptFileUri. If you've selected a service-managed bucket, a temporary URI is provided; use this URI to download your transcript. Note Temporary URIs for service-managed Amazon S3 buckets are only valid for 15 minutes. If you get an AccesDenied error when using the URI, run the get-transcription-job request again to get a new temporary URI. Getting the status of a transcription job 141 Amazon Transcribe Developer Guide Listing your transcription jobs To list all your transcription jobs in a given AWS Region, use the list-transcription-jobs command. The only required parameter for this command is the AWS Region in which your transcription jobs are located. aws transcribe list-transcription-jobs \ --region us-west-2 Amazon Transcribe responds with: { "NextToken": "A-very-long-string", "TranscriptionJobSummaries":
transcribe-dg-027	transcribe-dg.pdf	27	your transcript. Note Temporary URIs for service-managed Amazon S3 buckets are only valid for 15 minutes. If you get an AccesDenied error when using the URI, run the get-transcription-job request again to get a new temporary URI. Getting the status of a transcription job 141 Amazon Transcribe Developer Guide Listing your transcription jobs To list all your transcription jobs in a given AWS Region, use the list-transcription-jobs command. The only required parameter for this command is the AWS Region in which your transcription jobs are located. aws transcribe list-transcription-jobs \ --region us-west-2 Amazon Transcribe responds with: { "NextToken": "A-very-long-string", "TranscriptionJobSummaries": [ { "TranscriptionJobName": "my-first-transcription-job", "CreationTime": "2022-03-07T15:03:44.229000-08:00", "StartTime": "2022-03-07T15:03:44.246000-08:00", "CompletionTime": "2022-03-07T15:04:01.158000-08:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "SERVICE_BUCKET" } ] } Deleting your transcription job To delete your transcription job, use the delete-transcription-job command. The only required parameters for this command are the AWS Region where the job is located and the name of the job. aws transcribe delete-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job To conﬁrm your delete request is successful, you can run the list-transcription-jobs command. Your job should no longer appear in the list. Listing your transcription jobs 142 Amazon Transcribe Developer Guide Transcribing with the AWS SDKs You can use SDKs for both batch and streaming transcriptions. If you're transcribing a media ﬁle located in an Amazon S3 bucket, you're performing a batch transcription. If you're transcribing a real-time stream of audio data, you're performing a streaming transcription. For a list of the programming languages you can use with Amazon Transcribe, see Supported programming languages. Note that streaming transcriptions are not supported with all AWS SDKs. To view supported media formats and other media requirements and constraints, see Data input and output. For more information on all available AWS SDKs and builder tools, refer to Tools to Build on AWS. Tip For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. You can also ﬁnd SDK code samples in these GitHub repositories: • AWS Code Examples • Amazon Transcribe Examples Batch transcriptions You can create batch transcriptions using the URI of a media ﬁle located in an Amazon S3 bucket. If you're unsure how to create an Amazon S3 bucket or upload your ﬁle, refer to Create your ﬁrst S3 bucket and Upload an object to your bucket. Java import software.amazon.awssdk.auth.credentials.AwsCredentialsProvider; import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.transcribe.TranscribeClient; import software.amazon.awssdk.services.transcribe.model.*; import software.amazon.awssdk.services.transcribestreaming.model.LanguageCode; public class TranscribeDemoApp { Transcribing with the AWS SDKs 143 Amazon Transcribe Developer Guide private static final Region REGION = Region.US_WEST_2; private static TranscribeClient client; public static void main(String args[]) { client = TranscribeClient.builder() .credentialsProvider(getCredentials()) .region(REGION) .build(); String transcriptionJobName = "my-first-transcription-job"; String mediaType = "flac"; // can be other types Media myMedia = Media.builder() .mediaFileUri("s3://amzn-s3-demo-bucket/my-input-files/my-media- file.flac") .build(); String outputS3BucketName = "s3://amzn-s3-demo-bucket"; // Create the transcription job request StartTranscriptionJobRequest request = StartTranscriptionJobRequest.builder() .transcriptionJobName(transcriptionJobName) .languageCode(LanguageCode.EN_US.toString()) .mediaSampleRateHertz(16000) .mediaFormat(mediaType) .media(myMedia) .outputBucketName(outputS3BucketName) .build(); // send the request to start the transcription job StartTranscriptionJobResponse startJobResponse = client.startTranscriptionJob(request); System.out.println("Created the transcription job"); System.out.println(startJobResponse.transcriptionJob()); // Create the get job request GetTranscriptionJobRequest getJobRequest = GetTranscriptionJobRequest.builder() .transcriptionJobName(transcriptionJobName) .build(); // send the request to get the transcription job including the job status Transcribing with the AWS SDKs 144 Amazon Transcribe Developer Guide GetTranscriptionJobResponse getJobResponse = client.getTranscriptionJob(getJobRequest); System.out.println("Get the transcription job request"); System.out.println(getJobResponse.transcriptionJob()); } private static AwsCredentialsProvider getCredentials() { return DefaultCredentialsProvider.create(); } } JavaScript const { TranscribeClient, StartTranscriptionJobCommand } = require("@aws-sdk/client- transcribe"); // CommonJS import const region = "us-west-2"; const credentials = { "accessKeyId": "", "secretAccessKey": "", }; const input = { TranscriptionJobName: "my-first-transcription-job", LanguageCode: "en-US", Media: { MediaFileUri: "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, OutputBucketName: "amzn-s3-demo-bucket", }; async function startTranscriptionRequest() { const transcribeConfig = { region, credentials }; const transcribeClient = new TranscribeClient(transcribeConfig); const transcribeCommand = new StartTranscriptionJobCommand(input); try { const transcribeResponse = await transcribeClient.send(transcribeCommand); console.log("Transcription job created, the details:"); Transcribing with the AWS SDKs 145 Amazon Transcribe Developer Guide console.log(transcribeResponse.TranscriptionJob); } catch(err) { console.log(err); } } startTranscriptionRequest(); Python import time import boto3 def transcribe_file(job_name, file_uri, transcribe_client): transcribe_client.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': file_uri }, MediaFormat = 'flac', LanguageCode = 'en-US' ) max_tries = 60 while max_tries > 0: max_tries -= 1 job = transcribe_client.get_transcription_job(TranscriptionJobName = job_name) job_status = job['TranscriptionJob']['TranscriptionJobStatus'] if job_status in ['COMPLETED', 'FAILED']: print(f"Job {job_name} is {job_status}.") if job_status == 'COMPLETED': print( f"Download the transcript from\n" f"\t{job['TranscriptionJob']['Transcript'] ['TranscriptFileUri']}.") break else: print(f"Waiting for {job_name}. Current status is {job_status}.") time.sleep(10) def main(): Transcribing with the AWS SDKs 146 Amazon Transcribe Developer Guide transcribe_client = boto3.client('transcribe', region_name = 'us-west-2') file_uri = 's3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac' transcribe_file('Example-job', file_uri, transcribe_client) if __name__ == '__main__': main() Streaming transcriptions You can create streaming transcriptions using a streamed media ﬁle or a live media stream. Note that the standard AWS SDK for Python (Boto3) is not supported for Amazon Transcribe streaming. To start a streaming transcription using Python, use this async Python SDK for
transcribe-dg-028	transcribe-dg.pdf	28	print(f"Job {job_name} is {job_status}.") if job_status == 'COMPLETED': print( f"Download the transcript from\n" f"\t{job['TranscriptionJob']['Transcript'] ['TranscriptFileUri']}.") break else: print(f"Waiting for {job_name}. Current status is {job_status}.") time.sleep(10) def main(): Transcribing with the AWS SDKs 146 Amazon Transcribe Developer Guide transcribe_client = boto3.client('transcribe', region_name = 'us-west-2') file_uri = 's3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac' transcribe_file('Example-job', file_uri, transcribe_client) if __name__ == '__main__': main() Streaming transcriptions You can create streaming transcriptions using a streamed media ﬁle or a live media stream. Note that the standard AWS SDK for Python (Boto3) is not supported for Amazon Transcribe streaming. To start a streaming transcription using Python, use this async Python SDK for Amazon Transcribe. Java The following example is a Java program that transcribes streaming audio. To run this example, note the following: • You must use the AWS SDK for Java 2.x. • Clients must use Java 1.8 to be compatible with the AWS SDK for Java 2.x. • The sample rate you specify must match the actual sample rate of your audio stream. See also: Retry client for Amazon Transcribe streaming (Java SDK). This code manages the connection to Amazon Transcribe and retries sending data when there are errors on the connection. For example, if there is a transient error on the network, this client resends the request that failed. public class TranscribeStreamingDemoApp { private static final Region REGION = Region.US_WEST_2; private static TranscribeStreamingAsyncClient client; public static void main(String args[]) throws URISyntaxException, ExecutionException, InterruptedException, LineUnavailableException { client = TranscribeStreamingAsyncClient.builder() .credentialsProvider(getCredentials()) Transcribing with the AWS SDKs 147 Amazon Transcribe Developer Guide .region(REGION) .build(); CompletableFuture<Void> result = client.startStreamTranscription(getRequest(16_000), new AudioStreamPublisher(getStreamFromMic()), getResponseHandler()); result.get(); client.close(); } private static InputStream getStreamFromMic() throws LineUnavailableException { // Signed PCM AudioFormat with 16,000 Hz, 16 bit sample size, mono int sampleRate = 16000; AudioFormat format = new AudioFormat(sampleRate, 16, 1, true, false); DataLine.Info info = new DataLine.Info(TargetDataLine.class, format); if (!AudioSystem.isLineSupported(info)) { System.out.println("Line not supported"); System.exit(0); } TargetDataLine line = (TargetDataLine) AudioSystem.getLine(info); line.open(format); line.start(); InputStream audioStream = new AudioInputStream(line); return audioStream; } private static AwsCredentialsProvider getCredentials() { return DefaultCredentialsProvider.create(); } private static StartStreamTranscriptionRequest getRequest(Integer mediaSampleRateHertz) { return StartStreamTranscriptionRequest.builder() .languageCode(LanguageCode.EN_US.toString()) .mediaEncoding(MediaEncoding.PCM) .mediaSampleRateHertz(mediaSampleRateHertz) .build(); } Transcribing with the AWS SDKs 148 Amazon Transcribe Developer Guide private static StartStreamTranscriptionResponseHandler getResponseHandler() { return StartStreamTranscriptionResponseHandler.builder() .onResponse(r -> { System.out.println("Received Initial response"); }) .onError(e -> { System.out.println(e.getMessage()); StringWriter sw = new StringWriter(); e.printStackTrace(new PrintWriter(sw)); System.out.println("Error Occurred: " + sw.toString()); }) .onComplete(() -> { System.out.println("=== All records stream successfully ==="); }) .subscriber(event -> { List<Result> results = ((TranscriptEvent) event).transcript().results(); if (results.size() > 0) { if (! results.get(0).alternatives().get(0).transcript().isEmpty()) { System.out.println(results.get(0).alternatives().get(0).transcript()); } } }) .build(); } private InputStream getStreamFromFile(String myMediaFileName) { try { File inputFile = new File(getClass().getClassLoader().getResource(myMediaFileName).getFile()); InputStream audioStream = new FileInputStream(inputFile); return audioStream; } catch (FileNotFoundException e) { throw new RuntimeException(e); } } private static class AudioStreamPublisher implements Publisher<AudioStream> { private final InputStream inputStream; private static Subscription currentSubscription; Transcribing with the AWS SDKs 149 Amazon Transcribe Developer Guide private AudioStreamPublisher(InputStream inputStream) { this.inputStream = inputStream; } @Override public void subscribe(Subscriber<? super AudioStream> s) { if (this.currentSubscription == null) { this.currentSubscription = new SubscriptionImpl(s, inputStream); } else { this.currentSubscription.cancel(); this.currentSubscription = new SubscriptionImpl(s, inputStream); } s.onSubscribe(currentSubscription); } } public static class SubscriptionImpl implements Subscription { private static final int CHUNK_SIZE_IN_BYTES = 1024 * 1; private final Subscriber<? super AudioStream> subscriber; private final InputStream inputStream; private ExecutorService executor = Executors.newFixedThreadPool(1); private AtomicLong demand = new AtomicLong(0); SubscriptionImpl(Subscriber<? super AudioStream> s, InputStream inputStream) { this.subscriber = s; this.inputStream = inputStream; } @Override public void request(long n) { if (n <= 0) { subscriber.onError(new IllegalArgumentException("Demand must be positive")); } demand.getAndAdd(n); executor.submit(() -> { try { do { ByteBuffer audioBuffer = getNextEvent(); Transcribing with the AWS SDKs 150 Amazon Transcribe Developer Guide if (audioBuffer.remaining() > 0) { AudioEvent audioEvent = audioEventFromBuffer(audioBuffer); subscriber.onNext(audioEvent); } else { subscriber.onComplete(); break; } } while (demand.decrementAndGet() > 0); } catch (Exception e) { subscriber.onError(e); } }); } @Override public void cancel() { executor.shutdown(); } private ByteBuffer getNextEvent() { ByteBuffer audioBuffer = null; byte[] audioBytes = new byte[CHUNK_SIZE_IN_BYTES]; int len = 0; try { len = inputStream.read(audioBytes); if (len <= 0) { audioBuffer = ByteBuffer.allocate(0); } else { audioBuffer = ByteBuffer.wrap(audioBytes, 0, len); } } catch (IOException e) { throw new UncheckedIOException(e); } return audioBuffer; } private AudioEvent audioEventFromBuffer(ByteBuffer bb) { return AudioEvent.builder() .audioChunk(SdkBytes.fromByteBuffer(bb)) .build(); Transcribing with the AWS SDKs 151 Amazon Transcribe } } } JavaScript const { Developer Guide TranscribeStreamingClient, StartStreamTranscriptionCommand, } = require("@aws-sdk/client-transcribe-streaming"); const { createReadStream } = require("fs"); const { join } = require("path"); const audio = createReadStream(join(__dirname, "my-media-file.flac"), { highWaterMark: 1024 * 16}); const LanguageCode = "en-US"; const MediaEncoding = "pcm"; const MediaSampleRateHertz = "16000"; const credentials = { "accessKeyId": "", "secretAccessKey": "", }; async function startRequest() { const client = new TranscribeStreamingClient({ region: "us-west-2", credentials }); const params = { LanguageCode, MediaEncoding, MediaSampleRateHertz, AudioStream: (async function* () { for await (const chunk of audio) { yield {AudioEvent: {AudioChunk: chunk}}; } })(),
transcribe-dg-029	transcribe-dg.pdf	29	the AWS SDKs 151 Amazon Transcribe } } } JavaScript const { Developer Guide TranscribeStreamingClient, StartStreamTranscriptionCommand, } = require("@aws-sdk/client-transcribe-streaming"); const { createReadStream } = require("fs"); const { join } = require("path"); const audio = createReadStream(join(__dirname, "my-media-file.flac"), { highWaterMark: 1024 * 16}); const LanguageCode = "en-US"; const MediaEncoding = "pcm"; const MediaSampleRateHertz = "16000"; const credentials = { "accessKeyId": "", "secretAccessKey": "", }; async function startRequest() { const client = new TranscribeStreamingClient({ region: "us-west-2", credentials }); const params = { LanguageCode, MediaEncoding, MediaSampleRateHertz, AudioStream: (async function* () { for await (const chunk of audio) { yield {AudioEvent: {AudioChunk: chunk}}; } })(), }; const command = new StartStreamTranscriptionCommand(params); // Send transcription request const response = await client.send(command); // Start to print response Transcribing with the AWS SDKs 152 Amazon Transcribe try { for await (const event of response.TranscriptResultStream) { console.log(JSON.stringify(event)); Developer Guide } } catch(err) { console.log("error") console.log(err) } } startRequest(); Python The following example is a Python program that transcribes streaming audio. To run this example, note the following: • You must use this SDK for Python . • The sample rate you specify must match the actual sample rate of your audio stream. import asyncio # This example uses aiofile for asynchronous file reads. # It's not a dependency of the project but can be installed # with `pip install aiofile`. import aiofile from amazon_transcribe.client import TranscribeStreamingClient from amazon_transcribe.handlers import TranscriptResultStreamHandler from amazon_transcribe.model import TranscriptEvent """ Here's an example of a custom event handler you can extend to process the returned transcription results as needed. This handler will simply print the text out to your interpreter. """ class MyEventHandler(TranscriptResultStreamHandler): async def handle_transcript_event(self, transcript_event: TranscriptEvent): # This handler can be implemented to handle transcriptions as needed. # Here's an example to get started. results = transcript_event.transcript.results for result in results: for alt in result.alternatives: Transcribing with the AWS SDKs 153 Amazon Transcribe Developer Guide print(alt.transcript) async def basic_transcribe(): # Set up our client with your chosen Region client = TranscribeStreamingClient(region = "us-west-2") # Start transcription to generate async stream stream = await client.start_stream_transcription( language_code = "en-US", media_sample_rate_hz = 16000, media_encoding = "pcm", ) async def write_chunks(): # NOTE: For pre-recorded files longer than 5 minutes, the sent audio # chunks should be rate limited to match the real-time bitrate of the # audio stream to avoid signing issues. async with aiofile.AIOFile('filepath/my-media-file.flac', 'rb') as afp: reader = aiofile.Reader(afp, chunk_size = 1024 * 16) async for chunk in reader: await stream.input_stream.send_audio_event(audio_chunk = chunk) await stream.input_stream.end_stream() # Instantiate our handler and start processing events handler = MyEventHandler(stream.output_stream) await asyncio.gather(write_chunks(), handler.handle_events()) loop = asyncio.get_event_loop() loop.run_until_complete(basic_transcribe()) loop.close() C++ Refer to the Code examples chapter for the streaming C++ SDK example . Using this service with an AWS SDK AWS software development kits (SDKs) are available for many popular programming languages. Each SDK provides an API, code examples, and documentation that make it easier for developers to build applications in their preferred language. Working with AWS SDKs 154 Amazon Transcribe Developer Guide SDK documentation Code examples AWS SDK for C++ AWS SDK for C++ code examples AWS CLI AWS SDK for Go AWS SDK for Java AWS CLI code examples AWS SDK for Go code examples AWS SDK for Java code examples AWS SDK for JavaScript AWS SDK for JavaScript code examples AWS SDK for Kotlin AWS SDK for Kotlin code examples AWS SDK for .NET AWS SDK for PHP AWS SDK for .NET code examples AWS SDK for PHP code examples AWS Tools for PowerShell Tools for PowerShell code examples AWS SDK for Python (Boto3) AWS SDK for Python (Boto3) code examples AWS SDK for Ruby AWS SDK for Rust AWS SDK for Ruby code examples AWS SDK for Rust code examples AWS SDK for SAP ABAP AWS SDK for SAP ABAP code examples AWS SDK for Swift AWS SDK for Swift code examples For examples speciﬁc to this service, see Code examples for Amazon Transcribe using AWS SDKs. Example availability Can't ﬁnd what you need? Request a code example by using the Provide feedback link at the bottom of this page. Working with AWS SDKs 155 Amazon Transcribe Developer Guide Transcribing with HTTP or WebSockets Amazon Transcribe supports HTTP for both batch (HTTP/1.1) and streaming (HTTP/2) transcriptions. WebSockets are supported for streaming transcriptions. If you're transcribing a media ﬁle located in an Amazon S3 bucket, you're performing a batch transcription. If you're transcribing a real-time stream of audio data, you're performing a streaming transcription. Both HTTP and WebSockets require you to authenticate your request using AWS Signature Version 4 headers. Refer to Signing AWS API requests for more information. Batch transcriptions You can make a batch HTTP request using the following headers: • host • x-amz-target • content-type • x-amz-content-sha256 • x-amz-date • authorization Here's an example of a StartTranscriptionJob request: POST /transcribe HTTP/1.1 host:
transcribe-dg-030	transcribe-dg.pdf	30	(HTTP/2) transcriptions. WebSockets are supported for streaming transcriptions. If you're transcribing a media ﬁle located in an Amazon S3 bucket, you're performing a batch transcription. If you're transcribing a real-time stream of audio data, you're performing a streaming transcription. Both HTTP and WebSockets require you to authenticate your request using AWS Signature Version 4 headers. Refer to Signing AWS API requests for more information. Batch transcriptions You can make a batch HTTP request using the following headers: • host • x-amz-target • content-type • x-amz-content-sha256 • x-amz-date • authorization Here's an example of a StartTranscriptionJob request: POST /transcribe HTTP/1.1 host: transcribe.us-west-2.amazonaws.com x-amz-target: com.amazonaws.transcribe.Transcribe.StartTranscriptionJob content-type: application/x-amz-json-1.1 x-amz-content-sha256: string x-amz-date: YYYYMMDDTHHMMSSZ authorization: AWS4-HMAC-SHA256 Credential=access-key/YYYYMMSS/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string { "TranscriptionJobName": "my-first-transcription-job", "LanguageCode": "en-US", "Media": { Transcribing with HTTP or WebSockets 156 Amazon Transcribe Developer Guide "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/" } Additional operations and parameters are listed in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Other signature elements are detailed in Elements of an AWS Signature Version 4 request. Streaming transcriptions Streaming transcriptions using HTTP/2 and WebSockets are more involved than using SDKs. We recommend reviewing the Setting up a streaming transcription section before setting up your ﬁrst stream. For more information on these methods, refer to Setting up an HTTP/2 stream or Setting up a WebSocket stream. Note We strongly recommend using an SDK for streaming transcriptions. For a list of supported SDKs, refer to Supported programming languages. Transcribing with HTTP or WebSockets 157 Amazon Transcribe Developer Guide Transcribing streaming audio Using Amazon Transcribe streaming, you can produce real-time transcriptions for your media content. Unlike batch transcriptions, which involve uploading media ﬁles, streaming media is delivered to Amazon Transcribe in real time. Amazon Transcribe then returns a transcript, also in real time. Streaming can include pre-recorded media (movies, music, and podcasts) and real-time media (live news broadcasts). Common streaming use cases for Amazon Transcribe include live closed captioning for sporting events and real-time monitoring of call center audio. Streaming content is delivered as a series of sequential data packets, or 'chunks,' that Amazon Transcribe transcribes instantaneously. The advantages of using streaming over batch include real- time speech-to-text capabilities in your applications and faster transcription times. However, this increased speed may have accuracy limitations in some cases. Amazon Transcribe oﬀers the following options for streaming: • SDKs (preferred) • HTTP/2 • WebSockets • AWS Management Console To transcribe streaming audio in the AWS Management Console, speak into your computer microphone. Tip For SDK code examples, refer to the AWS Samples repository on GitHub. Audio formats supported for streaming transcriptions are: • FLAC • OPUS-encoded audio in an Ogg container • PCM (only signed 16-bit little-endian audio formats, which does not include WAV) 158 Amazon Transcribe Developer Guide Lossless formats (FLAC or PCM) are recommended. Note Streaming transcriptions are not supported with all languages. Refer to the 'Data input' column in the supported languages table for details. To view the Amazon Transcribe Region availability for streaming transcriptions, see: Amazon Transcribe Endpoints and Quotas. Best practices The following recommendations improve streaming transcription eﬃciency: • If possible, use PCM-encoded audio. • Ensure that your stream is as close to real-time as possible. • Latency depends on the size of your audio chunks. If you're able to specify chunk size with your audio type (such as with PCM), set each chunk to between 50 ms and 200 ms. You can calculate the audio chunk size by the following formula: chunk_size_in_bytes = chunk_duration_in_millisecond / 1000 * audio_sample_rate * 2 • Use a uniform chunk size. • Make sure you correctly specify the number of audio channels. • With single-channel PCM audio, each sample consists of two bytes, so each chunk should consist of an even number of bytes. • With dual-channel PCM audio, each sample consists of four bytes, so each chunk should be a multiple of 4 bytes. • When your audio stream contains no speech, encode and send the same amount of silence. For example, silence for PCM is a stream of zero bytes. • Make sure you specify the correct sampling rate for your audio. If possible, record at a sampling rate of 16,000 Hz; this provides the best compromise between quality and data volume sent over the network. Note that most high-end microphones record at 44,100 Hz or 48,000 Hz. Best practices 159 Amazon Transcribe Developer Guide Streaming and partial results Because streaming works in real time, transcripts are produced in partial results. Amazon Transcribe breaks up the incoming audio stream based on natural speech segments, such as a change in speaker or a pause in the audio. The transcription is returned to your application in a stream of transcription events, with each response containing more transcribed speech until an entire
transcribe-dg-031	transcribe-dg.pdf	31	of 16,000 Hz; this provides the best compromise between quality and data volume sent over the network. Note that most high-end microphones record at 44,100 Hz or 48,000 Hz. Best practices 159 Amazon Transcribe Developer Guide Streaming and partial results Because streaming works in real time, transcripts are produced in partial results. Amazon Transcribe breaks up the incoming audio stream based on natural speech segments, such as a change in speaker or a pause in the audio. The transcription is returned to your application in a stream of transcription events, with each response containing more transcribed speech until an entire segment is transcribed. An approximation of this is shown in the following code block. You can view this process in action by signing into the AWS Management Console, selecting Real-time transcription, and speaking into your microphone. Watch the Transcription output pane as you speak. In this example, each line is the partial result of an audio segment. The The Amazon. The Amazon is The Amazon is the law. The Amazon is the largest The Amazon is the largest ray The Amazon is the largest rain for The Amazon is the largest rainforest. The Amazon is the largest rainforest on the The Amazon is the largest rainforest on the planet. These partial results are present in your transcription output within the Results objects. Also in this object block is an IsPartial ﬁeld. If this ﬁeld is true, your transcription segment is not yet complete. You can view the diﬀerence between an incomplete and a complete segment below: "IsPartial": true (incomplete segment) "Transcript": "The Amazon is the largest rainforest." "EndTime": 4.545, "IsPartial": true, "ResultId": "12345a67-8bc9-0de1-2f34-a5b678c90d12", "StartTime": 0.025 "IsPartial": false (complete segment) Streaming and partial results 160 Amazon Transcribe Developer Guide "Transcript": "The Amazon is the largest rainforest on the planet." "EndTime": 6.025, "IsPartial": false, "ResultId": "34567e89-0fa1-2bc3-4d56-78e90123456f", "StartTime": 0.025 Each word within a complete segment has an associated conﬁdence score, which is a value between 0 and 1. A larger value indicates a greater likelihood that the word is correctly transcribed. Tip The StartTime and EndTime of an audio segment can be used to synchronize transcription output with video dialogue. If you're running an application that requires low latency, you may want to use partial-result stabilization. Partial-result stabilization Amazon Transcribe starts returning transcription results as soon as you start streaming your audio. It returns these partial results incrementally until it generates a ﬁnished result at the level of a natural speech segment. A natural speech segment is continuous speech that contains a pause or a change in speaker. Amazon Transcribe continues outputting partial results until it generates the ﬁnal transcription result for a speech segment. Because speech recognition may revise words as it gains more context, streaming transcriptions can change slightly with each new partial result output. This process gives you two options for each speech segment: • Wait for the ﬁnished segment • Use the segment's partial results Partial result stabilization changes how Amazon Transcribe produces the ﬁnal transcription result for each complete segment. When activated, only the last few words from the partial results can change. Because of this, transcription accuracy may be aﬀected. However, your transcript Partial-result stabilization 161 Amazon Transcribe Developer Guide is returned faster than without partial-results stabilization. This reduction in latency may be beneﬁcial when subtitling videos or generating captions for live streams. The following examples show how the same audio stream is handled when partial-results stabilization is not activated and when it is. Note that you can set the stability level to low, medium, or high. Low stability provides the highest accuracy. High stability transcribes faster, but with slightly lower accuracy. "Transcript": "EndTime": "IsPartial": Partial-result stabilization not enabled true true true true true true true true true true true true false 0.545 1.045 1.545 2.045 2.545 3.045 3.545 4.045 4.545 5.045 5.545 6.025 6.025 The The The Amazon. The Amazon is The Amazon is the law. The Amazon is the largest The Amazon is the largest ray The Amazon is the largest rain for The Amazon is the largest rainforest. The Amazon is the largest rainforest on the The Amazon is the largest rainforest on the planet. The Amazon is the largest rainforest on the planet. The Amazon is the largest rainforest on the planet. Partial-result stabilization enabled (high stability) The 0.515 true Partial-result stabilization 162 Amazon Transcribe Developer Guide "Transcript": "EndTime": "IsPartial": true true true true true true true true true true true true false 1.015 1.515 2.015 2.515 3.015 3.515 4.015 4.515 5.015 5.515 6.015 6.335 6.335 The The Amazon. The Amazon is The Amazon is the large The Amazon is the largest The Amazon is the largest rainfall. The Amazon is the largest rain forest. The Amazon is the largest rain forest on The Amazon is the largest rain forest on the planet.
transcribe-dg-032	transcribe-dg.pdf	32	The Amazon is the largest rainforest on the planet. Partial-result stabilization enabled (high stability) The 0.515 true Partial-result stabilization 162 Amazon Transcribe Developer Guide "Transcript": "EndTime": "IsPartial": true true true true true true true true true true true true false 1.015 1.515 2.015 2.515 3.015 3.515 4.015 4.515 5.015 5.515 6.015 6.335 6.335 The The Amazon. The Amazon is The Amazon is the large The Amazon is the largest The Amazon is the largest rainfall. The Amazon is the largest rain forest. The Amazon is the largest rain forest on The Amazon is the largest rain forest on the planet. The Amazon is the largest rain forest on the planet. The Amazon is the largest rain forest on the planet. The Amazon is the largest rain forest on the planet. The Amazon is the largest rain forest on the planet. When you activate partial-result stabilization, Amazon Transcribe uses a Stable ﬁeld to indicate whether an item is stable, where 'item' refers to a transcribed word or punctuation mark. Values for Stable are true or false. Items ﬂagged as false (not stable) are more likely to change as your segment is transcribed. Conversely, items ﬂagged as true (stable) won't change. You can choose to render non-stable words so your captions align with speech. Even if captions change slightly as context is added, this is a better user experience than periodic text bursts, which may or may not align with speech. You can also choose to display non-stable words in a diﬀerent format, such as italics, to indicate to viewers that these words may change. Displaying partial results limits the amount of text displayed Partial-result stabilization 163 Amazon Transcribe Developer Guide at a given time. This can be important when you're dealing with space constraints, as with video captions. Dive deeper with the AWS Machine Learning Blog To learn more about improving accuracy with real-time transcriptions, see: • Improve the streaming transcription experience with Amazon Transcribe partial results stabilization • “What was that?” Increasing subtitle accuracy for live broadcasts using Amazon Transcribe Partial-result stabilization example output The following example output shows Stable ﬂags for an incomplete segment ("IsPartial": true). You can see that the words "to" and "Amazon" are not stable and therefore could change before the segment is ﬁnalized. "Transcript": { "Results": [ { "Alternatives": [ { "Items": [ { "Content": "Welcome", "EndTime": 2.4225, "Stable": true, "StartTime": 1.65, "Type": "pronunciation", "VocabularyFilterMatch": false }, { "Content": "to", "EndTime": 2.8325, "Stable": false, "StartTime": 2.4225, "Type": "pronunciation", "VocabularyFilterMatch": false }, Partial-result stabilization 164 Amazon Transcribe Developer Guide { "Content": "Amazon", "EndTime": 3.635, "Stable": false, "StartTime": 2.8325, "Type": "pronunciation", "VocabularyFilterMatch": false }, { "Content": ".", "EndTime": 3.635, "Stable": false, "StartTime": 3.635, "Type": "punctuation", "VocabularyFilterMatch": false } ], "Transcript": "Welcome to Amazon." } ], "EndTime": 4.165, "IsPartial": true, "ResultId": "12345a67-8bc9-0de1-2f34-a5b678c90d12", "StartTime": 1.65 } ] } Setting up a streaming transcription This section expands on the main streaming section. It's intended to provide information for users who want to set up their stream with HTTP/2 or WebSockets directly, rather than with an AWS SDK. The information in this section can also be used to build your own SDK. Important We strongly recommend using SDKs rather than using HTTP/2 and WebSockets directly. SDKs are the simplest and most reliable method for transcribing data streams. To start streaming using an AWS SDK, see Transcribing with the AWS SDKs. Setting up a streaming transcription 165 Amazon Transcribe Developer Guide Setting up an HTTP/2 stream The key components for an HTTP/2 protocol for streaming transcription requests with Amazon Transcribe are: • A header frame. This contains the HTTP/2 headers for your request, and a signature in the authorization header that Amazon Transcribe uses as a seed signature to sign the data frames. • One or more message frames in event stream encoding that contain metadata and raw audio bytes. • An end frame. This is a signed message in event stream encoding with an empty body. Note Amazon Transcribe only supports one stream per HTTP/2 session. If you attempt to use multiple streams, your transcription request fails. 1. Attach the following policy to the IAM role that makes the request. See Adding IAM policies for more information. { "Version": "2012-10-17", "Statement": [ { "Sid": "my-transcribe-http2-policy", "Effect": "Allow", "Action": "transcribe:StartStreamTranscription", "Resource": "*" } ] } 2. To start the session, send an HTTP/2 request to Amazon Transcribe. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: YYYYMMDDTHHMMSSZ Setting up a streaming transcription 166 Amazon Transcribe Developer Guide Authorization: AWS4-HMAC-SHA256 Credential=access-key/YYYYMMDD/us-west-2/ transcribe/aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x- amz-date;x-amz-target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 transfer-encoding: chunked Additional operations and parameters are listed in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Amazon Transcribe sends the following response: HTTP/2.0 200 x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate:
transcribe-dg-033	transcribe-dg.pdf	33	[ { "Sid": "my-transcribe-http2-policy", "Effect": "Allow", "Action": "transcribe:StartStreamTranscription", "Resource": "*" } ] } 2. To start the session, send an HTTP/2 request to Amazon Transcribe. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: YYYYMMDDTHHMMSSZ Setting up a streaming transcription 166 Amazon Transcribe Developer Guide Authorization: AWS4-HMAC-SHA256 Credential=access-key/YYYYMMDD/us-west-2/ transcribe/aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x- amz-date;x-amz-target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 transfer-encoding: chunked Additional operations and parameters are listed in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Amazon Transcribe sends the following response: HTTP/2.0 200 x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-request-id: 8a08df7d-5998-48bf-a303-484355b4ab4e x-amzn-transcribe-session-id: b4526fcf-5eee-4361-8192-d1cb9e9d6887 content-type: application/json 3. Create an audio event that contains your audio data. Combine the headers—described in the following table—with a chunk of audio bytes in an event-encoded message. To create the payload for the event message, use a buﬀer in raw-byte format. Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 24 10 5 application/ octet-stream AudioEvent event Binary data in this example request are base64-encoded. In an actual request, data are raw bytes. :content-type: "application/vnd.amazon.eventstream" Setting up a streaming transcription 167 Amazon Transcribe Developer Guide :event-type: "AudioEvent" :message-type: "event" UklGRjzxPQBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YVTwPQAAAAAAAAAAAAAAAAD//wIA/ f8EAA== 4. Create an audio message that contains your audio data. a. Your audio message data frame contains event-encoding headers that include the current date and a signature for the audio chunk and the audio event. Header name byte length Header name (string) Header value type Value string byte length Value 16 5 :chunk-si gnature :date 6 8 varies 8 generated signature timestamp Binary data in this request are base64-encoded. In an actual request, data are raw bytes. :date: 2019-01-29T01:56:17.291Z :chunk-signature: signature AAAA0gAAAIKVoRFcTTcjb250ZW50LXR5cGUHABhhcHBsaWNhdGlvbi9vY3RldC1zdHJlYW0LOmV2ZW50LXR5 cGUHAApBdWRpb0V2ZW50DTptZXNzYWdlLXR5cGUHAAVldmVudAxDb256ZW50LVR5cGUHABphcHBsaWNhdGlv bi94LWFtei1qc29uLTEuMVJJRkY88T0AV0FWRWZtdCAQAAAAAQABAIA +AAAAfQAAAgAQAGRhdGFU8D0AAAAA AAAAAAAAAAAA//8CAP3/BAC7QLFf b. Construct a string to sign, as outlined in Create a string to sign for Signature Version 4. Your string follows this format: String stringToSign = "AWS4-HMAC-SHA256" + "\n" + DateTime + "\n" + Keypath + "\n" + Hex(priorSignature) + Setting up a streaming transcription 168 Amazon Transcribe "\n" + HexHash(nonSignatureHeaders) + "\n" + HexHash(payload); Developer Guide • DateTime: The date and time the signature is created. The format is YYYYMMDDTHHMMSSZ, where YYYY=year, MM=month, DD=day, HH=hour, MM=minute, SS=seconds, and 'T' and 'Z' are ﬁxed characters. For more information, refer to Handling Dates in Signature Version 4. • Keypath: The signature scope in the format date/region/service/aws4_request. For example, 20220127/us-west-2/transcribe/aws4_request. • Hex: A function that encodes input into a hexadecimal representation. • priorSignature: The signature for the previous frame. For the ﬁrst data frame, use the signature of the header frame. • HexHash: A function that ﬁrst creates a SHA-256 hash of its input and then uses the Hex function to encode the hash. • nonSignatureHeaders: The DateTime header encoded as a string. • payload: The byte buﬀer containing the audio event data. c. Derive a signing key from your AWS secret access key and use it to sign the stringToSign. For a greater degree of protection, the derived key is speciﬁc to the date, service, and AWS Region. For more information, see Calculate the signature for AWSSignature Version 4. Make sure you implement the GetSignatureKey function to derive your signing key. If you have not yet derived a signing key, refer to Examples of how to derive a signing key for Signature Version 4. String signature = HMACSHA256(derivedSigningKey, stringToSign); • HMACSHA256: A function that creates a signature using the SHA-256 hash function. • derivedSigningKey: The Signature Version 4 signing key. • stringToSign: The string you calculated for the data frame. Setting up a streaming transcription 169 Amazon Transcribe Developer Guide After you've calculated the signature for the data frame, construct a byte buﬀer containing the date, signature, and audio event payload. Send the byte array to Amazon Transcribe for transcription. 5. To indicate the audio stream is complete, send an end frame (an empty data frame) that contains only the date and signature. You construct this end frame the same way that you construct a data frame. Amazon Transcribe responds with a stream of transcription events, sent to your application. This response is event stream encoded. It contains the standard prelude and the following headers. Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 16 15 5 application/ json TranscriptEvent event The events are sent in raw-byte format. In this example, the bytes are base64-encoded. AAAAUwAAAEP1RHpYBTpkYXRlCAAAAWiXUkMLEDpjaHVuay1zaWduYXR1cmUGACCt6Zy+uymwEK2SrLp/ zVBI 5eGn83jdBwCaRUBJA+eaDafqjqI= To see the transcription results, decode the raw bytes using event stream encoding. :content-type: "application/vnd.amazon.eventstream" :event-type: "TranscriptEvent" :message-type: "event" { "Transcript": { "Results": Setting up a streaming transcription 170 Amazon Transcribe Developer Guide [ results ] } } 6. To end your stream, send an empty
transcribe-dg-034	transcribe-dg.pdf	34	and the following headers. Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 16 15 5 application/ json TranscriptEvent event The events are sent in raw-byte format. In this example, the bytes are base64-encoded. AAAAUwAAAEP1RHpYBTpkYXRlCAAAAWiXUkMLEDpjaHVuay1zaWduYXR1cmUGACCt6Zy+uymwEK2SrLp/ zVBI 5eGn83jdBwCaRUBJA+eaDafqjqI= To see the transcription results, decode the raw bytes using event stream encoding. :content-type: "application/vnd.amazon.eventstream" :event-type: "TranscriptEvent" :message-type: "event" { "Transcript": { "Results": Setting up a streaming transcription 170 Amazon Transcribe Developer Guide [ results ] } } 6. To end your stream, send an empty audio event to Amazon Transcribe. Create the audio event exactly like any other, except with an empty payload. Sign the event and include the signature in the :chunk-signature header, as follows: :date: 2019-01-29T01:56:17.291Z :chunk-signature: signature Handling HTTP/2 streaming errors If an error occurs when processing your media stream, Amazon Transcribe sends an exception response. The response is event stream encoded. The response contains the standard prelude and the following headers: Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 16 19 9 application/json BadReques tException exception When the exception response is decoded, it contains the following information: :content-type: "application/vnd.amazon.eventstream" :event-type: "BadRequestException" :message-type: "exception" Exception message Setting up a streaming transcription 171 Amazon Transcribe Developer Guide Setting up a WebSocket stream The key components for a WebSocket protocol for streaming transcription requests with Amazon Transcribe are: • The upgrade request. This contains the query parameters for your request, and a signature that Amazon Transcribe uses as a seed signature to sign the data frames. • One or more message frames in event stream encoding that contain metadata and raw audio bytes. • An end frame. This is a signed message in event stream encoding with an empty body. Note Amazon Transcribe only supports one stream per WebSocket session. If you attempt to use multiple streams, your transcription request fails. 1. Attach the following policy to the IAM role that makes the request. See Adding IAM policies for more information. { "Version": "2012-10-17", "Statement": [ { "Sid": "my-transcribe-websocket-policy", "Effect": "Allow", "Action": "transcribe:StartStreamTranscriptionWebSocket", "Resource": "*" } ] } 2. To start the session, create a presigned URL in the following format. Line breaks have been added for readability. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=access-key%2FYYYYMMDD%2Fus-west-2%2Ftranscribe%2Faws4_request Setting up a streaming transcription 172 Amazon Transcribe Developer Guide &X-Amz-Date=YYYYMMDDTHHMMSSZ &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &language-code=en-US &media-encoding=flac &sample-rate=16000 Note The maximum value for X-Amz-Expires is 300 (5 minutes). Additional operations and parameters are listed in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. To construct the URL for your request and create the Signature Version 4 signature, refer to the following steps. Examples are in pseudocode. a. Create a canonical request. A canonical request is a string that includes information from your request in a standardized format. This ensures that when AWS receives the request, it can calculate the same signature you created for your URL. For more information, see Create a Canonical Request for Signature Version 4. # HTTP verb method = "GET" # Service name service = "transcribe" # Region region = "us-west-2" # Amazon Transcribe streaming endpoint endpoint = "wss://transcribestreaming.us-west-2.amazonaws.com:8443" # Host host = "transcribestreaming.us-west-2.amazonaws.com:8443" # Date and time of request amz-date = YYYYMMDDTHHMMSSZ # Date without time for credential scope datestamp = YYYYMMDD Setting up a streaming transcription 173 Amazon Transcribe Developer Guide b. Create a canonical URI, which is the part of the URI between the domain and the query string. canonical_uri = "/stream-transcription-websocket" c. Create the canonical headers and signed headers. Note the trailing \n in the canonical headers. • Append the lowercase header name followed by a colon ( : ). • Append a comma-separated list of values for that header. Do not sort values in headers that have multiple values. • Append a new line (\n). canonical_headers = "host:" + host + "\n" signed_headers = "host" d. Match the algorithm to the hashing algorithm. Use SHA-256. algorithm = "AWS4-HMAC-SHA256" e. Create the credential scope, which scopes the derived key to the date, AWS Region, and service. For example, 20220127/us-west-2/transcribe/aws4_request. credential_scope = datestamp + "/" + region + "/" + service + "/" + "aws4_request" f. Create the canonical query string. Query string values must be URI-encoded and sorted by name. • Sort the parameter names by character code point in ascending order. Parameters with duplicate names should be sorted by value. For example, a parameter name that begins with the uppercase letter F precedes a parameter name that begins with the lowercase letter b. • Do not URI-encode any of the unreserved characters that RFC 3986 deﬁnes: A-Z, a-z, 0-9, hyphen ( - ), underscore (
transcribe-dg-035	transcribe-dg.pdf	35	For example, 20220127/us-west-2/transcribe/aws4_request. credential_scope = datestamp + "/" + region + "/" + service + "/" + "aws4_request" f. Create the canonical query string. Query string values must be URI-encoded and sorted by name. • Sort the parameter names by character code point in ascending order. Parameters with duplicate names should be sorted by value. For example, a parameter name that begins with the uppercase letter F precedes a parameter name that begins with the lowercase letter b. • Do not URI-encode any of the unreserved characters that RFC 3986 deﬁnes: A-Z, a-z, 0-9, hyphen ( - ), underscore ( _ ), period ( . ), and tilde ( ~ ). • Percent-encode all other characters with %XY, where X and Y are hexadecimal characters (0-9 and uppercase A-F). For example, the space character must be encoded Setting up a streaming transcription 174 Amazon Transcribe Developer Guide as %20 (don't include '+', as some encoding schemes do); extended UTF-8 characters must be in the form %XY%ZA%BC. • Double-encode any equals ( = ) characters in parameter values. canonical_querystring = "X-Amz-Algorithm=" + algorithm canonical_querystring += "&X-Amz-Credential="+ URI-encode(access key + "/" + credential_scope) canonical_querystring += "&X-Amz-Date=" + amz_date canonical_querystring += "&X-Amz-Expires=300" canonical_querystring += "&X-Amz-Security-Token=" + token canonical_querystring += "&X-Amz-SignedHeaders=" + signed_headers canonical_querystring += "&language-code=en-US&media-encoding=flac&sample- rate=16000" g. Create a hash of the payload. For a GET request, the payload is an empty string. payload_hash = HashSHA256(("").Encode("utf-8")).HexDigest() h. Combine the following elements to create the canonical request. canonical_request = method + '\n' + canonical_uri + '\n' + canonical_querystring + '\n' + canonical_headers + '\n' + signed_headers + '\n' + payload_hash 3. Create the string to sign, which contains meta information about your request. You use the string to sign in the next step when you calculate the request signature. For more information, see Create a String to Sign for Signature Version 4. string_to_sign=algorithm + "\n" + amz_date + "\n" + credential_scope + "\n" + HashSHA256(canonical_request.Encode("utf-8")).HexDigest() 4. Calculate the signature. To do this, derive a signing key from your AWS secret access key. For a greater degree of protection, the derived key is speciﬁc to the date, service, and AWS Region. Use this derived key to sign the request. For more information, see Calculate the Signature for AWS Signature Version 4. Setting up a streaming transcription 175 Amazon Transcribe Developer Guide Make sure you implement the GetSignatureKey function to derive your signing key. If you have not yet derived a signing key, refer to Examples of how to derive a signing key for Signature Version 4. #Create the signing key signing_key = GetSignatureKey(secret_key, datestamp, region, service) # Sign the string_to_sign using the signing key signature = HMAC.new(signing_key, (string_to_sign).Encode("utf-8"), Sha256()).HexDigest The function HMAC(key, data) represents an HMAC-SHA256 function that returns results in binary format. 5. Add signing information to the request and create the request URL. After you calculate the signature, add it to the query string. For more information, see Add the Signature to the Request. First, add the authentication information to the query string. canonical_querystring += "&X-Amz-Signature=" + signature Second, create the URL for the request. request_url = endpoint + canonical_uri + "?" + canonical_querystring Use the request URL with your WebSocket library to make the request to Amazon Transcribe. 6. The request to Amazon Transcribe must include the following headers. Typically these headers are managed by your WebSocket client library. Host: transcribestreaming.us-west-2.amazonaws.com:8443 Connection: Upgrade Upgrade: websocket Origin: URI-of-WebSocket-client Sec-WebSocket-Version: 13 Sec-WebSocket-Key: randomly-generated-string Setting up a streaming transcription 176 Amazon Transcribe Developer Guide 7. When Amazon Transcribe receives your WebSocket request, it responds with a WebSocket upgrade response. Typically your WebSocket library manages this response and sets up a socket for communications with Amazon Transcribe. The following is the response from Amazon Transcribe. Line breaks have been added for readability. HTTP/1.1 101 WebSocket Protocol Handshake Connection: upgrade Upgrade: websocket websocket-origin: wss://transcribestreaming.us-west-2.amazonaws.com:8443 websocket-location: transcribestreaming.us-west-2.amazonaws.com:8443/stream- transcription-websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus-west-2%2Ftranscribe %2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Signature=Signature Version 4 signature &X-Amz-SignedHeaders=host &language-code=en-US &session-id=String &media-encoding=flac &sample-rate=16000 x-amzn-RequestId: RequestId Strict-Transport-Security: max-age=31536000 sec-websocket-accept: hash-of-the-Sec-WebSocket-Key-header 8. Make your WebSocket streaming request. After the WebSocket connection is established, the client can start sending a sequence of audio frames, each encoded using event stream encoding. Each data frame contains three headers combined with a chunk of raw audio bytes; the following table describes these headers. Setting up a streaming transcription 177 Amazon Transcribe Developer Guide Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 24 10 5 application/ octet-stream AudioEvent event 9. To end the data stream, send an empty audio chunk in an event stream encoded message. The response contains event stream encoded raw bytes in the payload. It contains the standard prelude and the following headers. Header name byte length Header name (string) Header value type Value string
transcribe-dg-036	transcribe-dg.pdf	36	audio bytes; the following table describes these headers. Setting up a streaming transcription 177 Amazon Transcribe Developer Guide Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 24 10 5 application/ octet-stream AudioEvent event 9. To end the data stream, send an empty audio chunk in an event stream encoded message. The response contains event stream encoded raw bytes in the payload. It contains the standard prelude and the following headers. Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 11 13 :content-type :event-type :message-type 7 7 7 16 15 5 application/ json TranscriptEvent event When you decode the binary response, you end up with a JSON structure containing the transcription results. Handling WebSocket streaming errors If an exception occurs while processing your request, Amazon Transcribe responds with a terminal WebSocket frame containing an event stream encoded response. This response contains the headers described in the following table; the body of the response contains a descriptive error message. After sending the exception response, Amazon Transcribe sends a close frame. Setting up a streaming transcription 178 Amazon Transcribe Developer Guide Header name byte length Header name (string) Header value type Value string byte length Value string (UTF-8) 13 15 13 :content-type :exception-type :message-type 7 7 7 16 varies 9 application/json varies, see below exception The exception-type header contains one of the following values: • BadRequestException: There was a client error when the stream was created, or an error occurred while streaming data. Make sure that your client is ready to accept data and try your request again. • InternalFailureException: Amazon Transcribe had a problem during the handshake with the client. Try your request again. • LimitExceededException: The client exceeded the concurrent stream limit. For more information, see Amazon Transcribe Limits. Reduce the number of streams that you're transcribing. • UnrecognizedClientException: The WebSocket upgrade request was signed with an incorrect access key or secret key. Make sure you're correctly creating the access key and try your request again. Amazon Transcribe can also return any of the common service errors. For a list, see Common Errors. Event stream encoding Amazon Transcribe uses a format called event stream encoding for streaming transcriptions. Event stream encoding provides bidirectional communication between a client and a server. Data frames sent to the Amazon Transcribe streaming service are encoded in this format. The response from Amazon Transcribe also uses this encoding. Each message consists of two sections: the prelude and the data. The prelude consists of: 1. The total byte length of the message 2. The combined byte length of all headers Event stream encoding 179 Amazon Transcribe The data section consists of: 1. Headers 2. Payload Developer Guide Each section ends with a 4-byte big-endian integer cyclic redundancy check (CRC) checksum. The message CRC checksum is for both the prelude section and the data section. Amazon Transcribe uses CRC32 (often referred to as GZIP CRC32) to calculate both CRCs. For more information about CRC32, see GZIP ﬁle format speciﬁcation version 4.3. Total message overhead, including the prelude and both checksums, is 16 bytes. The following diagram shows the components that make up a message and a header. There are multiple headers per message. Each message contains the following components: • Prelude: Consists of two, 4-byte ﬁelds, for a ﬁxed total of 8 bytes. • First 4 bytes: The big-endian integer byte-length of the entire message, inclusive of this 4-byte length ﬁeld. • Second 4 bytes: The big-endian integer byte-length of the 'headers' portion of the message, excluding the 'headers' length ﬁeld itself. • Prelude CRC: The 4-byte CRC checksum for the prelude portion of the message, excluding the CRC itself. The prelude has a separate CRC from the message CRC. That ensures that Amazon Transcribe can detect corrupted byte-length information immediately without causing errors, such as buﬀer overruns. Event stream encoding 180 Amazon Transcribe Developer Guide • Headers: Metadata annotating the message; for example, message type and content type. Messages have multiple headers, which are key:value pairs, where the key is a UTF-8 string. Headers can appear in any order in the 'headers' portion of the message, and each header can appear only once. • Payload: The audio content to be transcribed. • Message CRC: The 4-byte CRC checksum from the start of the message to the start of the checksum. That is, everything in the message except the CRC itself. The header frame is the authorization frame for the streaming transcription. Amazon Transcribe uses the authorization header's value as the seed for generating a chain of authorization headers for the data frames in the request. Each header contains the following components; there are multiple headers per frame. • Header name
transcribe-dg-037	transcribe-dg.pdf	37	the 'headers' portion of the message, and each header can appear only once. • Payload: The audio content to be transcribed. • Message CRC: The 4-byte CRC checksum from the start of the message to the start of the checksum. That is, everything in the message except the CRC itself. The header frame is the authorization frame for the streaming transcription. Amazon Transcribe uses the authorization header's value as the seed for generating a chain of authorization headers for the data frames in the request. Each header contains the following components; there are multiple headers per frame. • Header name byte-length: The byte-length of the header name. • Header name: The name of the header that indicates the header type. For valid values, see the following frame descriptions. • Header value type: A number indicating the header value. The following list shows the possible values for the header and what they indicate. • 0 – TRUE • 1 – FALSE • 2 – BYTE • 3 – SHORT • 4 – INTEGER • 5 – LONG • 6 – BYTE ARRAY • 7 – STRING • 8 – TIMESTAMP • 9 – UUID • Value string byte length: The byte length of the header value string. • Header value: The value of the header string. Valid values for this ﬁeld depend on the type of header. See Setting up an HTTP/2 stream or Setting up a WebSocket stream for more information. Event stream encoding 181 Amazon Transcribe Data frames Developer Guide Each streaming request contains one or more data frames. There are two steps to creating a data frame: 1. Combine raw audio data with metadata to create the payload of your request. 2. Combine the payload with a signature to form the event message that is sent to Amazon Transcribe. The following diagram shows how this works. Data frames 182 Amazon Transcribe Developer Guide Job queueing Using job queueing, you can submit more transcription job requests than can be concurrently processed. Without job queueing, once you reach the quota of allowed concurrent requests, you must wait until one or more requests are completed before submitting a new request. Job queueing is optional for both transcription job and post-call analytics job requests. If you enable job queueing, Amazon Transcribe creates a queue that contains all requests that exceed your limit. As soon as a request is completed, a new request is pulled from your queue and processed. Queued requests are processed in a FIFO (ﬁrst in, ﬁrst out) order. You can add up to 10,000 jobs to your queue. If you exceed this limit, you get a LimitExceededConcurrentJobException error. To maintain optimal performance, Amazon Transcribe only uses up to 90 percent of your quota (a bandwidth ratio of 0.9) to process queued jobs. Note that these are default values that can be increased upon request. Tip You can ﬁnd a list of default limits and quotas for Amazon Transcribe resources in the AWS General Reference. Some of these defaults can be increased upon request. If you enable job queueing but don't exceed the quota for concurrent requests, all requests are processed concurrently. Enabling job queueing You can enable job queueing using the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples; see the following for examples: AWS Management Console 1. 2. 3. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. In the Job Settings box, there is an Additional settings panel. If you expand this panel, you can select the Add to job queue box to enable job queueing. Enabling job queueing 183 Amazon Transcribe Developer Guide 4. Fill in any other ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. 5. Select Create job to run your transcription job. Enabling job queueing 184 Amazon Transcribe AWS CLI Developer Guide This example uses the start-transcription-job command and job-execution-settings parameter with the AllowDeferredExecution sub-parameter. Note that when you include AllowDeferredExecution in your request, you must also include DataAccessRoleArn. For more information, see StartTranscriptionJob and JobExecutionSettings. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --job-execution-settings AllowDeferredExecution=true,DataAccessRoleArn=arn:aws:iam::111122223333:role/ ExampleRole Here's another example using the start-transcription-job command, and a request body that enables queueing. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-queueing-request.json The ﬁle my-ﬁrst-queueing-request.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "JobExecutionSettings": { "AllowDeferredExecution": true, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole" } } Enabling job queueing 185 Amazon Transcribe Developer Guide AWS SDK for Python (Boto3) This example uses the AWS SDK for
transcribe-dg-038	transcribe-dg.pdf	38	and JobExecutionSettings. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --job-execution-settings AllowDeferredExecution=true,DataAccessRoleArn=arn:aws:iam::111122223333:role/ ExampleRole Here's another example using the start-transcription-job command, and a request body that enables queueing. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-queueing-request.json The ﬁle my-ﬁrst-queueing-request.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "JobExecutionSettings": { "AllowDeferredExecution": true, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole" } } Enabling job queueing 185 Amazon Transcribe Developer Guide AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to enable job queueing using the AllowDeferredExecution argument for the start_transcription_job method. Note that when you include AllowDeferredExecution in your request, you must also include DataAccessRoleArn. For more information, see StartTranscriptionJob and JobExecutionSettings. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-queueing-request" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', JobExecutionSettings = { 'AllowDeferredExecution': True, 'DataAccessRoleArn': 'arn:aws:iam::111122223333:role/ExampleRole' } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) You can view the progress of a queued job via the AWS Management Console or by submitting a GetTranscriptionJob request. When a job is queued, the Status is QUEUED. The status Enabling job queueing 186 Amazon Transcribe Developer Guide changes to IN_PROGRESS once your job starts processing, then changes to COMPLETED or FAILED when processing is ﬁnished. Enabling job queueing 187 Amazon Transcribe Developer Guide Tagging resources A tag is a custom metadata label that you can add to a resource to make it easier to identify, organize, and ﬁnd in a search. Tags are comprised of two individual parts: A tag key and a tag value. This is referred to as a key:value pair. A tag key typically represents a larger category, while a tag value represents a subset of that category. For example you could have tag key=Color and tag value=Blue, which would produce the key:value pair Color:Blue. Note that you can set the value of a tag to an empty string, but you can't set the value of a tag to null. Omitting the tag value is the same as using an empty string. Tip AWS Billing and Cost Management can use tags to separate your bills into dynamic categories. For example, if you add tags to represent diﬀerent departments within your company, such as Department:Sales or Department:Legal, AWS can provide you with your cost distribution per department. In Amazon Transcribe, you can tag the following resources: • Transcription jobs • Medical transcription jobs • Call analytics post call transcription jobs • Custom vocabularies • Custom medical vocabularies • Custom vocabulary ﬁlters • Call analytics categories • Custom language models Tag keys can be up to 128 characters in length and tag values can be up to 256 characters in length; both are case sensitive. Amazon Transcribe supports up to 50 tags per resource. For a given resource, each tag key must be unique with only one value. Note that your tags cannot begin with aws: because AWS reserves this preﬁx for system-generated tags. You cannot add, modify, or delete aws:* tags, and they don't count against your tags-per-resource limit. 188 Amazon Transcribe Developer Guide API operations speciﬁc to resource tagging ListTagsForResource, TagResource, UntagResource To use the tagging APIs, you must include an Amazon Resource Name (ARN) with your request. ARNs have the format arn:partition:service:region:account- id:resource-type/resource-id. For example, the ARN associated with a transcription job may look like: arn:aws:transcribe:us- west-2:111122223333:transcription-job/my-transcription-job-name. To learn more about tagging, including best practices, see Tagging AWS resources. Tag-based access control You can use tags to control access within your AWS accounts. For tag-based access control, you provide tag information in the condition element of an IAM policy. You can then use tags and their associated tag condition key to control access to: • Resources: Control access to your Amazon Transcribe resources based on the tags you've assigned to those resources. • Use the aws:ResourceTag/key-name condition key to specify which tag key:value pair must be attached to the resource. • Requests: Control which tags can be passed in a request. • Use the aws:RequestTag/key-name condition key to specify which tags can be added, modiﬁed, or removed from an IAM user or role. • Authorization processes: Control tag-based access for any part of your authorization process. • Use the aws:TagKeys/ condition key to control whether speciﬁc tag keys can be used on a resource, in a request, or by a principal. In this case, the key value doesn't matter. For
transcribe-dg-039	transcribe-dg.pdf	39	those resources. • Use the aws:ResourceTag/key-name condition key to specify which tag key:value pair must be attached to the resource. • Requests: Control which tags can be passed in a request. • Use the aws:RequestTag/key-name condition key to specify which tags can be added, modiﬁed, or removed from an IAM user or role. • Authorization processes: Control tag-based access for any part of your authorization process. • Use the aws:TagKeys/ condition key to control whether speciﬁc tag keys can be used on a resource, in a request, or by a principal. In this case, the key value doesn't matter. For an example tag-based access control policy, see Viewing transcription jobs based on tags. For more detailed information on tag-based access control, see Controlling access to AWS resources using tags. Tag-based access control 189 Amazon Transcribe Developer Guide Adding tags to your Amazon Transcribe resources You can add tags before or after you run your Amazon Transcribe job. Using the existing Create* and Start* APIs, you can add add tags with your transcription request. You can add, modify or delete tags using the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console 1. 2. 3. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Scroll to the bottom of the Specify job details page to ﬁnd the Tags - optional box and select Add new tag. 4. Enter information for the Key ﬁeld and, optionally, the Value ﬁeld. Adding tags to your Amazon Transcribe resources 190 Amazon Transcribe Developer Guide 5. Fill in any other ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. Select Create job to run your transcription job. 6. You can view the tags associated with a transcription job by navigating to the Transcription jobs page, selecting a transcription job, and scrolling to the bottom of that job's information page. If you want to edit your tags, you can do so by selecting Manage tags. AWS CLI This example uses the start-transcription-job command and Tags parameter. For more information, see StartTranscriptionJob and Tag. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --tags Key=color,Value=blue Key=shape,Value=square Here's another example using the start-transcription-job command, and a request body that adds tags to that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://filepath/my-first-tagging-job.json The ﬁle my-ﬁrst-tagging-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", Adding tags to your Amazon Transcribe resources 191 Amazon Transcribe "Media": { Developer Guide "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Tags": [ { "Key": "color", "Value": "blue" }, { "Key": "shape", "Value": "square" } ] } AWS SDK for Python (Boto3) The following example uses the AWS SDK for Python (Boto3) to add a tag by using the Tags argument for the start_transcription_job method. For more information, see StartTranscriptionJob and Tag. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Tags = [ { Adding tags to your Amazon Transcribe resources 192 Amazon Transcribe Developer Guide 'Key':'color', 'Value':'blue' } ] ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Adding tags to your Amazon Transcribe resources 193 Amazon Transcribe Developer Guide Partitioning speakers (diarization) With speaker diarization, you can distinguish between diﬀerent speakers in your transcription output. Amazon Transcribe can diﬀerentiate between a maximum of 30 unique speakers and labels the text from each unique speaker with a unique value (spk_0 through spk_9). In addition to the standard transcript sections (transcripts and items), requests with speaker partitioning enabled include a speaker_labels section. This section is grouped by speaker and contains information on each utterance, including speaker label and timestamps. "speaker_labels": { "channel_label": "ch_0", "speakers": 2, "segments": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "6.88", "items": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "5.02" }, ... { "start_time": "8.49", "speaker_label": "spk_1", "end_time": "9.24", "items": [ { "start_time": "8.49", "speaker_label": "spk_1", "end_time": "8.88" }, To view a complete example transcript with speaker partitioning (for two speakers), see Example diarization output (batch). 194 Amazon Transcribe Developer Guide Partitioning speakers in a batch transcription To partition speakers in a batch transcription, see the following examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation
transcribe-dg-040	transcribe-dg.pdf	40	label and timestamps. "speaker_labels": { "channel_label": "ch_0", "speakers": 2, "segments": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "6.88", "items": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "5.02" }, ... { "start_time": "8.49", "speaker_label": "spk_1", "end_time": "9.24", "items": [ { "start_time": "8.49", "speaker_label": "spk_1", "end_time": "8.88" }, To view a complete example transcript with speaker partitioning (for two speakers), see Example diarization output (batch). 194 Amazon Transcribe Developer Guide Partitioning speakers in a batch transcription To partition speakers in a batch transcription, see the following examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Partitioning speakers in a batch transcription 195 Amazon Transcribe Developer Guide 3. Fill in any ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. To enable speaker partitioning, in Audio settings, choose Audio identiﬁcation. Then choose Speaker partitioning and specify the number of speakers. 4. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job. For more information, see StartTranscriptionJob. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --settings ShowSpeakerLabels=true,MaxSpeakerLabels=3 Partitioning speakers in a batch transcription 196 Amazon Transcribe Developer Guide Here's another example using the start-transcription-job command, and a request body that enables speaker partitioning with that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-transcription-job.json The ﬁle my-ﬁrst-transcription-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "ShowSpeakerLabels": 'TRUE', "MaxSpeakerLabels": 3 } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to identify channels using the start_transcription_job method. For more information, see StartTranscriptionJob. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Settings = { Partitioning speakers in a batch transcription 197 Amazon Transcribe Developer Guide 'ShowSpeakerLabels': True, 'MaxSpeakerLabels': 3 } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Partitioning speakers in a streaming transcription To partition speakers in a streaming transcription, see the following examples: Streaming transcriptions 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Real-time transcription. Scroll down to Audio settings and expand this ﬁeld if it is minimized. Partitioning speakers in a streaming transcription 198 Amazon Transcribe Developer Guide 3. Toggle on Speaker partitioning. 4. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming. Partitioning speakers in a streaming transcription 199 Amazon Transcribe HTTP/2 stream Developer Guide This example creates an HTTP/2 request that partitions speakers in your transcription output. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-show-speaker-label: true transfer-encoding: chunked Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that separates speakers in your transcription output. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date Partitioning speakers in a streaming transcription 200 Amazon Transcribe Developer Guide &language-code=en-US &specialty=PRIMARYCARE &type=DICTATION &media-encoding=flac &sample-rate=16000 &show-speaker-label=true Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Example diarization output (batch) Here's an output example for a batch transcription with diarization enabled. { "jobName": "my-first-transcription-job", "accountId": "111122223333", "results": { "transcripts": [ { "transcript": "I've been on hold for an hour. Sorry about that." } ], "speaker_labels": { "channel_label": "ch_0", "speakers": 2, "segments": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "6.88", "items": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "5.02" }, { "start_time": "5.02", "speaker_label": "spk_0", "end_time": "5.17" }, { Example output 201 Amazon Transcribe Developer Guide "start_time": "5.17", "speaker_label": "spk_0", "end_time": "5.29" }, { "start_time": "5.29", "speaker_label": "spk_0", "end_time": "5.64" }, { "start_time": "5.64", "speaker_label": "spk_0", "end_time": "5.84" }, { "start_time": "6.11", "speaker_label": "spk_0", "end_time": "6.26" }, { "start_time": "6.26", "speaker_label": "spk_0", "end_time": "6.88" } ] }, { "start_time": "8.49", "speaker_label":
transcribe-dg-041	transcribe-dg.pdf	41	{ "transcript": "I've been on hold for an hour. Sorry about that." } ], "speaker_labels": { "channel_label": "ch_0", "speakers": 2, "segments": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "6.88", "items": [ { "start_time": "4.87", "speaker_label": "spk_0", "end_time": "5.02" }, { "start_time": "5.02", "speaker_label": "spk_0", "end_time": "5.17" }, { Example output 201 Amazon Transcribe Developer Guide "start_time": "5.17", "speaker_label": "spk_0", "end_time": "5.29" }, { "start_time": "5.29", "speaker_label": "spk_0", "end_time": "5.64" }, { "start_time": "5.64", "speaker_label": "spk_0", "end_time": "5.84" }, { "start_time": "6.11", "speaker_label": "spk_0", "end_time": "6.26" }, { "start_time": "6.26", "speaker_label": "spk_0", "end_time": "6.88" } ] }, { "start_time": "8.49", "speaker_label": "spk_1", "end_time": "9.24", "items": [ { "start_time": "8.49", "speaker_label": "spk_1", "end_time": "8.88" }, { "start_time": "8.88", "speaker_label": "spk_1", "end_time": "9.05" }, { "start_time": "9.05", "speaker_label": "spk_1", Example output 202 Amazon Transcribe Developer Guide "end_time": "9.24" } ] } ] }, "items": [ { "id": 0, "start_time": "4.87", "speaker_label": "spk_0", "end_time": "5.02", "alternatives": [ { "confidence": "1.0", "content": "I've" } ], "type": "pronunciation" }, { "id": 1, "start_time": "5.02", "speaker_label": "spk_0", "end_time": "5.17", "alternatives": [ { "confidence": "1.0", "content": "been" } ], "type": "pronunciation" }, { "id": 2, "start_time": "5.17", "speaker_label": "spk_0", "end_time": "5.29", "alternatives": [ { "confidence": "1.0", "content": "on" } ], Example output 203 Amazon Transcribe Developer Guide "type": "pronunciation" }, { "id": 3, "start_time": "5.29", "speaker_label": "spk_0", "end_time": "5.64", "alternatives": [ { "confidence": "1.0", "content": "hold" } ], "type": "pronunciation" }, { "id": 4, "start_time": "5.64", "speaker_label": "spk_0", "end_time": "5.84", "alternatives": [ { "confidence": "1.0", "content": "for" } ], "type": "pronunciation" }, { "id": 5, "start_time": "6.11", "speaker_label": "spk_0", "end_time": "6.26", "alternatives": [ { "confidence": "1.0", "content": "an" } ], "type": "pronunciation" }, { "id": 6, "start_time": "6.26", Example output 204 Amazon Transcribe Developer Guide "speaker_label": "spk_0", "end_time": "6.88", "alternatives": [ { "confidence": "1.0", "content": "hour" } ], "type": "pronunciation" }, { "id": 7, "speaker_label": "spk_0", "alternatives": [ { "confidence": "0.0", "content": "." } ], "type": "punctuation" }, { "id": 8, "start_time": "8.49", "speaker_label": "spk_1", "end_time": "8.88", "alternatives": [ { "confidence": "1.0", "content": "Sorry" } ], "type": "pronunciation" }, { "id": 9, "start_time": "8.88", "speaker_label": "spk_1", "end_time": "9.05", "alternatives": [ { "confidence": "0.902", "content": "about" } Example output 205 Developer Guide Amazon Transcribe ], "type": "pronunciation" }, { "id": 10, "start_time": "9.05", "speaker_label": "spk_1", "end_time": "9.24", "alternatives": [ { "confidence": "1.0", "content": "that" } ], "type": "pronunciation" }, { "id": 11, "speaker_label": "spk_1", "alternatives": [ { "confidence": "0.0", "content": "." } ], "type": "punctuation" } ], "audio_segments": [ { "id": 0, "transcript": "I've been on hold for an hour.", "start_time": "4.87", "end_time": "6.88", "speaker_label": "spk_0", "items": [ 0, 1, 2, 3, 4, 5, 6, 7 Example output 206 Developer Guide Amazon Transcribe ] }, { "id": 1, "transcript": "Sorry about that.", "start_time": "8.49", "end_time": "9.24", "speaker_label": "spk_1", "items": [ 8, 9, 10, 11 ] } ] }, "status": "COMPLETED" } Example output 207 Amazon Transcribe Developer Guide Transcribing multi-channel audio If your audio has two channels, you can use channel identiﬁcation to transcribe the speech from each channel separately. Amazon Transcribe doesn't currently support audio with more than two channels. In your transcript, channels are assigned the labels ch_0 and ch_1. In addition to the standard transcript sections (transcripts and items), requests with channel identiﬁcation enabled include a channel_labels section. This section contains each utterance or punctuation mark, grouped by channel, and its associated channel label, timestamps, and conﬁdence score. "channel_labels": { "channels": [ { "channel_label": "ch_0", "items": [ { "channel_label": "ch_0", "start_time": "4.86", "end_time": "5.01", "alternatives": [ { "confidence": "1.0", "content": "I've" } ], "type": "pronunciation" }, ... "channel_label": "ch_1", "items": [ { "channel_label": "ch_1", "start_time": "8.5", "end_time": "8.89", "alternatives": [ { "confidence": "1.0", "content": "Sorry" 208 Amazon Transcribe Developer Guide } ], "type": "pronunciation" }, ... "number_of_channels": 2 }, Note that if a person on one channel speaks at the same time as a person on a separate channel, timestamps for each channel overlap while the individuals are speaking over each other. To view a complete example transcript with channel identiﬁcation, see Example channel identiﬁcation output (batch). Using channel identiﬁcation in a batch transcription To identify channels in a batch transcription, you can use the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Using channel identiﬁcation in a batch transcription 209 Amazon Transcribe Developer Guide 3. Fill in any ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. In the Audio settings panel, select Channel identiﬁcation (under the 'Audio identiﬁcation type' heading). Using channel identiﬁcation in a batch transcription 210 Amazon Transcribe Developer Guide
transcribe-dg-042	transcribe-dg.pdf	42	for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Using channel identiﬁcation in a batch transcription 209 Amazon Transcribe Developer Guide 3. Fill in any ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. In the Audio settings panel, select Channel identiﬁcation (under the 'Audio identiﬁcation type' heading). Using channel identiﬁcation in a batch transcription 210 Amazon Transcribe Developer Guide 4. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job. For more information, see StartTranscriptionJob. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --settings ChannelIdentification=true Here's another example using the start-transcription-job command, and a request body that enables channel identiﬁcation with that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-transcription-job.json The ﬁle my-ﬁrst-transcription-job.json contains the following request body. Using channel identiﬁcation in a batch transcription 211 Amazon Transcribe Developer Guide { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Settings": { "ChannelIdentification": true } } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to identify channels using the start_transcription_job method. For more information, see StartTranscriptionJob. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Settings = { 'ChannelIdentification':True } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) Using channel identiﬁcation in a batch transcription 212 Amazon Transcribe print(status) Developer Guide Using channel identiﬁcation in a streaming transcription To identify channels in a streaming transcription, you can use HTTP/2 or WebSockets; see the following for examples: HTTP/2 stream This example creates an HTTP/2 request that separates channels in your transcription output. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-channel-identification: TRUE transfer-encoding: chunked Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that separates channels in your transcription output. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? Using channel identiﬁcation in a streaming transcription 213 Amazon Transcribe Developer Guide &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &language-code=en-US &specialty=PRIMARYCARE &type=DICTATION &media-encoding=flac &sample-rate=16000 &channel-identification=TRUE Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Example channel identiﬁcation output (batch) Here's an output example for a batch transcription with channel identiﬁcation enabled. { "jobName": "my-first-transcription-job", "accountId": "111122223333", "results": { "transcripts": [ { "transcript": "I've been on hold for an hour. Sorry about that." } ], "channel_labels": { "channels": [ { "channel_label": "ch_0", "items": [ { "channel_label": "ch_0", "start_time": "4.86", "end_time": "5.01", "alternatives": [ { "confidence": "1.0", Example output 214 Amazon Transcribe Developer Guide "content": "I've" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "5.01", "end_time": "5.16", "alternatives": [ { "confidence": "1.0", "content": "been" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "5.16", "end_time": "5.28", "alternatives": [ { "confidence": "1.0", "content": "on" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "5.28", "end_time": "5.62", "alternatives": [ { "confidence": "1.0", "content": "hold" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "5.62", Example output 215 Amazon Transcribe Developer Guide "end_time": "5.83", "alternatives": [ { "confidence": "1.0", "content": "for" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "6.1", "end_time": "6.25", "alternatives": [ { "confidence": "1.0", "content": "an" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "6.25", "end_time": "6.87", "alternatives": [ { "confidence": "1.0", "content": "hour" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "language_code": "en-US", "alternatives": [ { "confidence": "0.0", "content": "." } ], "type": "punctuation" } Example output 216 Amazon Transcribe Developer Guide ] }, { "channel_label": "ch_1", "items": [ { "channel_label": "ch_1", "start_time": "8.5", "end_time": "8.89", "alternatives": [ { "confidence": "1.0", "content": "Sorry" } ], "type": "pronunciation" }, { "channel_label": "ch_1", "start_time": "8.89", "end_time": "9.06", "alternatives": [ { "confidence": "0.9176", "content": "about" } ], "type": "pronunciation" }, { "channel_label": "ch_1", "start_time": "9.06",
transcribe-dg-043	transcribe-dg.pdf	43	"content": "an" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "start_time": "6.25", "end_time": "6.87", "alternatives": [ { "confidence": "1.0", "content": "hour" } ], "type": "pronunciation" }, { "channel_label": "ch_0", "language_code": "en-US", "alternatives": [ { "confidence": "0.0", "content": "." } ], "type": "punctuation" } Example output 216 Amazon Transcribe Developer Guide ] }, { "channel_label": "ch_1", "items": [ { "channel_label": "ch_1", "start_time": "8.5", "end_time": "8.89", "alternatives": [ { "confidence": "1.0", "content": "Sorry" } ], "type": "pronunciation" }, { "channel_label": "ch_1", "start_time": "8.89", "end_time": "9.06", "alternatives": [ { "confidence": "0.9176", "content": "about" } ], "type": "pronunciation" }, { "channel_label": "ch_1", "start_time": "9.06", "end_time": "9.25", "alternatives": [ { "confidence": "1.0", "content": "that" } ], "type": "pronunciation" }, { "channel_label": "ch_1", "alternatives": [ Example output 217 Amazon Transcribe Developer Guide { "confidence": "0.0", "content": "." } ], "type": "punctuation" } ] } ], "number_of_channels": 2 }, "items": [ { "id": 0, "channel_label": "ch_0", "start_time": "4.86", "end_time": "5.01", "alternatives": [ { "confidence": "1.0", "content": "I've" } ], "type": "pronunciation" }, { "id": 1, "channel_label": "ch_0", "start_time": "5.01", "end_time": "5.16", "alternatives": [ { "confidence": "1.0", "content": "been" } ], "type": "pronunciation" }, { "id": 2, "channel_label": "ch_0", "start_time": "5.16", "end_time": "5.28", Example output 218 Amazon Transcribe Developer Guide "alternatives": [ { "confidence": "1.0", "content": "on" } ], "type": "pronunciation" }, { "id": 3, "channel_label": "ch_0", "start_time": "5.28", "end_time": "5.62", "alternatives": [ { "confidence": "1.0", "content": "hold" } ], "type": "pronunciation" }, { "id": 4, "channel_label": "ch_0", "start_time": "5.62", "end_time": "5.83", "alternatives": [ { "confidence": "1.0", "content": "for" } ], "type": "pronunciation" }, { "id": 5, "channel_label": "ch_0", "start_time": "6.1", "end_time": "6.25", "alternatives": [ { "confidence": "1.0", "content": "an" } Example output 219 Developer Guide Amazon Transcribe ], "type": "pronunciation" }, { "id": 6, "channel_label": "ch_0", "start_time": "6.25", "end_time": "6.87", "alternatives": [ { "confidence": "1.0", "content": "hour" } ], "type": "pronunciation" }, { "id": 7, "channel_label": "ch_0", "alternatives": [ { "confidence": "0.0", "content": "." } ], "type": "punctuation" }, { "id": 8, "channel_label": "ch_1", "start_time": "8.5", "end_time": "8.89", "alternatives": [ { "confidence": "1.0", "content": "Sorry" } ], "type": "pronunciation" }, { "id": 9, "channel_label": "ch_1", "start_time": "8.89", Example output 220 Amazon Transcribe Developer Guide "end_time": "9.06", "alternatives": [ { "confidence": "0.9176", "content": "about" } ], "type": "pronunciation" }, { "id": 10, "channel_label": "ch_1", "start_time": "9.06", "end_time": "9.25", "alternatives": [ { "confidence": "1.0", "content": "that" } ], "type": "pronunciation" }, { "id": 11, "channel_label": "ch_1", "alternatives": [ { "confidence": "0.0", "content": "." } ], "type": "punctuation" } ], "audio_segments": [ { "id": 0, "transcript": "I've been on hold for an hour.", "start_time": "4.86", "end_time": "6.87", "channel_label": "ch_0", "items": [ 0, 1, Example output 221 Amazon Transcribe Developer Guide 2, 3, 4, 5, 6, 7 ] }, { "id": 1, "transcript": "Sorry about that.", "start_time": "8.5", "end_time": "9.25", "channel_label": "ch_1", "items": [ 8, 9, 10, 11 ] } ] }, "status": "COMPLETED" } Example output 222 Amazon Transcribe Developer Guide Identifying the dominant languages in your media Amazon Transcribe is able to automatically identify the languages spoken in your media without you having to specify a language code. Batch language identiﬁcation can identify the dominant language spoken in your media ﬁle or, if your media contains multiple languages, it can identify all languages spoken. To improve language identiﬁcation accuracy, you can optionally provide a list of two or more languages you think may be present in your media. Streaming language identiﬁcation can identify one language per channel (a maximum of two channels are supported) or, if your stream contains multiple languages, it can identify all languages spoken. Streaming requests must have a minimum of two additional language options included in your request. Providing language options allows for faster language identiﬁcation. The faster Amazon Transcribe is able to identify the language, the less change there is of data loss in the ﬁrst few seconds of your stream. Important Batch and streaming transcriptions support diﬀerent languages. Refer to the Data input column in the supported languages table for details. Note that Swedish and Vietnamese are not currently supported with language identiﬁcation. To learn about monitoring and events with language identiﬁcation, refer to Language identiﬁcation events. Language identiﬁcation with batch transcription jobs Use batch language identiﬁcation to automatically identify the language, or languages, in your media ﬁle. If your media contains only one language, you can enable single-language identiﬁcation, which identiﬁes the dominant language spoken in your media ﬁle and creates your transcript using only this language. If your media contains more than one language, you can enable multi-language identiﬁcation, which identiﬁes all languages spoken in your media ﬁle and creates your transcript using each Batch language identiﬁcation 223 Amazon Transcribe Developer Guide identiﬁed language. Note that a multi-lingual transcript is produced. You can use other services, such as Amazon Translate, to translate your transcript. Refer to the supported languages table for
transcribe-dg-044	transcribe-dg.pdf	44	languages, in your media ﬁle. If your media contains only one language, you can enable single-language identiﬁcation, which identiﬁes the dominant language spoken in your media ﬁle and creates your transcript using only this language. If your media contains more than one language, you can enable multi-language identiﬁcation, which identiﬁes all languages spoken in your media ﬁle and creates your transcript using each Batch language identiﬁcation 223 Amazon Transcribe Developer Guide identiﬁed language. Note that a multi-lingual transcript is produced. You can use other services, such as Amazon Translate, to translate your transcript. Refer to the supported languages table for a complete list of supported languages and associated language codes. For best results, ensure that your media ﬁle contains at least 30 seconds of speech. For usage examples with the AWS Management Console, AWS CLI, and AWS Python SDK, see Using language identiﬁcation with batch transcriptions. Identifying languages in multi-language audio Multi-language identiﬁcation is intended for multi-lingual media ﬁles, and provides you with a transcript that reﬂects all supported languages spoken in your media. This means that if speakers change languages mid-conversation, or if each participant is speaking a diﬀerent language, your transcription output detects and transcribes each language correctly. For example, if your media contains a bilingual speaker who is alternating between US English (en-US) and Hindi (hi-IN), multi-language identiﬁcation can identify and transcribe spoken US English as en-US and spoken Hindi as hi-IN. This diﬀers from single-language identiﬁcation, where only one dominant language is used to create a transcript. In this case, any spoken language that is not the dominant language is transcribed incorrectly. Note Redaction and custom language models are not currently supported with multi-language identiﬁcation. Note The following languages are currently supported with multi-language identiﬁcation: en-AB, en-AU, en-GB, en-IE, en-IN, en-NZ, en-US, en-WL, en-ZA, es-ES, es-US, fr-CA, fr-FR, zh-CN, zh-TW, pt-BR, pt-PT, de-CH, de-DE, af-ZA, ar-AE, da-DK, he-IL, hi-IN, id-ID, fa-IR, it-IT, ja-JP, ko-KR, ms-MY, nl-NL, ru-RU, ta-IN, te-IN, th-TH, tr-TR Identifying languages in multi-language audio 224 Amazon Transcribe Developer Guide Multi-language transcripts provide a summary of detected languages and the total time that each language is spoken in your media. Here's an example: "results": { "transcripts": [ { "transcript": "welcome to Amazon transcribe. ## ## ###### ### #### #### ## #### ### ###################" } ], ... "language_codes": [ { "language_code": "en-US", "duration_in_seconds": 2.45 }, { "language_code": "hi-IN", "duration_in_seconds": 5.325 }, { "language_code": "ja-JP", "duration_in_seconds": 4.15 } ] } Improving language identiﬁcation accuracy With language identiﬁcation, you have the option to include a list of languages you think may be present in your media. Including language options (LanguageOptions) restricts Amazon Transcribe to using only the languages that you specify when matching your audio to the correct language, which can speed up language identiﬁcation and improve the accuracy associated with assigning the correct language dialect. If you choose to include language codes, you must include at least two. There's no limit on the number of language codes you can include, but we recommend using between two and ﬁve for optimal eﬃciency and accuracy. Improving language identiﬁcation accuracy 225 Amazon Transcribe Note Developer Guide If you include language codes with your request and none of the language codes you provide match the language, or languages, identiﬁed in your audio, Amazon Transcribe selects the closest language match from your speciﬁed language codes. It then produces a transcript in that language. For example, if your media is in US English (en-US) and you provide Amazon Transcribe with the language codes zh-CN, fr-FR, and de-DE, Amazon Transcribe is likely to match your media to German (de-DE) and produce a German- language transcription. Mismatching language codes and spoken languages can result in an inaccurate transcript, so we recommend caution when including language codes. Combining language identiﬁcation with other Amazon Transcribe features You can use batch language identiﬁcation in combination with any other Amazon Transcribe feature. If combining language identiﬁcation with other features, you are limited to the languages supported with those features. For example, if using language identiﬁcation with content redaction, you are limited to US English (en-US) or US Spanish (es-US), as this is only language available for redaction. Refer to Supported languages and language-speciﬁc features for more information. Important If you're using automatic language identiﬁcation with content redaction enabled and your audio contains languages other than US English (en-US) or US Spanish (es-US), only the US English or US Spanish content is redacted in your transcript. Other languages cannot be redacted and there are no warnings or job failures. Custom language models, custom vocabularies, and custom vocabulary ﬁlters If you want to add one or more custom language models, custom vocabularies, or custom vocabulary ﬁlters to your language identiﬁcation request, you must include the LanguageIdSettings parameter. You can then specify a language code with a corresponding custom language model, custom
transcribe-dg-045	transcribe-dg.pdf	45	you're using automatic language identiﬁcation with content redaction enabled and your audio contains languages other than US English (en-US) or US Spanish (es-US), only the US English or US Spanish content is redacted in your transcript. Other languages cannot be redacted and there are no warnings or job failures. Custom language models, custom vocabularies, and custom vocabulary ﬁlters If you want to add one or more custom language models, custom vocabularies, or custom vocabulary ﬁlters to your language identiﬁcation request, you must include the LanguageIdSettings parameter. You can then specify a language code with a corresponding custom language model, custom vocabulary, and custom vocabulary ﬁlter. Note that multi- language identiﬁcation doesn't support custom language models. Combining language identiﬁcation with other Amazon Transcribe features 226 Amazon Transcribe Developer Guide It's recommended that you include LanguageOptions when using LanguageIdSettings to ensure that the correct language dialect is identiﬁed. For example, if you specify an en-US custom vocabulary, but Amazon Transcribe determines that the language spoken in your media is en-AU, your custom vocabulary is not applied to your transcription. If you include LanguageOptions and specify en-US as the only English language dialect, your custom vocabulary is applied to your transcription. For examples of LanguageIdSettings in a request, refer to Option 2 in the AWS CLI and AWS SDKs dropdown panels in the Using language identiﬁcation with batch transcriptions section. Using language identiﬁcation with batch transcriptions You can use automatic language identiﬁcation in a batch transcription job using the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console 1. 2. 3. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. In the Job settings panel, ﬁnd the Language settings section and select Automatic language identiﬁcation or Automatic multiple languages identiﬁcation. You have the option to select multiple language options (from the Select languages dropdown box) if you know which languages are present in your audio ﬁle. Providing language options can improve accuracy, but is not required. Using language identiﬁcation with batch transcriptions 227 Amazon Transcribe Developer Guide 4. Fill in any other ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. Using language identiﬁcation with batch transcriptions 228 Amazon Transcribe Developer Guide 5. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job command and IdentifyLanguage parameter. For more information, see StartTranscriptionJob and LanguageIdSettings. Using language identiﬁcation with batch transcriptions 229 Amazon Transcribe Developer Guide Option 1: Without the language-id-settings parameter. Use this option if you are not including a custom language model, custom vocabulary, or custom vocabulary ﬁlter in your request. language-options is optional, but recommended. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --identify-language \ (or --identify-multiple-languages) \ --language-options "en-US" "hi-IN" Option 2: With the language-id-settings parameter. Use this option if you are including a custom language model, custom vocabulary, or custom vocabulary ﬁlter in your request. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --identify-language \ (or --identify-multiple-languages) --language-options "en-US" "hi-IN" \ --language-id-settings en-US=VocabularyName=my-en-US-vocabulary,en- US=VocabularyFilterName=my-en-US-vocabulary-filter,en-US=LanguageModelName=my-en-US- language-model,hi-IN=VocabularyName=my-hi-IN-vocabulary,hi-IN=VocabularyFilterName=my- hi-IN-vocabulary-filter Here's another example using the start-transcription-job command, and a request body that identiﬁes the language. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://filepath/my-first-language-id-job.json The ﬁle my-ﬁrst-language-id-job.json contains the following request body. Option 1: Without the LanguageIdSettings parameter. Use this option if you are not including a custom language model, custom vocabulary, or custom vocabulary ﬁlter in your request. LanguageOptions is optional, but recommended. Using language identiﬁcation with batch transcriptions 230 Amazon Transcribe Developer Guide { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "IdentifyLanguage": true, (or "IdentifyMultipleLanguages": true), "LanguageOptions": [ "en-US", "hi-IN" ] } Option 2: With the LanguageIdSettings parameter. Use this option if you are including a custom language model, custom vocabulary, or custom vocabulary ﬁlter in your request. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "IdentifyLanguage": true, (or "IdentifyMultipleLanguages": true) "LanguageOptions": [ "en-US", "hi-IN" ], "LanguageIdSettings": { "en-US" : { "LanguageModelName": "my-en-US-language-model", "VocabularyFilterName": "my-en-US-vocabulary-filter", "VocabularyName": "my-en-US-vocabulary" }, "hi-IN": { "VocabularyName": "my-hi-IN-vocabulary", "VocabularyFilterName": "my-hi-IN-vocabulary-filter" } } } Using language identiﬁcation with batch transcriptions 231 Amazon Transcribe AWS SDK for Python (Boto3) Developer Guide This example uses the AWS SDK for Python (Boto3) to identify your ﬁle's language using the IdentifyLanguage argument for the start_transcription_job method. For more information, see StartTranscriptionJob and LanguageIdSettings. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. Option 1: Without the LanguageIdSettings parameter. Use this option
transcribe-dg-046	transcribe-dg.pdf	46	"LanguageIdSettings": { "en-US" : { "LanguageModelName": "my-en-US-language-model", "VocabularyFilterName": "my-en-US-vocabulary-filter", "VocabularyName": "my-en-US-vocabulary" }, "hi-IN": { "VocabularyName": "my-hi-IN-vocabulary", "VocabularyFilterName": "my-hi-IN-vocabulary-filter" } } } Using language identiﬁcation with batch transcriptions 231 Amazon Transcribe AWS SDK for Python (Boto3) Developer Guide This example uses the AWS SDK for Python (Boto3) to identify your ﬁle's language using the IdentifyLanguage argument for the start_transcription_job method. For more information, see StartTranscriptionJob and LanguageIdSettings. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. Option 1: Without the LanguageIdSettings parameter. Use this option if you are not including a custom language model, custom vocabulary, or custom vocabulary ﬁlter in your request. LanguageOptions is optional, but recommended. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', MediaFormat = 'flac', IdentifyLanguage = True, (or IdentifyMultipleLanguages = True), LanguageOptions = [ 'en-US', 'hi-IN' ] ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using language identiﬁcation with batch transcriptions 232 Amazon Transcribe Developer Guide Option 2: With the LanguageIdSettings parameter. Use this option if you are including a custom language model, custom vocabulary, or custom vocabulary ﬁlter in your request. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', MediaFormat='flac', IdentifyLanguage=True, (or IdentifyMultipleLanguages=True) LanguageOptions = [ 'en-US', 'hi-IN' ], LanguageIdSettings={ 'en-US': { 'VocabularyName': 'my-en-US-vocabulary', 'VocabularyFilterName': 'my-en-US-vocabulary-filter', 'LanguageModelName': 'my-en-US-language-model' }, 'hi-IN': { 'VocabularyName': 'my-hi-IN-vocabulary', 'VocabularyFilterName': 'my-hi-IN-vocabulary-filter' } } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using language identiﬁcation with batch transcriptions 233 Amazon Transcribe Developer Guide Language identiﬁcation with streaming transcriptions Streaming language identiﬁcation can identify the dominant language spoken in your media stream. Amazon Transcribe requires a minimum of three seconds of speech to identify the language. If your stream contains only one language, you can enable single-language identiﬁcation, which identiﬁes the dominant language spoken in your media ﬁle and creates your transcript using only this language. If your stream contains more than one language, you can enable multi-language identiﬁcation, which identiﬁes all languages spoken in your stream and creates your transcript using each identiﬁed language. Note that a multi-lingual transcript is produced. You can use other services, such as Amazon Transcribe, to translate your transcript. To use streaming language identiﬁcation, you must provide at least two language codes, and you can select only one language dialect per language per stream. This means that you cannot select en-US and en-AU as language options for the same transcription. You also have the option to select a preferred language from the set of language codes you provide. Adding a preferred language can speed up the language identiﬁcation process, which is helpful for short audio clips. Important If none of the language codes you provide match the language, or languages, identiﬁed in your audio, Amazon Transcribe selects the closest language match from your speciﬁed language codes. It then produces a transcript in that language. For example, if your media is in US English (en-US) and you provide Amazon Transcribe with the language codes zh-CN, fr-FR, and de-DE, Amazon Transcribe is likely to match your media to German (de-DE) and produce a German-language transcription. Mismatching language codes and spoken languages can result in an inaccurate transcript, so we recommend caution when including language codes. If your media contains two channels, Amazon Transcribe can identify the dominant language spoken in each channel. In this case, set the ChannelIdentification parameter to true and each channel is transcribed separately. Note that the default for this parameter is false. If you don't change it, only the ﬁrst channel is transcribed and only one language is identiﬁed. Streaming language identiﬁcation 234 Amazon Transcribe Developer Guide Streaming language identiﬁcation can't be combined with custom language models or redaction. If combining language identiﬁcation with other features, you are limited to the languages supported with those features, and also with streaming transcriptions. Refer to Supported languages. Note PCM and FLAC are the only supported audio formats for streaming language identiﬁcation. Identifying languages in multi-language audio Multi-language identiﬁcation is intended for multi-lingual streams, and provides you with a transcript that reﬂects all supported languages spoken in your stream. This means that if speakers change languages mid-conversation, or if each participant is speaking a diﬀerent language, your transcription output detects and transcribes each language correctly. For example, if your stream contains a bilingual speaker who is alternating between US English (en-US) and Hindi (hi-IN), multi-language identiﬁcation can identify and
transcribe-dg-047	transcribe-dg.pdf	47	and also with streaming transcriptions. Refer to Supported languages. Note PCM and FLAC are the only supported audio formats for streaming language identiﬁcation. Identifying languages in multi-language audio Multi-language identiﬁcation is intended for multi-lingual streams, and provides you with a transcript that reﬂects all supported languages spoken in your stream. This means that if speakers change languages mid-conversation, or if each participant is speaking a diﬀerent language, your transcription output detects and transcribes each language correctly. For example, if your stream contains a bilingual speaker who is alternating between US English (en-US) and Hindi (hi-IN), multi-language identiﬁcation can identify and transcribe spoken US English as en-US and spoken Hindi as hi-IN. This diﬀers from single-language identiﬁcation, where only one dominant language is used to create a transcript. In this case, any spoken language that is not the dominant language is transcribed incorrectly. Note Redaction and custom language models are not currently supported with multi-language identiﬁcation. Using language identiﬁcation with streaming media You can use automatic language identiﬁcation in a streaming transcription using the AWS Management Console, HTTP/2, or WebSockets; see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Real-time transcription. Scroll down to Language settings and expand this ﬁeld if it is minimized. Identifying languages in multi-language audio 235 Amazon Transcribe Developer Guide 3. Select Automatic language identiﬁcation or Automatic multiple languages identiﬁcation. Using language identiﬁcation with streaming media 236 Amazon Transcribe Developer Guide 4. Provide a minimum of two language codes for your transcription. Note that you can provide only one dialect per language. For example, you cannot select both en-US and fr-CA as language options for the same transcription. Using language identiﬁcation with streaming media 237 Amazon Transcribe Developer Guide 5. (Optional) From the subset of languages you selected in the previous step, you can choose a preferred language for your transcript. Using language identiﬁcation with streaming media 238 Amazon Transcribe Developer Guide 6. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming. HTTP/2 stream This example creates an HTTP/2 request with language identiﬁcation enabled. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 Using language identiﬁcation with streaming media 239 Amazon Transcribe Developer Guide x-amzn-transcribe-identify-language: true x-amzn-transcribe-language-options: en-US,de-DE x-amzn-transcribe-preferred-language: en-US transfer-encoding: chunked This example creates an HTTP/2 request with multiple languages identiﬁcation enabled. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-identify-multiple-languages: true x-amzn-transcribe-language-options: en-US,de-DE x-amzn-transcribe-preferred-language: en-US transfer-encoding: chunked If you use identify-language or identify-multiple-languages in your request, you must also include language-options. You cannot use both language-code and identify- language in the same request. Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that uses language identiﬁcation in a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? Using language identiﬁcation with streaming media 240 Amazon Transcribe Developer Guide &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &media-encoding=flac &sample-rate=16000 &identify-language=true &language-options=en-US,de-DE &preferred-language=en-US This example creates a presigned URL that uses multiple languages identiﬁcation in a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &media-encoding=flac &sample-rate=16000 &identify-multiple-languages=true &language-options=en-US,de-DE &preferred-language=en-US If you use identify-language or identify-multiple-languages in your request, you must also include language-options. You cannot use both language-code and identify- language in the same request. Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Using language identiﬁcation with streaming media 241 Amazon Transcribe Developer Guide Alternative transcriptions When Amazon Transcribe transcribes audio, it creates diﬀerent versions of the same transcript and assigns a conﬁdence score to each version. In a typical transcription, you only get the version with the highest conﬁdence score. If you turn on alternative transcriptions, Amazon Transcribe returns other versions of your transcript that
transcribe-dg-048	transcribe-dg.pdf	48	include language-options. You cannot use both language-code and identify- language in the same request. Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Using language identiﬁcation with streaming media 241 Amazon Transcribe Developer Guide Alternative transcriptions When Amazon Transcribe transcribes audio, it creates diﬀerent versions of the same transcript and assigns a conﬁdence score to each version. In a typical transcription, you only get the version with the highest conﬁdence score. If you turn on alternative transcriptions, Amazon Transcribe returns other versions of your transcript that have lower conﬁdence levels. You can choose to have up to 10 alternative transcriptions returned. If you specify a greater number of alternatives than what Amazon Transcribe identiﬁes, only the actual number of alternatives is returned. All alternatives are located in the same transcription output ﬁle and are presented at the segment level. Segments are natural pauses in speech, such as a change in speaker or a pause in the audio. Alternative transcriptions are only available for batch transcriptions. Your transcription output is structured as follows. The ellipses (...) in the code examples indicate where content has been removed for brevity. 1. A complete ﬁnal transcription for a given segment. "results": { "language_code": "en-US", "transcripts": [ { "transcript": "The amazon is the largest rainforest on the planet." } ], 2. A conﬁdence score for each word in the preceding transcript section. "items": [ { "start_time": "1.15", "end_time": "1.35", "alternatives": [ { "confidence": "1.0", "content": "The" } ], 242 Amazon Transcribe Developer Guide "type": "pronunciation" }, { "start_time": "1.35", "end_time": "2.05", "alternatives": [ { "confidence": "1.0", "content": "amazon" } ], "type": "pronunciation" }, 3. Your alternative transcriptions are located in the segments portion of your transcription output. The alternatives for each segment are ordered by descending conﬁdence score. "segments": [ { "start_time": "1.04", "end_time": "5.065", "alternatives": [ { ... "transcript": "The amazon is the largest rain forest on the planet.", "items": [ { "start_time": "1.15", "confidence": "1.0", "end_time": "1.35", "type": "pronunciation", "content": "The" }, ... { "start_time": "3.06", "confidence": "0.0037", "end_time": "3.38", "type": "pronunciation", "content": "rain" }, { 243 Amazon Transcribe Developer Guide "start_time": "3.38", "confidence": "0.0037", "end_time": "3.96", "type": "pronunciation", "content": "forest" }, 4. A status at the end of your transcription output. "status": "COMPLETED" } Requesting alternative transcriptions You can request alternative transcriptions using the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Requesting alternative transcriptions 244 Amazon Transcribe Developer Guide 3. Fill in any ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. Select Alternative results and specify the maximum number of alternative transcription result you want in your transcript. Requesting alternative transcriptions 245 Amazon Transcribe Developer Guide 4. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job command and ShowAlternatives parameter. For more information, see StartTranscriptionJob and ShowAlternatives. Note that if you include ShowAlternatives=true in your request, you must also include MaxAlternatives. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --settings ShowAlternatives=true,MaxAlternatives=4 Here's another example using the start-transcription-job command, and a request body that includes alternative transcriptions. aws transcribe start-transcription-job \ Requesting alternative transcriptions 246 Amazon Transcribe --region us-west-2 \ --cli-input-json file://filepath/my-first-alt-transcription-job.json The ﬁle my-ﬁrst-alt-transcription-job.json contains the following request body. Developer Guide { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Settings": { "ShowAlternatives": true, "MaxAlternatives": 4 } } AWS SDK for Python (Boto3) The following example uses the AWS SDK for Python (Boto3) to request alternative transcriptions by using the ShowAlternatives argument for the start_transcription_job method. For more information, see StartTranscriptionJob and ShowAlternatives. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. Note that if you include 'ShowAlternatives':True in your request, you must also include MaxAlternatives. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, Requesting alternative transcriptions 247 Amazon Transcribe Developer Guide OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Settings = { 'ShowAlternatives':True, 'MaxAlternatives':4 } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Requesting alternative transcriptions 248 Amazon Transcribe Developer Guide Improving transcription accuracy with custom vocabularies and custom language models If your media contains domain-speciﬁc or non-standard terms, such
transcribe-dg-049	transcribe-dg.pdf	49	also include MaxAlternatives. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, Requesting alternative transcriptions 247 Amazon Transcribe Developer Guide OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Settings = { 'ShowAlternatives':True, 'MaxAlternatives':4 } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Requesting alternative transcriptions 248 Amazon Transcribe Developer Guide Improving transcription accuracy with custom vocabularies and custom language models If your media contains domain-speciﬁc or non-standard terms, such as brand names, acronyms, technical words, and jargon, Amazon Transcribe might not correctly capture these terms in your transcription output. To correct transcription inaccuracies and customize your output for your speciﬁc use case, you can create Custom vocabularies and Custom language models. • Custom vocabularies are designed to tune and boost both the recognition and the formatting of speciﬁc words in all contexts. This involves supplying Amazon Transcribe with words and, optionally, pronunciation and display forms. If Amazon Transcribe is not correctly rendering speciﬁc terms in your transcripts, you can create a custom vocabulary ﬁle that tells Amazon Transcribe how you want these terms displayed. This word-speciﬁc approach is most appropriate for correcting terms like brand names and acronyms. • Custom language models are designed to capture the context associated with terms. This involves supplying Amazon Transcribe with a large volume of domain-speciﬁc text data. If Amazon Transcribe is not correctly rendering technical terms or is using the incorrect homophone in your transcripts, you can create a custom language model that teaches Amazon Transcribe your domain-speciﬁc language. For example, a custom language model can learn when to use 'ﬂoe' (ice ﬂoe) versus 'ﬂow' (linear ﬂow). This context-aware approach is most appropriate for transcribing large volumes of domain- speciﬁc speech. Custom language models can produce signiﬁcant accuracy improvements over custom vocabularies alone. When using batch transcriptions, you can include both a custom language model and a custom vocabulary in your request. Tip To achieve the highest transcription accuracy, use custom vocabularies in conjunction with your custom language models. 249 Amazon Transcribe Developer Guide For a video demo on how to create a custom vocabulary using the AWS Management Console, see Using a custom vocabulary. For a video demo on how to create and use custom language models, see Using Custom Language Models (CLM) to supercharge transcription accuracy. Dive deeper with the AWS Machine Learning Blog Custom vocabularies: • Live transcriptions of F1 races using Amazon Transcribe Custom language models: • Building custom language models to supercharge speech-to-text performance Amazon Transcribe • Boost transcription accuracy of class lectures with custom language models for Amazon Transcribe Custom vocabularies Use custom vocabularies to improve transcription accuracy for one or more speciﬁc words. These are generally domain-speciﬁc terms, such as brand names and acronyms, proper nouns, and words that Amazon Transcribe isn't rendering correctly. Custom vocabularies can be used with all supported languages. Note that only the characters listed in your language's character set can be used in a custom vocabulary. Important You are responsible for the integrity of your own data when you use Amazon Transcribe. Do not enter conﬁdential information, personal information (PII), or protected health information (PHI) into a custom vocabulary. Considerations when creating a custom vocabulary: • You can have up to 100 custom vocabulary ﬁles per AWS account Custom vocabularies 250 Amazon Transcribe Developer Guide • The size limit for each custom vocabulary ﬁle is 50 Kb • If using the API to create your custom vocabulary, your vocabulary ﬁle must be in text (.txt) format. If using the AWS Management Console, your vocabulary ﬁle can be in text (.txt) format or comma-separated value (*.csv) format. • Each entry within a custom vocabulary cannot exceed 256 characters • To use a custom vocabulary, it must have been created in the same AWS Region as your transcription. Tip You can test your custom vocabulary using the AWS Management Console. Once your custom vocabulary is ready to use, log in to the AWS Management Console, select Real- time transcription, scroll to Customizations, toggle on Custom vocabulary, and select your custom vocabulary from the dropdown list. Then select start streaming. Speak some of the words in your custom vocabulary into your microphone to see if they render correctly. Custom vocabulary tables versus lists Important Custom vocabularies in list format are being deprecated. If you're creating a new custom vocabulary, use the table format. Tables give you more options for—and more control over—the input and output of words within your custom vocabulary. With tables, you must specify multiple categories (Phrase and DisplayAs), allowing you to ﬁne-tune your output. Lists don't have additional options, so you can only type in entries as you want them
transcribe-dg-050	transcribe-dg.pdf	50	the dropdown list. Then select start streaming. Speak some of the words in your custom vocabulary into your microphone to see if they render correctly. Custom vocabulary tables versus lists Important Custom vocabularies in list format are being deprecated. If you're creating a new custom vocabulary, use the table format. Tables give you more options for—and more control over—the input and output of words within your custom vocabulary. With tables, you must specify multiple categories (Phrase and DisplayAs), allowing you to ﬁne-tune your output. Lists don't have additional options, so you can only type in entries as you want them to appear in your transcript, replacing all spaces with hyphens. The AWS Management Console, AWS CLI, and AWS SDKs all use custom vocabulary tables in the same way; lists are used diﬀerently for each method and thus may require additional formatting for successful use between methods. Custom vocabulary tables versus lists 251 Amazon Transcribe Developer Guide For more information, see Creating a custom vocabulary using a table and Creating a custom vocabulary using a list. To dive a little deeper and learn how to use Amazon Augmented AI with custom vocabularies, see Start building a human review along with Amazon Transcribe API operations speciﬁc to custom vocabularies CreateVocabulary, DeleteVocabulary, GetVocabulary, ListVocabularies, UpdateVocabulary Creating a custom vocabulary using a table Using a table format is the preferred way to create your custom vocabulary. Vocabulary tables must consist of four columns (Phrase, SoundsLike, IPA, and DisplayAs), which can be included in any order: Phrase SoundsLike IPA DisplayAs Required. Every row in your table must SoundsLike is no longer supported for IPA is no longer supported for Optional. Rows in this column can be left contain an entry in Custom Vocabular Custom Vocabular empty. this column. y. Please leave the y. Please leave the column empty. Any column empty. Any values in this column values in this column will be ignored. We will remove the will be ignored. We will remove the support for this support for this column in the future. column in the future. Do not use spaces in this column. If your entry contains multiple words, separate each word with a hyphen (-). For example, Andorra- la-Vella or Los- Angeles . For acronyms, any pronounced letters must be separated by You can use spaces in this column. Deﬁnes the how you want your entry to look in your transcrip tion output. For example, Andorra- la-Vella in the Phrase column is Andorra la Vella in the DisplayAs column. Creating a custom vocabulary using a table 252 Amazon Transcribe Developer Guide Phrase SoundsLike IPA DisplayAs If a row in this column is empty, Amazon Transcribe uses the contents of the Phrase column to determine output. You can include digits (0-9) in this column. a period. The trailing period also needs to be pronounced. If your acronym is plural, you must use a hyphen between the acronym and the 's'. For example, 'CLI' is C.L.I. (not C.L.I) and 'ABCs' is A.B.C.-s (not A.B.C-s). If your phrase consists of both a word and an acronym, these two components must be separated by a hyphen. For example, 'DynamoDB' is Dynamo-D.B. . Do not include digits in this column; numbers must be spelled out. For example, 'VX02Q' is V.X.-zero-two- Q.. Things to note when creating your table: • Your table must contain all four column headers (Phrase, SoundsLike, IPA, and DisplayAs). The Phrase column must contain an entry on each row. The ability to provide pronunciation inputs Creating a custom vocabulary using a table 253 Amazon Transcribe Developer Guide through IPA and SoundsLike is no longer supported and you may leave the column empty. Any values in these columns will be ignored. • Each column must be TAB or comma (,) delineated; this applies to every row in your custom vocabulary ﬁle. If a row contains empty columns, you must still include a delineator (TAB or comma) for each column. • Spaces are only allowed within the IPA and DisplayAs columns. Do not use spaces to separate columns. • IPA and SoundsLike are no longer supported for Custom Vocabulary. Please leave the column empty. Any values in these column will be ignored. We will remove the support for this column in the future. • The DisplayAs column supports symbols and special characters (for example, C++). All other columns support the characters that are listed on your language's character set page. • If you want to include numbers in the Phrase column, you must spell them out. Digits (0-9) are only supported in the DisplayAs column. • You must save your table as a plaintext (*.txt) ﬁle in LF format. If you use any other format, such as CRLF, your custom vocabulary can't be processed. • You must upload your custom vocabulary ﬁle into an Amazon S3
transcribe-dg-051	transcribe-dg.pdf	51	support for this column in the future. • The DisplayAs column supports symbols and special characters (for example, C++). All other columns support the characters that are listed on your language's character set page. • If you want to include numbers in the Phrase column, you must spell them out. Digits (0-9) are only supported in the DisplayAs column. • You must save your table as a plaintext (*.txt) ﬁle in LF format. If you use any other format, such as CRLF, your custom vocabulary can't be processed. • You must upload your custom vocabulary ﬁle into an Amazon S3 bucket and process it using CreateVocabulary before you can include it in a transcription request. Refer to Creating custom vocabulary tables for instructions. Note Enter acronyms, or other words whose letters should be pronounced individually, as single letters separated by periods (A.B.C.). To enter the plural form of an acronym, such as 'ABCs', separate the 's' from the acronym with a hyphen (A.B.C.-s). You can use upper or lower case letters to deﬁne an acronym. Acronyms are not supported in all languages; refer to Supported languages and language-speciﬁc features. Here is a sample custom vocabulary table (where [TAB] represents a tab character): Phrase[TAB]SoundsLike[TAB]IPA[TAB]DisplayAs Los-Angeles[TAB][TAB][TAB]Los Angeles Eva-Maria[TAB][TAB][TAB] A.B.C.-s[TAB][TAB][TAB]ABCs Creating a custom vocabulary using a table 254 Amazon Transcribe Developer Guide Amazon-dot-com[TAB][TAB][TAB]Amazon.com C.L.I.[TAB][TAB][TAB]CLI Andorra-la-Vella[TAB][TAB][TAB]Andorra la Vella Dynamo-D.B.[TAB][TAB][TAB]DynamoDB V.X.-zero-two[TAB][TAB][TAB]VX02 V.X.-zero-two-Q.[TAB][TAB][TAB]VX02Q For visual clarity, here is the same table with aligned columns. Do not add spaces between columns in your custom vocabulary table; your table should look misaligned like the preceding example. Phrase [TAB]SoundsLike [TAB]IPA [TAB]DisplayAs Los-Angeles [TAB] [TAB] [TAB]Los Angeles Eva-Maria [TAB] [TAB] [TAB] A.B.C.-s [TAB] [TAB] [TAB]ABCs amazon-dot-com [TAB] [TAB] [TAB]amazon.com C.L.I. [TAB] [TAB] [TAB]CLI Andorra-la-Vella[TAB] [TAB] [TAB]Andorra la Vella Dynamo-D.B. [TAB] [TAB] [TAB]DynamoDB V.X.-zero-two [TAB] [TAB] [TAB]VX02 V.X.-zero-two-Q.[TAB] [TAB] [TAB]VX02Q Creating custom vocabulary tables To process a custom vocabulary table for use with Amazon Transcribe, see the following examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Custom vocabulary. This opens the Custom vocabulary page where you can view existing vocabularies or create a new one. 3. Select Create vocabulary. Creating a custom vocabulary using a table 255 Amazon Transcribe Developer Guide This takes you to the Create vocabulary page. Enter a name for your new custom vocabulary. Here, you have three options: a. Upload a txt or csv ﬁle from your computer. You can either create your custom vocabulary from scratch or download a template to help you get started. Your vocabulary is then auto-populated in the View and edit vocabulary pane. Creating a custom vocabulary using a table 256 Amazon Transcribe Developer Guide b. Import a txt or csv ﬁle from an Amazon S3 location. You can either create your custom vocabulary from scratch or download a template to help you get started. Upload your ﬁnished vocabulary ﬁle to an Amazon S3 bucket and specify its URI in your request. Your vocabulary is then auto-populated in the View and edit vocabulary pane. Creating a custom vocabulary using a table 257 Amazon Transcribe Developer Guide c. Manually create your vocabulary in the console. Scroll to the View and edit vocabulary pane and select Add 10 rows. You can now manually enter terms. 4. You can edit your vocabulary the View and edit vocabulary pane. To make changes, click on the entry you want to modify. Creating a custom vocabulary using a table 258 Amazon Transcribe Developer Guide If you make an error, you get a detailed error message so you can correct any issues prior to processing your vocabulary. Note that if you don't correct all errors before selecting Create vocabulary, your vocabulary request fails. Select the check mark (✓) to save your changes or the 'X' to discard your changes. Creating a custom vocabulary using a table 259 Amazon Transcribe Developer Guide 5. Optionally, add tags to your custom vocabulary. Once you have all ﬁelds completed and are happy with your vocabulary, select Create vocabulary at the bottom of the page. This takes you back to the Custom vocabulary page where you can view the status of your custom vocabulary. When the status changes from 'Pending' to 'Ready' your custom vocabulary can be used with a transcription. 6. If the status changes to 'Failed', select the name of your custom vocabulary to go to its information page. There is a Failure reason banner at the top of this page that provides information on why your custom vocabulary failed. Correct the error in your text ﬁle and try again. Creating a custom vocabulary using a table 260 Amazon Transcribe AWS CLI Developer Guide This example uses the create-vocabulary command with a table-formatted vocabulary ﬁle. For more information, see CreateVocabulary. To use an existing custom vocabulary in a transcription job, set the
transcribe-dg-052	transcribe-dg.pdf	52	vocabulary can be used with a transcription. 6. If the status changes to 'Failed', select the name of your custom vocabulary to go to its information page. There is a Failure reason banner at the top of this page that provides information on why your custom vocabulary failed. Correct the error in your text ﬁle and try again. Creating a custom vocabulary using a table 260 Amazon Transcribe AWS CLI Developer Guide This example uses the create-vocabulary command with a table-formatted vocabulary ﬁle. For more information, see CreateVocabulary. To use an existing custom vocabulary in a transcription job, set the VocabularyName in the Settings ﬁeld when you call the StartTranscriptionJob operation or, from the AWS Management Console, choose the custom vocabulary from the dropdown list. aws transcribe create-vocabulary \ --vocabulary-name my-first-vocabulary \ --vocabulary-file-uri s3://amzn-s3-demo-bucket/my-vocabularies/my-vocabulary-file.txt \ --language-code en-US Here's another example using the create-vocabulary command, and a request body that creates your custom vocabulary. aws transcribe create-vocabulary \ --cli-input-json file://filepath/my-first-vocab-table.json The ﬁle my-ﬁrst-vocab-table.json contains the following request body. { "VocabularyName": "my-first-vocabulary", "VocabularyFileUri": "s3://amzn-s3-demo-bucket/my-vocabularies/my-vocabulary- table.txt", "LanguageCode": "en-US" } Once VocabularyState changes from PENDING to READY, your custom vocabulary is ready to use with a transcription. To view the current status of your custom vocabulary, run: aws transcribe get-vocabulary \ --vocabulary-name my-first-vocabulary AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a custom vocabulary from a table using the create_vocabulary method. For more information, see CreateVocabulary. Creating a custom vocabulary using a table 261 Amazon Transcribe Developer Guide To use an existing custom vocabulary in a transcription job, set the VocabularyName in the Settings ﬁeld when you call the StartTranscriptionJob operation or, from the AWS Management Console, choose the custom vocabulary from the dropdown list. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') vocab_name = "my-first-vocabulary" response = transcribe.create_vocabulary( LanguageCode = 'en-US', VocabularyName = vocab_name, VocabularyFileUri = 's3://amzn-s3-demo-bucket/my-vocabularies/my-vocabulary- table.txt' ) while True: status = transcribe.get_vocabulary(VocabularyName = vocab_name) if status['VocabularyState'] in ['READY', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Note If you create a new Amazon S3 bucket for your custom vocabulary ﬁles, make sure the IAM role making the CreateVocabulary request has permissions to access this bucket. If the role doesn't have the correct permissions, your request fails. You can optionally specify an IAM role within your request by including the DataAccessRoleArn parameter. For more information on IAM roles and policies in Amazon Transcribe, see Amazon Transcribe identity-based policy examples. Creating a custom vocabulary using a table 262 Amazon Transcribe Developer Guide Creating a custom vocabulary using a list Important Custom vocabularies in list format are being deprecated, so if you're creating a new custom vocabulary, we strongly recommend using the table format. You can create custom vocabularies from lists using the AWS Management Console, AWS CLI, or AWS SDKs. • AWS Management Console: You must create and upload a text ﬁle containing your custom vocabulary. You can use line-separated or comma-separated entries. Note that your list must be saved as a text (*.txt) ﬁle in LF format. If you use any other format, such as CRLF, your custom vocabulary is not accepted by Amazon Transcribe. • AWS CLI and AWS SDKs: You must include your custom vocabulary as comma-separated entries within your API call using the Phrases ﬂag. If an entry contains multiple words, you must hyphenate each word. For example, you include 'Los Angeles' as Los-Angeles and 'Andorra la Vella' as Andorra-la-Vella. Here are examples of the two valid list formats. Refer to Creating custom vocabulary lists for method-speciﬁc examples. • Comma-separated entries: Los-Angeles,CLI,Eva-Maria,ABCs,Andorra-la-Vella • Line-separated entries: Los-Angeles CLI Eva-Maria ABCs Andorra-la-Vella Creating a custom vocabulary using a list 263 Amazon Transcribe Important Developer Guide You can only use characters that are supported for your language. Refer to your language's character set for details. Custom vocabulary lists are not supported with the CreateMedicalVocabulary operation. If creating a custom medical vocabulary, you must use a table format; refer to Creating a custom vocabulary using a table for instructions. Creating custom vocabulary lists To process a custom vocabulary list for use with Amazon Transcribe, see the following examples: AWS CLI This example uses the create-vocabulary command with a list-formatted custom vocabulary ﬁle. For more information, see CreateVocabulary. aws transcribe create-vocabulary \ --vocabulary-name my-first-vocabulary \ --language-code en-US \ --phrases {CLI,Eva-Maria,ABCs} Here's another example using the create-vocabulary command, and a request body that creates your custom vocabulary. aws transcribe create-vocabulary \ --cli-input-json file://filepath/my-first-vocab-list.json The ﬁle my-ﬁrst-vocab-list.json contains the following request body. { "VocabularyName": "my-first-vocabulary", "LanguageCode": "en-US", "Phrases": [ "CLI","Eva-Maria","ABCs" ] } Creating a custom vocabulary using a list 264 Amazon Transcribe Developer Guide Once VocabularyState changes from PENDING to
transcribe-dg-053	transcribe-dg.pdf	53	vocabulary list for use with Amazon Transcribe, see the following examples: AWS CLI This example uses the create-vocabulary command with a list-formatted custom vocabulary ﬁle. For more information, see CreateVocabulary. aws transcribe create-vocabulary \ --vocabulary-name my-first-vocabulary \ --language-code en-US \ --phrases {CLI,Eva-Maria,ABCs} Here's another example using the create-vocabulary command, and a request body that creates your custom vocabulary. aws transcribe create-vocabulary \ --cli-input-json file://filepath/my-first-vocab-list.json The ﬁle my-ﬁrst-vocab-list.json contains the following request body. { "VocabularyName": "my-first-vocabulary", "LanguageCode": "en-US", "Phrases": [ "CLI","Eva-Maria","ABCs" ] } Creating a custom vocabulary using a list 264 Amazon Transcribe Developer Guide Once VocabularyState changes from PENDING to READY, your custom vocabulary is ready to use with a transcription. To view the current status of your custom vocabulary, run: aws transcribe get-vocabulary \ --vocabulary-name my-first-vocabulary AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a custom vocabulary from a list using the create_vocabulary method. For more information, see CreateVocabulary. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') vocab_name = "my-first-vocabulary" response = transcribe.create_vocabulary( LanguageCode = 'en-US', VocabularyName = vocab_name, Phrases = [ 'CLI','Eva-Maria','ABCs' ] ) while True: status = transcribe.get_vocabulary(VocabularyName = vocab_name) if status['VocabularyState'] in ['READY', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Note If you create a new Amazon S3 bucket for your custom vocabulary ﬁles, make sure the IAM role making the CreateVocabulary request has permissions to access this bucket. If the role doesn't have the correct permissions, your request fails. You can optionally specify an IAM role within your request by including the DataAccessRoleArn parameter. For Creating a custom vocabulary using a list 265 Amazon Transcribe Developer Guide more information on IAM roles and policies in Amazon Transcribe, see Amazon Transcribe identity-based policy examples. Using a custom vocabulary Once your custom vocabulary is created, you can include it in your transcription requests; refer to the following sections for examples. The language of the custom vocabulary you're including in your request must match the language code you specify for your media. If the languages don't match, your custom vocabulary is not applied to your transcription and there are no warnings or errors. Using a custom vocabulary in a batch transcription To use a custom vocabulary with a batch transcription, see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Using a custom vocabulary 266 Amazon Transcribe Developer Guide Name your job and specify your input media. Optionally include any other ﬁelds, then choose Next. 3. At the bottom of the Conﬁgure job page, in the Customization panel, toggle on Custom vocabulary. Using a custom vocabulary 267 Amazon Transcribe Developer Guide 4. Select your custom vocabulary from the dropdown menu. Select Create job to run your transcription job. Using a custom vocabulary 268 Amazon Transcribe AWS CLI Developer Guide This example uses the start-transcription-job command and Settings parameter with the VocabularyName sub-parameter. For more information, see StartTranscriptionJob and Settings. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --settings VocabularyName=my-first-vocabulary Here's another example using the start-transcription-job command, and a request body that includes your custom vocabulary with that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-vocabulary-job.json The ﬁle my-ﬁrst-vocabulary-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Settings": { "VocabularyName": "my-first-vocabulary" } } Using a custom vocabulary 269 Amazon Transcribe AWS SDK for Python (Boto3) Developer Guide This example uses the AWS SDK for Python (Boto3) to include a custom vocabulary using the Settings argument for the start_transcription_job method. For more information, see StartTranscriptionJob and Settings. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Settings = { 'VocabularyName': 'my-first-vocabulary' } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using a custom vocabulary in a streaming transcription To use a custom vocabulary with a streaming transcription, see the following for examples: AWS Management Console 1. Sign into the AWS Management Console. Using a custom vocabulary 270 Amazon Transcribe Developer Guide 2. In the navigation pane, choose Real-time transcription. Scroll down to Customizations and expand
transcribe-dg-054	transcribe-dg.pdf	54	"s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Settings = { 'VocabularyName': 'my-first-vocabulary' } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using a custom vocabulary in a streaming transcription To use a custom vocabulary with a streaming transcription, see the following for examples: AWS Management Console 1. Sign into the AWS Management Console. Using a custom vocabulary 270 Amazon Transcribe Developer Guide 2. In the navigation pane, choose Real-time transcription. Scroll down to Customizations and expand this ﬁeld if it is minimized. 3. Toggle on Custom vocabulary and select a custom vocabulary from the dropdown menu. Include any other settings that you want to apply to your stream. 4. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming. Using a custom vocabulary 271 Amazon Transcribe HTTP/2 stream Developer Guide This example creates an HTTP/2 request that includes your custom vocabulary. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-vocabulary-name: my-first-vocabulary transfer-encoding: chunked Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that applies your custom vocabulary to a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date Using a custom vocabulary 272 Amazon Transcribe &language-code=en-US &media-encoding=flac &sample-rate=16000 &vocabulary-name=my-first-vocabulary Developer Guide Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Custom language models Custom language models are designed to improve transcription accuracy for domain-speciﬁc speech. This includes any content outside of what you would hear in normal, everyday conversations. For example, if you're transcribing the proceedings from a scientiﬁc conference, a standard transcription is unlikely to recognize many of the scientiﬁc terms used by presenters. In this case, you can train a custom language model to recognize the specialized terms used in your discipline. Unlike custom vocabularies, which increase recognition of a word by providing hints (such as pronunciations), custom language models learn the context associated with a given word. This includes how and when a word is used, and the relationship a word has to other words. For example, if you train your model using climate science research papers, your model may learn that 'ice ﬂoe' is a more likely word pair than 'ice ﬂow'. To view the supported languages for custom language models, refer to Supported languages and language-speciﬁc features. Note that if you include a custom language model in your request, you can't enable language identiﬁcation (you must specify a language code). API operations speciﬁc to custom language models CreateLanguageModel, DeleteLanguageModel, DescribeLanguageModel, ListLanguageModels Data sources You can use any type of text data you want to train your model. However, the closer your text content is to your audio content, the more accurate your model. Therefore, it's important to choose text data that use the same terms in the same context as your audio. Custom language models 273 Amazon Transcribe Developer Guide The best data for training a model are accurate transcripts. This is considered in-domain data. In-domain text data have the exact same terms, usage, and context as the audio you want to transcribe. If you don't have accurate transcripts, use journal articles, technical reports, whitepapers, conference proceedings, instruction manuals, news articles, website content, and any other text that contains the desired terms used in a similar context to that of your audio. This is considered domain-related data. Creating a robust custom language model may require a signiﬁcant amount of text data, which must contain the terms spoken in your audio. You can supply Amazon Transcribe with up to 2 GB of text data to train your model—this is referred to as training data. Optionally, when you have no (or few) in-domain transcripts, you can provide Amazon Transcribe with up to 200 MB of text data to tune your model—this is referred to as tuning data. Training versus tuning data The purpose of training data is to teach Amazon Transcribe to recognize new terms and learn the context in which these terms are used. In order to create a robust model, Amazon Transcribe may require
transcribe-dg-055	transcribe-dg.pdf	55	the terms spoken in your audio. You can supply Amazon Transcribe with up to 2 GB of text data to train your model—this is referred to as training data. Optionally, when you have no (or few) in-domain transcripts, you can provide Amazon Transcribe with up to 200 MB of text data to tune your model—this is referred to as tuning data. Training versus tuning data The purpose of training data is to teach Amazon Transcribe to recognize new terms and learn the context in which these terms are used. In order to create a robust model, Amazon Transcribe may require a large volume of relevant text data. Providing as much training data as possible, up to the 2 GB limit, is strongly recommended. The purpose of tuning data is to help reﬁne and optimize the contextual relationships learned from your training data. Tuning data are not required to create a custom language model. It's up to you to decide how best to select training and, optionally, tuning data. Each case is unique and depends on the type and amount of data you have. Tuning data are recommended when you lack in-domain training data. If you choose to include both data types, do not overlap your training and tuning data; training and tuning data should be unique. Overlapping data can bias and skew your custom language model, impacting its accuracy. As general guidance, we recommend using accurate, in-domain text as training data whenever possible. Here are some general scenarios, listed in order of preference: • If you have more than 10,000 words of accurate, in-domain transcript text, use it as training data. In this case, there is no need to include tuning data. This is the ideal scenario for training a custom language model. Training versus tuning data 274 Amazon Transcribe Developer Guide • If you have accurate, in-domain transcript text that contains less than 10,000 words and are not getting the desired results, consider augmenting your training data with domain-related written texts, such as technical reports. In this case, reserve a small portion (10-25%) of your in-domain transcript data to use as tuning data. • If you have no in-domain transcript text, upload all your domain-related text as training data. In this case, transcript-style text is preferable to written text. This is the least eﬀective scenario for training a custom language model. When you're ready to create your model, see Creating a custom language model. Creating a custom language model Before you can create your custom language model, you must: • Prepare your data. Data must be saved in plain text format and can't contain any special characters. • Upload your data into an Amazon S3 bucket. Creating separate folders for training and tuning data is recommended. • Make sure Amazon Transcribe has access to your Amazon S3 bucket. You must specify an IAM role that has access permissions to use your data. Preparing your data You can compile all your data in one ﬁle or save it as multiple ﬁles. Note that if you choose to include tuning data, it must be saved in a separate ﬁle from your training data. It doesn't matter how many text ﬁles you use for your training or tuning data. Uploading one ﬁle with 100,000 words produces the same result as uploading 10 ﬁles with 10,000 words. Prepare your text data in a way that's most convenient for you. Make sure all your data ﬁles meet the following criteria: • They are all in the same language as the model you want to create. For example, if you want to create a custom language model that transcribes audio in US English (en-US), all your text data must be in US English. • They are in plain text format with UTF-8 encoding. • They don't contain any special characters or formatting, such as HTML tags. Creating a custom language model 275 Amazon Transcribe Developer Guide • They amount to a maximum combined total of 2 GB in size for training data and 200 MB for tuning data. If any of these criteria are not met, your model fails. Uploading your data Before uploading your data, create a new folder for your training data. If using tuning data, create another separate folder. The URIs for your buckets might look like: • s3://amzn-s3-demo-bucket/my-model-training-data/ • s3://amzn-s3-demo-bucket/my-model-tuning-data/ Upload your training and tuning data into the appropriate buckets. You can add more data to these buckets at a later date. However, if you do, you need to recreate your model with the new data. Existing models can't be updated with new data. Allowing access to your data To create a custom language model, you must specify an IAM role that has permissions to access your Amazon S3 bucket. If you don't already have a role with access to the
transcribe-dg-056	transcribe-dg.pdf	56	using tuning data, create another separate folder. The URIs for your buckets might look like: • s3://amzn-s3-demo-bucket/my-model-training-data/ • s3://amzn-s3-demo-bucket/my-model-tuning-data/ Upload your training and tuning data into the appropriate buckets. You can add more data to these buckets at a later date. However, if you do, you need to recreate your model with the new data. Existing models can't be updated with new data. Allowing access to your data To create a custom language model, you must specify an IAM role that has permissions to access your Amazon S3 bucket. If you don't already have a role with access to the Amazon S3 bucket where you placed your training data, you must create one. After you create a role, you can attach a policy to grant that role permissions. Do not attach a policy to a user. For example policies, see Amazon Transcribe identity-based policy examples. To learn how to create a new IAM identity, see IAM Identities (users, user groups, and roles). To learn more about policies, see: • Policies and permissions in IAM • Creating IAM policies • Access management for AWS resources Creating a custom language model 276 Amazon Transcribe Developer Guide Creating your custom language model When creating your custom language model, you must choose a base model. There are two base model options: • NarrowBand: Use this option for audio with a sample rate of less than 16,000 Hz. This model type is typically used for telephone conversations recorded at 8,000 Hz. • WideBand: Use this option for audio with a sample rate greater than or equal to 16,000 Hz. You can create custom language models using the AWS Management Console, AWS CLI, or AWS SDKs.; see the following examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Custom language model. This opens the Custom language models page where you can view existing custom language models or train a new custom language model. 3. To train a new model, select Train model. This takes you to the Train model page. Add a name, specify the language, and choose the base model you want for your model. Then, add the path to your training and, optionally, your tuning data. You must include an IAM role that has permissions to access your data. Creating a custom language model 277 Amazon Transcribe Developer Guide 4. Once you have all ﬁelds completed, select Train model at the bottom of the page. Creating a custom language model 278 Amazon Transcribe AWS CLI Developer Guide This example uses the create-language-model command. For more information, see CreateLanguageModel and LanguageModel. aws transcribe create-language-model \ --base-model-name NarrowBand \ --model-name my-first-language-model \ --input-data-config S3Uri=s3://amzn-s3-demo-bucket/my-clm-training- data/,TuningDataS3Uri=s3://amzn-s3-demo-bucket/my-clm-tuning- data/,DataAccessRoleArn=arn:aws:iam::111122223333:role/ExampleRole \ --language-code en-US Here's another example using the create-language-model command, and a request body that creates your custom language model. aws transcribe create-language-model \ --cli-input-json file://filepath/my-first-language-model.json The ﬁle my-ﬁrst-language-model.json contains the following request body. { "BaseModelName": "NarrowBand", "ModelName": "my-first-language-model", "InputDataConfig": { "S3Uri": "s3://amzn-s3-demo-bucket/my-clm-training-data/", "TuningDataS3Uri"="s3://amzn-s3-demo-bucket/my-clm-tuning-data/", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole" }, "LanguageCode": "en-US" } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a CLM using the create_language_model method. For more information, see CreateLanguageModel and LanguageModel. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. Creating a custom language model 279 Amazon Transcribe Developer Guide from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') model_name = 'my-first-language-model', transcribe.create_language_model( LanguageCode = 'en-US', BaseModelName = 'NarrowBand', ModelName = model_name, InputDataConfig = { 'S3Uri':'s3://amzn-s3-demo-bucket/my-clm-training-data/', 'TuningDataS3Uri':'s3://amzn-s3-demo-bucket/my-clm-tuning-data/', 'DataAccessRoleArn':'arn:aws:iam::111122223333:role/ExampleRole' } ) while True: status = transcribe.get_language_model(ModelName = model_name) if status['LanguageModel']['ModelStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Updating your custom language model Amazon Transcribe continually updates the base models available for custom language models. To beneﬁt from these updates, we recommend training new custom language models every 6 to 12 months. To see if your custom language model is using the latest base model, run a DescribeLanguageModel request using the AWS CLI or an AWS SDK, then ﬁnd the UpgradeAvailability ﬁeld in your response. If UpgradeAvailability is true, your model is not running the latest version of the base model. To use the latest base model in a custom language model, you must create a new custom language model. Custom language models cannot be upgraded. Using a custom language model Once you've created your custom language model, you can include it in your transcription requests; refer to the following sections for examples. Using a custom language model 280 Amazon Transcribe Developer Guide The language of the model you're including in your request must match the language code you specify for your media. If the languages don't match, your custom language model is not applied to your transcription and there are no warnings or
transcribe-dg-057	transcribe-dg.pdf	57	latest base model in a custom language model, you must create a new custom language model. Custom language models cannot be upgraded. Using a custom language model Once you've created your custom language model, you can include it in your transcription requests; refer to the following sections for examples. Using a custom language model 280 Amazon Transcribe Developer Guide The language of the model you're including in your request must match the language code you specify for your media. If the languages don't match, your custom language model is not applied to your transcription and there are no warnings or errors. Using a custom language model in a batch transcription To use a custom language model with a batch transcription, see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. 3. In the Job settings panel under Model type, select the Custom language model box. You must also select an input language from the dropdown menu. Using a custom language model 281 Amazon Transcribe Developer Guide 4. Under Custom model selection, select an existing custom language model from the dropdown menu or Create a new one. Add the Amazon S3 location of your input ﬁle in the Input data panel. 5. Select Next for additional conﬁguration options. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job command and ModelSettings parameter with the VocabularyName sub-parameter. For more information, see StartTranscriptionJob and ModelSettings. aws transcribe start-transcription-job \ Using a custom language model 282 Amazon Transcribe --region us-west-2 \ Developer Guide --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --model-settings LanguageModelName=my-first-language-model Here's another example using the start-transcription-job command, and a request body that includes your custom language model with that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-model-job.json The ﬁle my-ﬁrst-model-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "ModelSettings": { "LanguageModelName": "my-first-language-model" } } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to include a custom language model using the ModelSettings argument for the start_transcription_job method. For more information, see StartTranscriptionJob and ModelSettings. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time Using a custom language model 283 Amazon Transcribe import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" Developer Guide transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', ModelSettings = { 'LanguageModelName': 'my-first-language-model' } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using a custom language model in a streaming transcription To use a custom language model with a streaming transcription, see the following for examples: AWS Management Console 1. 2. Sign into the AWS Management Console. In the navigation pane, choose Real-time transcription. Scroll down to Customizations and expand this ﬁeld if it is minimized. Using a custom language model 284 Amazon Transcribe Developer Guide 3. Toggle on Custom language model and select a model from the dropdown menu. Include any other settings you want to apply to your stream. 4. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming. Using a custom language model 285 Amazon Transcribe HTTP/2 stream Developer Guide This example creates an HTTP/2 request that includes your custom language model. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-language-model-name: my-first-language-model transfer-encoding: chunked Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that applies your custom language model to a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date Using a custom language model 286 Amazon Transcribe &language-code=en-US &media-encoding=flac &sample-rate=16000 &language-model-name=my-first-language-model Developer Guide Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Using
transcribe-dg-058	transcribe-dg.pdf	58	Common Parameters section. WebSocket stream This example creates a presigned URL that applies your custom language model to a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date Using a custom language model 286 Amazon Transcribe &language-code=en-US &media-encoding=flac &sample-rate=16000 &language-model-name=my-first-language-model Developer Guide Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Using a custom language model 287 Amazon Transcribe Developer Guide Using custom vocabulary ﬁlters to delete, mask, or ﬂag words A custom vocabulary ﬁlter is a text ﬁle that contains a custom list of individual words that you want to modify in your transcription output. A common use case is the removal of oﬀensive or profane terms; however, custom vocabulary ﬁlters are completely custom, so you can select any words you want. For example, if you have a new product about to launch, you can mask the product name in meeting transcripts. In this case, you keep stakeholders up-to-date while keeping the product name secret until launch. Vocabulary ﬁltering has three display methods: mask, remove, and tag. Refer to the following examples to see how each works. • Mask: Replaces speciﬁed words with three asterisks (*). "transcript": "You can specify a list of * or * words, and * *** removes them from transcripts automatically." • Remove: Deletes speciﬁed words, leaving nothing in their place. "transcript": "You can specify a list of or words, and removes them from transcripts automatically." • Tag: Adds a tag ("vocabularyFilterMatch": true) to each speciﬁed word, but doesn't alter the word itself. Tagging allows for rapid transcript substitutions and edits. "transcript": "You can specify a list of profane or offensive words, and amazon transcribe removes them from transcripts automatically." ... "alternatives": [ { "confidence": "1.0", "content": "profane" } ], "type": "pronunciation", "vocabularyFilterMatch": true 288 Amazon Transcribe Developer Guide When you submit a transcription request, you can specify a custom vocabulary ﬁlter and the ﬁltration method you want to apply. Amazon Transcribe then modiﬁes exact word matches when they appear in your transcript, according to the ﬁltration method you specify. Custom vocabulary ﬁlters can be applied to batch and streaming transcription requests. To learn how to create a custom vocabulary ﬁlter, see Creating a vocabulary ﬁlter. To learn how to apply your custom vocabulary ﬁlter, see Using a custom vocabulary ﬁlter. Note Amazon Transcribe automatically masks racially sensitive terms, though you can opt out of this default ﬁlter by contacting AWS Technical Support. For a video walkthrough of vocabulary ﬁltering, see Using vocabulary ﬁlters. API operations speciﬁc to vocabulary ﬁltering CreateVocabularyFilter, DeleteVocabularyFilter, GetVocabularyFilter, ListVocabularyFilters, UpdateVocabularyFilter Creating a vocabulary ﬁlter There are two options for creating a custom vocabulary ﬁlter: 1. Save a list of line-separated words as a plain text ﬁle with UTF-8 encoding. • You can use this approach with the AWS Management Console, AWS CLI, or AWS SDKs. • If using the AWS Management Console, you can provide a local path or an Amazon S3 URI for your custom vocabulary ﬁle. • If using the AWS CLI or AWS SDKs, you must upload your custom vocabulary ﬁle to an Amazon S3 bucket and include the Amazon S3 URI in your request. 2. Include a list of comma-separated words directly in your API request. • You can use this approach with the AWS CLI or AWS SDKs using the Words parameter. For examples of each method, refer to Creating custom vocabulary ﬁlters Creating a vocabulary ﬁlter 289 Amazon Transcribe Developer Guide Things to note when creating your custom vocabulary ﬁlter: • Words aren't case sensitive. For example, "curse" and "CURSE" are treated the same. • Only exact word matches are ﬁltered. For example, if your ﬁlter includes "swear" but your media contains the word "swears" or "swearing", these are not ﬁltered. Only instances of "swear" are ﬁltered. You must therefore include all variations of the words you want ﬁltered. • Filters don't apply to words that are contained in other words. For example, if a custom vocabulary ﬁlter contains "marine" but not "submarine", "submarine" is not altered in the transcript. • Each entry can only contain one word (no spaces). • If you save your custom vocabulary ﬁlter as a text ﬁle, it must be in plain text format with UTF-8 encoding. • You can have up to 100 custom vocabulary ﬁlters per AWS account and each can be up to 50 Kb in size. • You can only use characters that are supported for your language. Refer to your language's character set for details. Creating custom vocabulary ﬁlters To process a custom vocabulary ﬁlter for
transcribe-dg-059	transcribe-dg.pdf	59	vocabulary ﬁlter contains "marine" but not "submarine", "submarine" is not altered in the transcript. • Each entry can only contain one word (no spaces). • If you save your custom vocabulary ﬁlter as a text ﬁle, it must be in plain text format with UTF-8 encoding. • You can have up to 100 custom vocabulary ﬁlters per AWS account and each can be up to 50 Kb in size. • You can only use characters that are supported for your language. Refer to your language's character set for details. Creating custom vocabulary ﬁlters To process a custom vocabulary ﬁlter for use with Amazon Transcribe, see the following examples: AWS Management Console Before continuing, save your custom vocabulary ﬁlter as a text (*.txt) ﬁle. You can optionally upload your ﬁle to an Amazon S3 bucket. 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Vocabulary ﬁltering. This opens the Vocabulary ﬁlters page where you can view existing custom vocabulary ﬁlters or create a new one. 3. Select Create vocabulary ﬁlter. Creating custom vocabulary ﬁlters 290 Amazon Transcribe Developer Guide This takes you to the Create vocabulary ﬁlter page. Enter a name for your new custom vocabulary ﬁlter. Select the File upload or S3 location option under Vocabulary input source. Then specify the location of your custom vocabulary ﬁle. 4. Optionally, add tags to your custom vocabulary ﬁlter. Once you have all ﬁelds completed, select Create vocabulary ﬁlter at the bottom of the page. If there are no errors processing your ﬁle, this takes you back to the Vocabulary ﬁlters page. Your custom vocabulary ﬁlter is now ready to use. Creating custom vocabulary ﬁlters 291 Amazon Transcribe AWS CLI Developer Guide This example uses the create-vocabulary-ﬁlter command to process a word list into a usable custom vocabulary ﬁlter. For more information, see CreateVocabularyFilter. Option 1: You can include your list of words to your request using the words parameter. aws transcribe create-vocabulary-filter \ --vocabulary-filter-name my-first-vocabulary-filter \ --language-code en-US \ --words profane,offensive,Amazon,Transcribe Option 2: You can save your list of words as a text ﬁle and upload it to an Amazon S3 bucket, then include the ﬁle's URI in your request using the vocabulary-filter-file-uri parameter. aws transcribe create-vocabulary-filter \ --vocabulary-filter-name my-first-vocabulary-filter \ --language-code en-US \ --vocabulary-filter-file-uri s3://amzn-s3-demo-bucket/my-vocabulary-filters/my- vocabulary-filter.txt Here's another example using the create-vocabulary-ﬁlter command, and a request body that creates your custom vocabulary ﬁlter. aws transcribe create-vocabulary-filter \ --cli-input-json file://filepath/my-first-vocab-filter.json The ﬁle my-ﬁrst-vocab-ﬁlter.json contains the following request body. Option 1: You can include your list of words to your request using the Words parameter. { "VocabularyFilterName": "my-first-vocabulary-filter", "LanguageCode": "en-US", "Words": [ "profane","offensive","Amazon","Transcribe" ] } Option 2: You can save your list of words as a text ﬁle and upload it to an Amazon S3 bucket, then include the ﬁle's URI in your request using the VocabularyFilterFileUri parameter. Creating custom vocabulary ﬁlters 292 Amazon Transcribe Developer Guide { "VocabularyFilterName": "my-first-vocabulary-filter", "LanguageCode": "en-US", "VocabularyFilterFileUri": "s3://amzn-s3-demo-bucket/my-vocabulary-filters/my- vocabulary-filter.txt" } Note If you include VocabularyFilterFileUri in your request, you cannot use Words; you must choose one or the other. AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a custom vocabulary ﬁlter using the create_vocabulary_ﬁlter method. For more information, see CreateVocabularyFilter. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. Option 1: You can include your list of words to your request using the Words parameter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') vocab_name = "my-first-vocabulary-filter" response = transcribe.create_vocabulary_filter( LanguageCode = 'en-US', VocabularyFilterName = vocab_name, Words = [ 'profane','offensive','Amazon','Transcribe' ] ) Option 2: You can save your list of words as a text ﬁle and upload it to an Amazon S3 bucket, then include the ﬁle's URI in your request using the VocabularyFilterFileUri parameter. from __future__ import print_function Creating custom vocabulary ﬁlters 293 Amazon Transcribe import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') vocab_name = "my-first-vocabulary-filter" response = transcribe.create_vocabulary_filter( LanguageCode = 'en-US', VocabularyFilterName = vocab_name, Developer Guide VocabularyFilterFileUri = 's3://amzn-s3-demo-bucket/my-vocabulary-filters/my- vocabulary-filter.txt' ) Note If you include VocabularyFilterFileUri in your request, you cannot use Words; you must choose one or the other. Note If you create a new Amazon S3 bucket for your custom vocabulary ﬁlter ﬁles, make sure the IAM role making the CreateVocabularyFilter request has permissions to access this bucket. If the role doesn't have the correct permissions, your request fails. You can optionally specify an IAM role within your request by including the DataAccessRoleArn parameter. For more information on IAM roles and policies in Amazon Transcribe, see Amazon Transcribe identity-based policy examples. Using a custom vocabulary ﬁlter Once your custom vocabulary ﬁlter is created, you can include it in your transcription requests; refer to the following sections for examples. The language of
transcribe-dg-060	transcribe-dg.pdf	60	create a new Amazon S3 bucket for your custom vocabulary ﬁlter ﬁles, make sure the IAM role making the CreateVocabularyFilter request has permissions to access this bucket. If the role doesn't have the correct permissions, your request fails. You can optionally specify an IAM role within your request by including the DataAccessRoleArn parameter. For more information on IAM roles and policies in Amazon Transcribe, see Amazon Transcribe identity-based policy examples. Using a custom vocabulary ﬁlter Once your custom vocabulary ﬁlter is created, you can include it in your transcription requests; refer to the following sections for examples. The language of the custom vocabulary ﬁlter you're including in your request must match the language code you specify for your media. If you use language identiﬁcation and specify multiple language options, you can include one custom vocabulary ﬁlter per speciﬁed language. If the languages of your custom vocabulary ﬁlters don't match the language identiﬁed in your audio, your ﬁlters are not applied to your transcription and there are no warnings or errors. Using a custom vocabulary ﬁlter 294 Amazon Transcribe Developer Guide Using a custom vocabulary ﬁlter in a batch transcription To use a custom vocabulary ﬁlter with a batch transcription, see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Using a custom vocabulary ﬁlter in a batch transcription 295 Amazon Transcribe Developer Guide Name your job and specify your input media. Optionally include any other ﬁelds, then choose Next. 3. On the Conﬁgure job page, in the Content removal panel, toggle on Vocabulary ﬁltering. Using a custom vocabulary ﬁlter in a batch transcription 296 Amazon Transcribe Developer Guide 4. Select your custom vocabulary ﬁlter from the dropdown menu and specify the ﬁltration method. 5. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job command and Settings parameter with the VocabularyFilterName and VocabularyFilterMethod sub-parameters. For more information, see StartTranscriptionJob and Settings. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --settings VocabularyFilterName=my-first-vocabulary-filter,VocabularyFilterMethod=mask Here's another example using the start-transcription-job command, and a request body that includes your custom vocabulary ﬁlter with that job. aws transcribe start-transcription-job \ Using a custom vocabulary ﬁlter in a batch transcription 297 Amazon Transcribe --region us-west-2 \ --cli-input-json file://my-first-vocabulary-filter-job.json Developer Guide The ﬁle my-ﬁrst-vocabulary-ﬁlter-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Settings": { "VocabularyFilterName": "my-first-vocabulary-filter", "VocabularyFilterMethod": "mask" } } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to include a custom vocabulary ﬁlter using the Settings argument for the start_transcription_job method. For more information, see StartTranscriptionJob and Settings. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Using a custom vocabulary ﬁlter in a batch transcription 298 Amazon Transcribe Settings = { 'VocabularyFilterName': 'my-first-vocabulary-filter', 'VocabularyFilterMethod': 'mask' Developer Guide } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using a custom vocabulary ﬁlter in a streaming transcription To use a custom vocabulary ﬁlter with a streaming transcription, see the following for examples: AWS Management Console 1. 2. Sign into the AWS Management Console. In the navigation pane, choose Real-time transcription. Scroll down to Content removal settings and expand this ﬁeld if it is minimized. Using a custom vocabulary ﬁlter in a streaming transcription 299 Amazon Transcribe Developer Guide 3. Toggle on Vocabulary ﬁltering. Select a custom vocabulary ﬁlter from the dropdown menu and specify the ﬁltration method. Include any other settings you want to apply to your stream. Using a custom vocabulary ﬁlter in a streaming transcription 300 Amazon Transcribe Developer Guide 4. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming. HTTP/2 stream This example creates an HTTP/2 request that includes your custom vocabulary ﬁlter and ﬁlter method. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-vocabulary-filter-name: my-first-vocabulary-filter x-amzn-transcribe-vocabulary-filter-method: mask transfer-encoding: chunked Parameter deﬁnitions can be found in the API Reference;
transcribe-dg-061	transcribe-dg.pdf	61	Select Start streaming and begin speaking. To end your dictation, select Stop streaming. HTTP/2 stream This example creates an HTTP/2 request that includes your custom vocabulary ﬁlter and ﬁlter method. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-vocabulary-filter-name: my-first-vocabulary-filter x-amzn-transcribe-vocabulary-filter-method: mask transfer-encoding: chunked Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that applies your custom vocabulary ﬁlter to a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request Using a custom vocabulary ﬁlter in a streaming transcription 301 Amazon Transcribe Developer Guide &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &language-code=en-US &media-encoding=flac &sample-rate=16000 &vocabulary-filter-name=my-first-vocabulary-filter &vocabulary-filter-method=mask Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Using a custom vocabulary ﬁlter in a streaming transcription 302 Amazon Transcribe Developer Guide Detecting toxic speech Toxic speech detection is designed to help moderate social media platforms that involve peer-to- peer dialogue, such as online gaming and social chat platforms. The use of toxic speech can be deeply detrimental to individuals, peer groups, and communities. Flagging harmful language helps organizations keep conversations civil and maintain a safe and inclusive online environment for users to create, share and participate freely. Amazon Transcribe Toxicity Detection leverages both audio and text-based cues to identify and classify voice-based toxic content across seven categories including sexual harassment, hate speech, threat, abuse, profanity, insult, and graphic. In addition to text, Amazon Transcribe Toxicity Detection uses speech cues, such as tones and pitch to hone in on toxic intent in speech. This is an improvement from standard content moderation systems that are designed to focus only on speciﬁc terms, without accounting for intention. Amazon Transcribe ﬂags and categorizes toxic speech, which minimizes the volume of data that must be manually processed. This enables content moderators to quickly and eﬃciently manage the discourse on their platforms. Toxic speech categories include: • Profanity: Speech that contains words, phrases, or acronyms that are impolite, vulgar, or oﬀensive. • Hate speech: Speech that criticizes, insults, denounces, or dehumanizes a person or group on the basis of an identity (such as race, ethnicity, gender, religion, sexual orientation, ability, and national origin). • Sexual: Speech that indicates sexual interest, activity, or arousal using direct or indirect references to body parts, physical traits, or sex. • Insults: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language. This type of language is also labeled as bullying. • Violence or threat: Speech that includes threats seeking to inﬂict pain, injury, or hostility toward a person or group. • Graphic: Speech that uses visually descriptive and unpleasantly vivid imagery. This type of language is often intentionally verbose to amplify a recipient's discomfort. • Harassment or abusive: Speech intended to aﬀect the psychological well-being of the recipient, including demeaning and objectifying terms. This type of language is also labeled as harassment. 303 Amazon Transcribe Developer Guide Toxicity detection analyzes speech segments (the speech between natural pauses) and assigns conﬁdence scores to these segments. Conﬁdence scores are values between 0 and 1. A larger conﬁdence score indicates a greater likelihood that the content is toxic speech in the associated category. You can use these conﬁdence scores to set the appropriate threshold of toxicity detection for your use case. Note Toxicity detection is only available for batch transcriptions in US English (en-US). View example output in JSON format. Using toxic speech detection Using toxic speech detection in a batch transcription To use toxic speech detection with a batch transcription, see the following for examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Using toxic speech detection 304 Amazon Transcribe Developer Guide 3. On the Specify job details page, you can also enable PII redaction if you want. Note that the other listed options are not supported with Toxicity detection. Select Next. This takes you to the Conﬁgure job - optional page. In the Audio settings panel, select Toxicity detection. Using toxic speech detection in a batch transcription 305 Amazon Transcribe Developer Guide 4. Select Create job to run your transcription job. 5. Once your transcription job is complete, you
transcribe-dg-062	transcribe-dg.pdf	62	then select Create job (top right). This opens the Specify job details page. Using toxic speech detection 304 Amazon Transcribe Developer Guide 3. On the Specify job details page, you can also enable PII redaction if you want. Note that the other listed options are not supported with Toxicity detection. Select Next. This takes you to the Conﬁgure job - optional page. In the Audio settings panel, select Toxicity detection. Using toxic speech detection in a batch transcription 305 Amazon Transcribe Developer Guide 4. Select Create job to run your transcription job. 5. Once your transcription job is complete, you can download your transcript from the Download drop-down menu in the transcription job's detail page. Using toxic speech detection in a batch transcription 306 Amazon Transcribe AWS CLI Developer Guide This example uses the start-transcription-job command and ToxicityDetection parameter. For more information, see StartTranscriptionJob and ToxicityDetection. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --toxicity-detection ToxicityCategories=ALL Here's another example using the start-transcription-job command, and a request body that includes toxicity detection. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://filepath/my-first-toxicity-job.json The ﬁle my-ﬁrst-toxicity-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "ToxicityDetection": [ { "ToxicityCategories": [ "ALL" ] } ] } Using toxic speech detection in a batch transcription 307 Amazon Transcribe AWS SDK for Python (Boto3) Developer Guide This example uses the AWS SDK for Python (Boto3) to enable ToxicityDetection for the start_transcription_job method. For more information, see StartTranscriptionJob and ToxicityDetection. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', ToxicityDetection = [ { 'ToxicityCategories': ['ALL'] } ] ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Using toxic speech detection in a batch transcription 308 Amazon Transcribe Example output Developer Guide Toxic speech is tagged and categorized in your transcription output. Each instance of toxic speech is categorized and assigned a conﬁdence score (a value between 0 and 1). A larger conﬁdence value indicates a greater likelihood that the content is toxic speech within the speciﬁed category. Example output (JSON) The following is an example output in JSON format showing categorized toxic speech with associated conﬁdence scores. { "jobName": "my-toxicity-job", "accountId": "111122223333", "results": { "transcripts": [...], "items":[...], "toxicity_detection": [ { "text": "What the * are you doing man? That's why I didn't want to play with your * . man it was a no, no I'm not calming down * man. I well I spent I spent too much * money on this game.", "toxicity": 0.7638, "categories": { "profanity": 0.9913, "hate_speech": 0.0382, "sexual": 0.0016, "insult": 0.6572, "violence_or_threat": 0.0024, "graphic": 0.0013, "harassment_or_abuse": 0.0249 }, "start_time": 8.92, "end_time": 21.45 }, Items removed for brevity { "text": "What? Who? What the * did you just say to me? What's your address? What is your * address? I will pull up right now on your * * man. Take your * back to , tired of this **.", "toxicity": 0.9816, "categories": { Example output 309 Amazon Transcribe Developer Guide "profanity": 0.9865, "hate_speech": 0.9123, "sexual": 0.0037, "insult": 0.5447, "violence_or_threat": 0.5078, "graphic": 0.0037, "harassment_or_abuse": 0.0613 }, "start_time": 43.459, "end_time": 54.639 }, ] }, ... "status": "COMPLETED" } Example output 310 Amazon Transcribe Developer Guide Redacting or identifying personally identiﬁable information Redaction is used to mask or remove sensitive content, in the form of personally identiﬁable information (PII), from your transcripts. The types of PII Amazon Transcribe can redact varies between batch and streaming transcriptions. To view the PII list for each transcription method, refer to Redacting PII in your batch job and Redacting or identifying PII in a real-time stream. With streaming transcriptions, you also have the option to ﬂag PII without redacting it; refer to Example PII identiﬁcation output for an output example. When redaction is enabled, you have the option to generate only a redacted transcript or both a redacted transcript and an unredacted transcript. If you choose to generate only a redacted transcript, note that your media is the only place where the complete conversation is stored. If you delete your original media, there is no record of the unredacted PII. Because of this, it may be prudent to generate an unredacted transcript in addition to a redacted one. To learn more about PII redaction with batch transcriptions, refer to: Redacting PII in your batch job. To learn more about PII redaction or
transcribe-dg-063	transcribe-dg.pdf	63	is enabled, you have the option to generate only a redacted transcript or both a redacted transcript and an unredacted transcript. If you choose to generate only a redacted transcript, note that your media is the only place where the complete conversation is stored. If you delete your original media, there is no record of the unredacted PII. Because of this, it may be prudent to generate an unredacted transcript in addition to a redacted one. To learn more about PII redaction with batch transcriptions, refer to: Redacting PII in your batch job. To learn more about PII redaction or identiﬁcation with streaming transcriptions, refer to: Redacting or identifying PII in a real-time stream. Important The redaction feature is designed to identify and remove sensitive data. However, due to the predictive nature of machine learning, Amazon Transcribe may not identify and remove all instances of sensitive data in your transcript. We strongly recommend that you review any redacted output to ensure it meets your needs. The redaction feature does not meet the requirements for de-identiﬁcation under medical privacy laws, such as the U.S. Health Insurance Portability and Accountability Act of 1996 (HIPAA). For a video walkthrough of the Amazon Transcribe's redaction feature, see Use content redaction to identify & redact PII. 311 Amazon Transcribe Developer Guide Redacting PII in your batch job When redacting personally identiﬁable information (PII) from a transcript during a batch transcription job, Amazon Transcribe replaces each identiﬁed instance of PII with [PII] in the main text body of your transcript. You can also view the type of PII that is redacted in the word- for-word portion of the transcription output. For an output sample, see Example redacted output (batch). Redaction with batch transcriptions is available with US English (en-US) and US Spanish (es-US). Redaction is not compatible with language identiﬁcation. Both redacted and unredacted transcripts are stored in the same output Amazon S3 bucket. Amazon Transcribe stores them in a bucket you specify or in the default Amazon S3 bucket managed by the service. Types of PII Amazon Transcribe can recognize for batch transcriptions PII type ADDRESS ALL BANK_ACCOUNT_NUMBER BANK_ROUTING Description A physical address, such as 100 Main Street, Anytown, USA or Suite #12, Building 123. An address can include a street, building, location, city, state, country, county, zip, precinct, neighborhood, and more. Redact or identify all PII types listed in this table. A US bank account number. These are typically between 10 - 12 digits long, but Amazon Transcribe also recognizes bank account numbers when only the last 4 digits are present. A US bank account routing number. These are typically 9 digits long, but Amazon Transcrib e also recognizes routing numbers when only the last 4 digits are present. Redacting PII in your batch job 312 Amazon Transcribe PII type CREDIT_DEBIT_CVV CREDIT_DEBIT_EXPIRY CREDIT_DEBIT_NUMBER EMAIL NAME PHONE Developer Guide Description A 3-digit card veriﬁcation code (CVV) that is present on VISA, MasterCard, and Discover credit and debit cards. In American Express credit or debit cards, it is a 4-digit numeric code. The expiration date for a credit or debit card. This number is usually 4 digits long and formatted as month/year or MM/YY. For example, Amazon Transcribe can recognize expiration dates such as 01/21, 01/2021, and Jan 2021. The number for a credit or debit card. These numbers can vary from 13 to 16 digits in length, but Amazon Transcribe also recognize s credit or debit card numbers when only the last 4 digits are present. An email address, such as [email protected] om. An individual's name. This entity type does not include titles, such as Mr., Mrs., Miss, or Dr. Amazon Transcribe does not apply this entity type to names that are part of organizations or addresses. For example, Amazon Transcrib e recognizes the John Doe Organization as an organization, and Jane Doe Street as an address. A phone number. This entity type also includes fax and pager numbers. Redacting PII in your batch job 313 Amazon Transcribe PII type PIN SSN Developer Guide Description A 4-digit personal identiﬁcation number (PIN) that allows someone to access their bank account information. A Social Security Number (SSN) is a 9- digit number that is issued to US citizens, permanent residents, and temporary working residents. Amazon Transcribe also recognizes Social Security Numbers when only the last 4 digits are present. You can start a batch transcription job using the AWS Management Console, AWS CLI, or AWS SDK. AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This will open the Specify job details page. 3. After ﬁlling in your desired ﬁelds on the Specify job details page, select Next to go to the Conﬁgure job - optional page. Here you'll ﬁnd the
transcribe-dg-064	transcribe-dg.pdf	64	citizens, permanent residents, and temporary working residents. Amazon Transcribe also recognizes Social Security Numbers when only the last 4 digits are present. You can start a batch transcription job using the AWS Management Console, AWS CLI, or AWS SDK. AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This will open the Specify job details page. 3. After ﬁlling in your desired ﬁelds on the Specify job details page, select Next to go to the Conﬁgure job - optional page. Here you'll ﬁnd the Content removal panel with the PII redaction toggle. Redacting PII in your batch job 314 Amazon Transcribe Developer Guide 4. Once you select PII redaction, you have the option to select all PII types you want to redact. You can also choose to have an unredacted transcript if you select Include unredacted transcript in job output box. Redacting PII in your batch job 315 Amazon Transcribe Developer Guide 5. Select Create job to run your transcription job. AWS CLI This example uses the start-transcription-job command and content-redaction parameter. For more information, see StartTranscriptionJob and ContentRedaction. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ Redacting PII in your batch job 316 Amazon Transcribe --content-redaction Developer Guide RedactionType=PII,RedactionOutput=redacted,PiiEntityTypes=NAME,ADDRESS,BANK_ACCOUNT_NUMBER Here's another example using the start-transcription-job method, and the request body redacts PII for that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://filepath/my-first-redaction-job.json The ﬁle my-ﬁrst-redaction-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "ContentRedaction": { "RedactionOutput":"redacted", "RedactionType":"PII", "PiiEntityTypes": [ "NAME", "ADDRESS", "BANK_ACCOUNT_NUMBER" ] } } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to redact content using the ContentRedaction argument for the start_transcription_job method. For more information, see StartTranscriptionJob and ContentRedaction. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function Redacting PII in your batch job 317 Amazon Transcribe import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" Developer Guide transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', ContentRedaction = { 'RedactionOutput':'redacted', 'RedactionType':'PII', 'PiiEntityTypes': [ 'NAME','ADDRESS','BANK_ACCOUNT_NUMBER' ] } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Note PII redaction for batch jobs is only supported in these AWS Regions: Asia Paciﬁc (Hong Kong), Asia Paciﬁc (Mumbai), Asia Paciﬁc (Seoul), Asia Paciﬁc (Singapore), Asia Paciﬁc (Sydney), Asia Paciﬁc (Tokyo), GovCloud (US-West), Canada (Central), EU (Frankfurt), EU (Ireland), EU (London), EU (Paris), Middle East (Bahrain), South America (Sao Paulo), US East (N. Virginia), US East (Ohio), US West (Oregon), and US West (N. California). Redacting PII in your batch job 318 Amazon Transcribe Developer Guide Redacting or identifying PII in a real-time stream When redacting personally identiﬁable information (PII) from a streaming transcription, Amazon Transcribe replaces each identiﬁed instance of PII with [PII] in your transcript. An additional option available for streaming transcriptions is PII identiﬁcation. When you activate PII Identiﬁcation, Amazon Transcribe labels the PII in your transcription results under an Entities object. For an output sample, see Example redacted streaming output and Example PII identiﬁcation output. Redaction and identiﬁcation of PII with streaming transcriptions is available with these English dialects: Australian (en-AU), British (en-GB), US (en-US) and Spanish US dialect (es-US). PII identiﬁcation and redaction for streaming jobs is performed only upon complete transcription of the audio segments. Types of PII Amazon Transcribe can recognize for streaming transcriptions PII type ADDRESS ALL BANK_ACCOUNT_NUMBER BANK_ROUTING Description A physical address, such as 100 Main Street, Anytown, USA or Suite #12, Building 123. An address can include a street, building, location, city, state, country, county, zip, precinct, neighborhood, and more. Redact or identify all PII types listed in this table. A US bank account number. These are typically between 10 - 12 digits long, but Amazon Transcribe also recognizes bank account numbers when only the last 4 digits are present. A US bank account routing number. These are typically 9 digits long, but Amazon Transcrib e also recognizes routing numbers when only the last 4 digits are present. Redacting or identifying PII in a real-time stream 319 Amazon Transcribe PII type CREDIT_DEBIT_CVV CREDIT_DEBIT_EXPIRY CREDIT_DEBIT_NUMBER EMAIL NAME PHONE Developer Guide Description A 3-digit card veriﬁcation code (CVV) that is present on VISA, MasterCard, and Discover credit and debit cards. In American Express credit or debit cards, it is a 4-digit numeric code. The expiration date for a credit or debit card. This number is usually 4 digits
transcribe-dg-065	transcribe-dg.pdf	65	4 digits are present. A US bank account routing number. These are typically 9 digits long, but Amazon Transcrib e also recognizes routing numbers when only the last 4 digits are present. Redacting or identifying PII in a real-time stream 319 Amazon Transcribe PII type CREDIT_DEBIT_CVV CREDIT_DEBIT_EXPIRY CREDIT_DEBIT_NUMBER EMAIL NAME PHONE Developer Guide Description A 3-digit card veriﬁcation code (CVV) that is present on VISA, MasterCard, and Discover credit and debit cards. In American Express credit or debit cards, it is a 4-digit numeric code. The expiration date for a credit or debit card. This number is usually 4 digits long and formatted as month/year or MM/YY. For example, Amazon Transcribe can recognize expiration dates such as 01/21, 01/2021, and Jan 2021. The number for a credit or debit card. These numbers can vary from 13 to 16 digits in length, but Amazon Transcribe also recognize s credit or debit card numbers when only the last 4 digits are present. An email address, such as [email protected] om. An individual's name. This entity type does not include titles, such as Mr., Mrs., Miss, or Dr. Amazon Transcribe does not apply this entity type to names that are part of organizations or addresses. For example, Amazon Transcrib e recognizes the John Doe Organization as an organization, and Jane Doe Street as an address. A phone number. This entity type also includes fax and pager numbers. Redacting or identifying PII in a real-time stream 320 Amazon Transcribe PII type PIN SSN Developer Guide Description A 4-digit personal identiﬁcation number (PIN) that allows someone to access their bank account information. A Social Security Number (SSN) is a 9- digit number that is issued to US citizens, permanent residents, and temporary working residents. Amazon Transcribe also recognizes Social Security Numbers when only the last 4 digits are present. You can start a streaming transcription using the AWS Management Console, WebSocket, or HTTP/2. AWS Management Console 1. 2. Sign into the AWS Management Console. In the navigation pane, choose Real-time transcription. Scroll down to Content removal settings and expand this ﬁeld if it is minimized. Redacting or identifying PII in a real-time stream 321 Amazon Transcribe Developer Guide 3. Toggle on PII Identiﬁcation & redaction. 4. Select Identiﬁcation only or Identiﬁcation & redaction, then select the PII entity types you want to identify or redact in your transcript. Redacting or identifying PII in a real-time stream 322 Amazon Transcribe Developer Guide 5. You're now ready to transcribe your stream. Select Start streaming and begin speaking. To end your dictation, select Stop streaming. WebSocket stream This example creates a presigned URL that uses PII redaction (or PII identiﬁcation) in a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartStreamTranscription. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/stream-transcription- websocket? &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z Redacting or identifying PII in a real-time stream 323 Amazon Transcribe &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &language-code=en-US &media-encoding=flac &sample-rate=16000 &pii-entity-types=NAME,ADDRESS &content-redaction-type=PII (or &content-identification-type=PII) Developer Guide You cannot use both content-identification-type and content-redaction-type in the same request. Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. HTTP/2 stream This example creates an HTTP/2 request with PII identiﬁcation or PII redaction enabled. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartStreamTranscription. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 x-amzn-transcribe-content-identification-type: PII (or x-amzn-transcribe-content- redaction-type: PII) x-amzn-transcribe-pii-entity-types: NAME,ADDRESS transfer-encoding: chunked You cannot use both content-identification-type and content-redaction-type in the same request. Redacting or identifying PII in a real-time stream 324 Amazon Transcribe Developer Guide Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Note PII redaction for streaming is only supported in these AWS Regions: Asia Paciﬁc (Seoul), Asia Paciﬁc (Sydney), Asia Paciﬁc (Tokyo), Canada (Central), EU (Frankfurt), EU (Ireland), EU (London), US East (N. Virginia), US East (Ohio), and US West (Oregon). Example PII redaction and identiﬁcation output The following examples show redacted output from batch and streaming jobs, and PII identiﬁcation from a streaming job. Transcription jobs using content redaction generate two types of confidence values. The Automatic Speech Recognition (ASR) conﬁdence indicates the items that have the type of pronunciation or punctuation is a speciﬁc utterance. In the following transcript output, the word Good has a confidence of 1.0. This conﬁdence value indicates that Amazon Transcribe is 100 percent conﬁdent that the word uttered in this transcript is 'Good'. The confidence value for
transcribe-dg-066	transcribe-dg.pdf	66	US East (Ohio), and US West (Oregon). Example PII redaction and identiﬁcation output The following examples show redacted output from batch and streaming jobs, and PII identiﬁcation from a streaming job. Transcription jobs using content redaction generate two types of confidence values. The Automatic Speech Recognition (ASR) conﬁdence indicates the items that have the type of pronunciation or punctuation is a speciﬁc utterance. In the following transcript output, the word Good has a confidence of 1.0. This conﬁdence value indicates that Amazon Transcribe is 100 percent conﬁdent that the word uttered in this transcript is 'Good'. The confidence value for a [PII] tag is the conﬁdence that the speech it ﬂagged for redaction is truly PII. In the following transcript output, the confidence of 0.9999 indicates that Amazon Transcribe is 99.99 percent conﬁdent that the entity it redacted in the transcript is PII. Example redacted output (batch) { "jobName": "my-first-transcription-job", "accountId": "111122223333", "isRedacted": true, "results": { "transcripts": [ { "transcript": "Good morning, everybody. My name is [PII], and today I feel like sharing a whole lot of personal information with you. Let's start with my Social Security number [PII]. My credit card number is [PII] and my C V V code is [PII]. Example output 325 Amazon Transcribe Developer Guide I hope that Amazon Transcribe is doing a good job at redacting that personal information away. Let's check." } ], "items": [ { "id": 0, "start_time": "2.86", "end_time": "3.35", "alternatives": [ { "confidence": "1.0", "content": "Good" } ], "type": "pronunciation" }, Items removed for brevity { "id": 8, "start_time": "5.56", "end_time": "6.25", "alternatives": [ { "content": "[PII]", "redactions": [ { "confidence": "0.9999", "type": "NAME", "category": "PII" } ] } ], "type": "pronunciation" }, Items removed for brevity ], }, "status": "COMPLETED" } Example redacted output (batch) 326 Amazon Transcribe Developer Guide Here's the unredacted transcript for comparison: { "jobName": "job id", "accountId": "111122223333", "isRedacted": false, "results": { "transcripts": [ { "transcript": "Good morning, everybody. My name is Mike, and today I feel like sharing a whole lot of personal information with you. Let's start with my Social Security number 000000000. My credit card number is 5555555555555555 and my C V V code is 000. I hope that Amazon Transcribe is doing a good job at redacting that personal information away. Let's check." } ], "items": [ { "id": 0, "start_time": "2.86", "end_time": "3.35", "alternatives": [ { "confidence": "1.0", "content": "Good" } ], "type": "pronunciation" }, Items removed for brevity { "id": 8, "start_time": "5.56", "end_time": "6.25", "alternatives": [ { "confidence": "0.9999", "content": "Mike", { ], "type": "pronunciation" Example redacted output (batch) 327 Developer Guide Amazon Transcribe }, Items removed for brevity ], }, "status": "COMPLETED" } Example redacted streaming output { "TranscriptResultStream": { "TranscriptEvent": { "Transcript": { "Results": [ { "Alternatives": [ { "Transcript": "my name is [NAME]", "Items": [ { "Content": "my", "EndTime": 0.3799375, "StartTime": 0.0299375, "Type": "pronunciation" }, { "Content": "name", "EndTime": 0.5899375, "StartTime": 0.3899375, "Type": "pronunciation" }, { "Content": "is", "EndTime": 0.7899375, "StartTime": 0.5999375, "Type": "pronunciation" }, { "Content": "[NAME]", "EndTime": 1.0199375, "StartTime": 0.7999375, "Type": "pronunciation" } Example redacted streaming output 328 Amazon Transcribe Developer Guide ], "Entities": [ { "Content": "[NAME]", "Category": "PII", "Type": "NAME", "StartTime" : 0.7999375, "EndTime" : 1.0199375, "Confidence": 0.9989 } ] } ], "EndTime": 1.02, "IsPartial": false, "ResultId": "12345a67-8bc9-0de1-2f34-a5b678c90d12", "StartTime": 0.0199375 } ] } } } } Example PII identiﬁcation output PII identiﬁcation is an additional feature that you can use with your streaming transcription job. The identiﬁed PII is listed in each segment's Entities section. { "TranscriptResultStream": { "TranscriptEvent": { "Transcript": { "Results": [ { "Alternatives": [ { "Transcript": "my name is mike", "Items": [ { "Content": "my", "EndTime": 0.3799375, "StartTime": 0.0299375, Example PII identiﬁcation output 329 Amazon Transcribe Developer Guide "Type": "pronunciation" }, { "Content": "name", "EndTime": 0.5899375, "StartTime": 0.3899375, "Type": "pronunciation" }, { "Content": "is", "EndTime": 0.7899375, "StartTime": 0.5999375, "Type": "pronunciation" }, { "Content": "mike", "EndTime": 0.9199375, "StartTime": 0.7999375, "Type": "pronunciation" } ], "Entities": [ { "Content": "mike", "Category": "PII", "Type": "NAME", "StartTime" : 0.7999375, "EndTime" : 1.0199375, "Confidence": 0.9989 } ] } ], "EndTime": 1.02, "IsPartial": false, "ResultId": "12345a67-8bc9-0de1-2f34-a5b678c90d12", "StartTime": 0.0199375 } ] } } } Example PII identiﬁcation output 330 Amazon Transcribe } Developer Guide Example PII identiﬁcation output 331 Amazon Transcribe Developer Guide Creating video subtitles Amazon Transcribe supports WebVTT (.vtt) and SubRip (.srt) output for use as video subtitles. You can select one or both ﬁle types when setting up your batch video transcription job. When using the subtitle feature, your selected subtitle ﬁle(s) and a regular transcript ﬁle (containing additional information) are produced. Subtitle and transcription ﬁles are output to the same destination. Subtitles are displayed at the same time text is spoken, and remain visible until there is a natural pause or the speaker ﬁnishes talking. Note that if you enable subtitles in
transcribe-dg-067	transcribe-dg.pdf	67	identiﬁcation output 331 Amazon Transcribe Developer Guide Creating video subtitles Amazon Transcribe supports WebVTT (.vtt) and SubRip (.srt) output for use as video subtitles. You can select one or both ﬁle types when setting up your batch video transcription job. When using the subtitle feature, your selected subtitle ﬁle(s) and a regular transcript ﬁle (containing additional information) are produced. Subtitle and transcription ﬁles are output to the same destination. Subtitles are displayed at the same time text is spoken, and remain visible until there is a natural pause or the speaker ﬁnishes talking. Note that if you enable subtitles in your transcription request and your audio contains no speech, a subtitle ﬁle is not created. Important Amazon Transcribe uses a default start index of 0 for subtitle output, which diﬀers from the more widely used value of 1. If you require a start index of 1, you can specify this in the AWS Management Console or in your API request using the OutputStartIndex parameter. Using the incorrect start index can result in compatibility errors with other services, so be sure to verify which start index you require before creating your subtitles. If you're uncertain which value to use, we recommend choosing 1. Refer to Subtitles for more information. Features supported with subtitles: • Content redaction — Any redacted content is reﬂected as 'PII' in both your subtitle and regular transcript output ﬁles. The audio is not altered. • Vocabulary ﬁlters — Subtitle ﬁles are generated from the transcription ﬁle, so any words you ﬁlter in your standard transcription output are also ﬁltered in your subtitles. Filtered content is displayed as whitespace or *** in your transcript and subtitle ﬁles. The audio is not altered. • Speaker diarization — If there are multiple speakers in a given subtitle segment, dashes are used to distinguish each speaker. This applies to both WebVTT and SubRip formats; for example: • -- Text spoken by Person 1 • -- Text spoken by Person 2 Subtitle ﬁles are stored in the same Amazon S3 location as your transcription output. 332 Amazon Transcribe Developer Guide See Amazon Transcribe Video Snacks: Creating Video Subtitles Without Writing Any Code for a video walkthrough of creating subtitles. Generating subtitle ﬁles You can create subtitle ﬁles using the AWS Management Console, AWS CLI, or AWS SDKs; see the following examples: AWS Management Console 1. 2. Sign in to the AWS Management Console. In the navigation pane, choose Transcription jobs, then select Create job (top right). This opens the Specify job details page. Subtitle options are located in the Output data panel. 3. Select the formats you want for your subtitles ﬁles, then choose a value for your start index. Note that the Amazon Transcribe default is 0, but 1 is more widely used. If you're uncertain which value to use, we recommend choosing 1, as this may improve compatibility with other services. 4. Fill in any other ﬁelds you want to include on the Specify job details page, then select Next. This takes you to the Conﬁgure job - optional page. 5. Select Create job to run your transcription job. Generating subtitle ﬁles 333 Amazon Transcribe AWS CLI Developer Guide This example uses the start-transcription-job command and Subtitles parameter. For more information, see StartTranscriptionJob and Subtitles. aws transcribe start-transcription-job \ --region us-west-2 \ --transcription-job-name my-first-transcription-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-bucket-name amzn-s3-demo-bucket \ --output-key my-output-files/ \ --language-code en-US \ --subtitles Formats=vtt,srt,OutputStartIndex=1 Here's another example using the start-transcription-job command, and a request body that adds subtitles to that job. aws transcribe start-transcription-job \ --region us-west-2 \ --cli-input-json file://my-first-subtitle-job.json The ﬁle my-ﬁrst-subtitle-job.json contains the following request body. { "TranscriptionJobName": "my-first-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Subtitles": { "Formats": [ "vtt","srt" ], "OutputStartIndex": 1 } } Generating subtitle ﬁles 334 Amazon Transcribe Developer Guide AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to add subtitles using the Subtitles argument for the start_transcription_job method. For more information, see StartTranscriptionJob and Subtitles. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Subtitles = { 'Formats': [ 'vtt','srt' ], 'OutputStartIndex': 1 } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Generating subtitle ﬁles 335 Amazon Transcribe Developer Guide Analyzing call center audio with Call Analytics Use Amazon Transcribe Call Analytics to gain insight into customer-agent interactions. Call Analytics is designed speciﬁcally for call center audio and automatically provides you with valuable data
transcribe-dg-068	transcribe-dg.pdf	68	transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" transcribe.start_transcription_job( TranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'my-output-files/', LanguageCode = 'en-US', Subtitles = { 'Formats': [ 'vtt','srt' ], 'OutputStartIndex': 1 } ) while True: status = transcribe.get_transcription_job(TranscriptionJobName = job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Generating subtitle ﬁles 335 Amazon Transcribe Developer Guide Analyzing call center audio with Call Analytics Use Amazon Transcribe Call Analytics to gain insight into customer-agent interactions. Call Analytics is designed speciﬁcally for call center audio and automatically provides you with valuable data relating to each call and each participant. You can also hone in on data at speciﬁc points throughout call. For example, you can compare customer sentiment in a call's ﬁrst few seconds to the last quarter of the call to see if your agent provided a positive experience. Other use case examples are listed in the following section. Call Analytics is available for post-call and real-time transcriptions. If you're transcribing a ﬁle located in an Amazon S3 bucket, you're performing a post-call transcription. If you're transcribing an audio stream, you're performing a real-time transcription. These two transcription methods oﬀer diﬀerent Call Analytics insights and features. For more detail on each method, see Post-call analytics and Real-time Call Analytics. With real-time Call Analytics transcriptions, you can also include post-call analytics in your request. Your post-call analytics transcript is stored in the Amazon S3 bucket that you specify in your request. Refer to Post-call analytics with real-time transcriptions for more information. API operations speciﬁc to Call Analytics Post-call: CreateCallAnalyticsCategory, DeleteCallAnalyticsCategory, DeleteCallAnalyticsJob, GetCallAnalyticsCategory, GetCallAnalyticsJob, ListCallAnalyticsCategories, ListCallAnalyticsJobs, StartCallAnalyticsJob, UpdateCallAnalyticsCategory Real-time: StartCallAnalyticsStreamTranscription, StartCallAnalyticsStreamTranscriptionWebSocket Common use cases Post-call transcriptions: • Monitor issue frequency over time: Use call categorization to identify recurring keywords within your transcripts. Common use cases 336 Amazon Transcribe Developer Guide • Gain insight into your customer service experience: Use call characteristics (non-talk time, talk time, interruptions, voice loudness, talk speed) and sentiment analysis to determine if customer issues are being appropriately resolved during the call. • Ensure regulatory compliance or adherence to company policy: Set keywords and phrases for company-speciﬁc greetings or disclaimers to verify that your agents are meeting regulatory requirements. • Improve handling of customers' personal data: Use PII redaction in your transcription output or audio ﬁle to help protect customer privacy. • Improve staﬀ training: Use criteria (sentiment, non-talk time, interruptions, talk speed) to ﬂag transcripts that can be used as examples of positive or negative customer interactions. • Measure staﬀ eﬃcacy in creating a positive customer experience: Use sentiment analysis to measure if your agents are able to turn a negative customer sentiment into a positive one as calls progress. • Improve data organization: Label and sort calls based on custom categories (including keywords and phrases, sentiment, talk time, and interruptions). • Summarize the important aspects of a call using Generative AI: Use generative call summarization to get a concise summary of the transcript, which includes key components such as issues, action items and outcomes discussed in the call. Real-time transcriptions: • Mitigate escalations in real time: Set up real-time alerts for key phrases—such as a customer saying "speak to a manager"—to ﬂag calls as they begin to escalate. You can create real-time alerts using real-time category matches. • Improve handling of customer data: Use PII identiﬁcation or PII redaction in your transcription output to help protect customer privacy. • Identify custom keywords and phrases: Use custom categories to ﬂag speciﬁc keywords in a call. • Automatically identify issues: Use automatic issue detection to get a succinct summary of all issues identiﬁed in a call. • Measure staﬀ eﬃcacy in creating a positive customer experience: Use sentiment analysis to measure if your agents are able to turn a negative customer sentiment into a positive one as calls progress. Common use cases 337 Amazon Transcribe Developer Guide • Set up agent-assist: Use the insights of your choice to provide your agents with proactive assistance in resolving customer calls. See Live Call Analytics and agent assist for your contact center with Amazon language AI services for more information. To compare the features available with Call Analytics to those for Amazon Transcribe and Amazon Transcribe Medical, refer to the feature table. To get started, see Starting a post-call analytics transcription and Starting a real-time Call Analytics transcription. Call Analytics output is similar to that of a standard transcription job, but contains additional analytics data. To view sample output, see Post-call analytics output and Real- time Call Analytics output. Considerations and additional information Before using Call Analytics, note that: • Call Analytics only supports two-channel audio, where an agent is present on one channel and a customer is present on a second channel. • Job queueing is always enabled for post-call analytics jobs,
transcribe-dg-069	transcribe-dg.pdf	69	and Amazon Transcribe Medical, refer to the feature table. To get started, see Starting a post-call analytics transcription and Starting a real-time Call Analytics transcription. Call Analytics output is similar to that of a standard transcription job, but contains additional analytics data. To view sample output, see Post-call analytics output and Real- time Call Analytics output. Considerations and additional information Before using Call Analytics, note that: • Call Analytics only supports two-channel audio, where an agent is present on one channel and a customer is present on a second channel. • Job queueing is always enabled for post-call analytics jobs, so you're limited to 100 concurrent Call Analytics jobs. If you want to request a quota increase, see AWS service quotas. • Input ﬁles for post-call analytics jobs cannot be greater than 500 MB and must be less than 4 hours. Note that the ﬁle size limit may be smaller for certain compressed, non-WAV audio ﬁle formats. • If using categories, you must create all desired categories before starting a Call Analytics transcription. Any new categories cannot be applied to existing transcriptions. To learn how to create a new category, see Creating categories for post-call transcriptions and Creating categories for real-time transcriptions. • Some Call Analytics quotas diﬀer from Amazon Transcribe and Amazon Transcribe Medical; refer to the AWS General Reference for details. Dive deeper with the AWS Machine Learning Blog To learn more about Call Analytics options, see: • Post Call Analytics for your contact center with Amazon language AI services Considerations and additional information 338 Amazon Transcribe Developer Guide • Live Call Analytics and agent assist for your contact center with Amazon language AI services To view sample Call Analytics output and features, see our GitHub demo. We also oﬀer a JSON to Word document application to convert your transcript into an easy-to-read format. Region availability and quotas Call Analytics is supported in the following AWS Regions: Region Transcription type ap-northeast-1 (Tokyo) post-call, real-time ap-northeast-2 (Seoul) post-call, real-time ap-south-1 (Mumbai) ap-southeast-1 (Singapore) post-call post-call ap-southeast-2 (Sydney) post-call, real-time ca-central-1 (Canada, Central) post-call, real-time eu-central-1 (Frankfurt) post-call, real-time eu-west-2 (London) post-call, real-time us-east-1 (N. Virginia) post-call, real-time us-west-2 (Oregon) post-call, real-time Note that Region support diﬀers for Amazon Transcribe, Amazon Transcribe Medical, and Call Analytics. To get the endpoints for each supported Region, see Service endpoints in the AWS General Reference. Region availability and quotas 339 Amazon Transcribe Developer Guide For a list of quotas that pertain to your transcriptions, refer to the Service quotas in the AWS General Reference. Some quotas can be changed upon request. If the Adjustable column contains 'Yes', you can request an increase. To do so, select the provided link. Post-call analytics Call Analytics provides post-call analyses, which are useful for monitoring customer service trends. Post-call transcriptions oﬀer the following insights: • Call characteristics, including talk time, non-talk time, speaker loudness, interruptions, talk speed, issues, outcomes, and action items • Generative call summarization, which creates a concise summary of the entire call • Custom categorization with rules that you can use to hone in on speciﬁc keywords and criteria • PII redaction of your text transcript and your audio ﬁle • Speaker sentiment for each caller at various points in a call Post-call insights This section details the insights available for post-call analytics transcriptions. Call characteristics The call characteristics feature measures the quality of agent-customer interactions using these criteria: • Interruption: Measures if and when one participant cuts oﬀ the other participant mid-sentence. Frequent interruptions may be associated with rudeness or anger, and could correlate to negative sentiment for one or both participants. • Loudness: Measures the volume at which each participant is speaking. Use this metric to see if the caller or the agent is speaking loudly or yelling, which is often indicative of being upset. This metric is represented as a normalized value (speech level per second of speech in a given segment) on a scale from 0 to 100, where a higher value indicates a louder voice. • Non-talk time: Measures periods of time that do not contain speech. Use this metric to see if there are long periods of silence, such as an agent keeping a customer on hold for an excessive amount of time. Post-call analytics 340 Amazon Transcribe Developer Guide • Talk speed: Measures the speed at which both participants are speaking. Comprehension can be aﬀected if one participant speaks too quickly. This metric is measured in words per minute. • Talk time: Measures the amount of time (in milliseconds) each participant spoke during the call. Use this metric to help identify if one participant is dominating the call or if the dialogue is balanced. • Issues, Outcomes, and Action Items: Identiﬁes issues, outcomes and action items from the call transcript. Here's an output example. Generative call summarization
transcribe-dg-070	transcribe-dg.pdf	70	an excessive amount of time. Post-call analytics 340 Amazon Transcribe Developer Guide • Talk speed: Measures the speed at which both participants are speaking. Comprehension can be aﬀected if one participant speaks too quickly. This metric is measured in words per minute. • Talk time: Measures the amount of time (in milliseconds) each participant spoke during the call. Use this metric to help identify if one participant is dominating the call or if the dialogue is balanced. • Issues, Outcomes, and Action Items: Identiﬁes issues, outcomes and action items from the call transcript. Here's an output example. Generative call summarization Generative call summarization creates a concise summary of the entire call, capturing key components such as reason for the call, steps taken to resolve issue, and next steps. Using generative call summarization, you can: • Reduce the need for manual note-taking during and after calls. • Improve agent eﬃciency as they can spend more time talking to callers waiting in queue rather than engaging in after-call work. • Speed up supervisor reviews as call summaries are much quicker to review than entire transcripts. To use generative call summarization with a post-call analytics job, see Enabling generative call summarization. For example output, see Generative call summarization output example. Generative call summarization is priced separately (please refer to pricing page). Note Generative call summarization is currently available in us-east-1 and us-west-2. This capability is supported with these English language dialects: Australian (en-AU), British (en-GB), Indian (en-IN), Irish (en-IE), Scottish (en-AB), US (en-US), and Welsh (en-WL). Post-call insights 341 Amazon Transcribe Custom categorization Developer Guide Use call categorization to ﬂag keywords, phrases, sentiment, or actions within a call. Our categorization options can help you triage escalations, such as negative-sentiment calls with many interruptions, or organize calls into speciﬁc categories, such as company departments. The criteria you can add to a category include: • Non-talk time: Periods of time when neither the customer nor the agent is talking. • Interruptions: When the customer or the agent is interrupting the other person. • Customer or agent sentiment: How the customer or the agent is feeling during a speciﬁed time period. If at least 50 percent of the conversation turns (the back-and-forth between two speakers) in a speciﬁed time period match the speciﬁed sentiment, Amazon Transcribe considers the sentiment a match. • Keywords or phrases: Matches part of the transcription based on an exact phrase. For example, if you set a ﬁlter for the phrase "I want to speak to the manager", Amazon Transcribe ﬁlters for that exact phrase. You can also ﬂag the inverse of the previous criteria (talk time, lack of interruptions, a sentiment not being present, and the lack of a speciﬁc phrase). Here's an output example. For more information on categories or to learn how to create a new category, see Creating categories for post-call transcriptions. Sensitive data redaction Sensitive data redaction replaces personally identiﬁable information (PII) in the text transcript and the audio ﬁle. A redacted transcript replaces the original text with [PII]; a redacted audio ﬁle replaces spoken personal information with silence. This parameter is useful for protecting customer information. Note Post-call PII redaction is supported with US English (en-US) and US Spanish (es-US). Post-call insights 342 Amazon Transcribe Developer Guide To view the list of PII that is redacted using this feature, or to learn more about redaction with Amazon Transcribe, see Redacting or identifying personally identiﬁable information. Here is an output example. Sentiment analysis Sentiment analysis estimates how the customer and agent are feeling throughout the call. This metric is represented as both a quantitative value (with a range from 5 to -5) and a qualitative value (positive, neutral, mixed, or negative). Quantitative values are provided per quarter and per call; qualitative values are provided per turn. This metric can help identify if your agent is able to delight an upset customer by the time the call ends. Sentiment analysis works out-of-the-box and thus doesn't support customization, such as model training or custom categories. Here's an output example. Creating categories for post-call transcriptions Post-call analytics supports the creation of custom categories, enabling you to tailor your transcript analyses to best suit your speciﬁc business needs. You can create as many categories as you like to cover a range of diﬀerent scenarios. For each category you create, you must create between 1 and 20 rules. Each rule is based on one of four criteria: interruptions, keywords, non-talk time, or sentiment. For more details on using these criteria with the CreateCallAnalyticsCategory operation, refer to the Rule criteria for post- call analytics categories section. If the content in your media matches all the rules you've speciﬁed in a given category, Amazon Transcribe labels your output with that category. See call categorization output for an example of a category match in JSON
transcribe-dg-071	transcribe-dg.pdf	71	as many categories as you like to cover a range of diﬀerent scenarios. For each category you create, you must create between 1 and 20 rules. Each rule is based on one of four criteria: interruptions, keywords, non-talk time, or sentiment. For more details on using these criteria with the CreateCallAnalyticsCategory operation, refer to the Rule criteria for post- call analytics categories section. If the content in your media matches all the rules you've speciﬁed in a given category, Amazon Transcribe labels your output with that category. See call categorization output for an example of a category match in JSON output. Here are a few examples of what you can do with custom categories: • Isolate calls with speciﬁc characteristics, such as calls that end with a negative customer sentiment • Identify trends in customer issues by ﬂagging and tracking speciﬁc sets of keywords Creating categories 343 Amazon Transcribe Developer Guide • Monitor compliance, such as an agent speaking (or omitting) a speciﬁc phrase during the ﬁrst few seconds of a call • Gain insight into customer experience by ﬂagging calls with many agent interruptions and negative customer sentiment • Compare multiple categories to measure correlations, such as analyzing whether an agent using a welcome phrase correlates with positive customer sentiment Post-call versus real-time categories When creating a new category, you can specify whether you want it created as a post-call analytics category (POST_CALL) or as a real-time Call Analytics category (REAL_TIME). If you don't specify an option, your category is created as a post-call category by default. Post-call analytics category matches are available in your output upon completion of your post-call analytics transcription. To create a new category for post-call analytics, you can use the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console 1. In the navigation pane, under Amazon Transcribe, choose Amazon Transcribe Call Analytics. 2. Choose Call analytics categories, which takes you to the Call analytics categories page. Select Create category. 3. You're now on the Create category page. Enter a name for your category, then choose 'Batch call analytics' in the Category type dropdown menu. Creating categories 344 Amazon Transcribe Developer Guide 4. You can choose a template to create your category or you can make one from scratch. If using a template: select Use a template (recommended), choose the template you want, then select Create category. 5. If creating a custom category: select Create from scratch. Creating categories 345 Amazon Transcribe Developer Guide 6. Add rules to your category using the dropdown menu. You can add up to 20 rules per category. Creating categories 346 Amazon Transcribe Developer Guide 7. Here's an example of a category with two rules: an agent who interrupts a customer for more than 15 seconds during the call and a negative sentiment felt by the customer or the agent in the last two minutes of the call. Creating categories 347 Amazon Transcribe Developer Guide 8. When you're ﬁnished adding rules to your category, choose Create category. AWS CLI This example uses the create-call-analytics-category command. For more information, see CreateCallAnalyticsCategory, CategoryProperties, and Rule. The following example creates a category with the rules: Creating categories 348 Amazon Transcribe Developer Guide • The customer was interrupted in the ﬁrst 60,000 milliseconds. The duration of these interruptions lasted at least 10,000 milliseconds. • There was a period of silence that lasted at least 20,000 milliseconds between 10% into the call and 80% into the call. • The agent had a negative sentiment at some point in the call. • The words "welcome" or "hello" were not used in the ﬁrst 10,000 milliseconds of the call. This example uses the create-call-analytics-category command, and a request body that adds several rules to your category. aws transcribe create-call-analytics-category \ --cli-input-json file://filepath/my-first-analytics-category.json The ﬁle my-ﬁrst-analytics-category.json contains the following request body. { "CategoryName": "my-new-category", "InputType": "POST_CALL", "Rules": [ { "InterruptionFilter": { "AbsoluteTimeRange": { "First": 60000 }, "Negate": false, "ParticipantRole": "CUSTOMER", "Threshold": 10000 } }, { "NonTalkTimeFilter": { "Negate": false, "RelativeTimeRange": { "EndPercentage": 80, "StartPercentage": 10 }, "Threshold": 20000 } }, { Creating categories 349 Amazon Transcribe Developer Guide "SentimentFilter": { "ParticipantRole": "AGENT", "Sentiments": [ "NEGATIVE" ] } }, { "TranscriptFilter": { "Negate": true, "AbsoluteTimeRange": { "First": 10000 }, "Targets": [ "welcome", "hello" ], "TranscriptFilterType": "EXACT" } } ] } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a category using the CategoryName and Rules arguments for the create_call_analytics_category method. For more information, see CreateCallAnalyticsCategory, CategoryProperties, and Rule. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. The following example creates a category with the rules: • The customer was interrupted in the ﬁrst 60,000 milliseconds. The duration
transcribe-dg-072	transcribe-dg.pdf	72	"Negate": true, "AbsoluteTimeRange": { "First": 10000 }, "Targets": [ "welcome", "hello" ], "TranscriptFilterType": "EXACT" } } ] } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a category using the CategoryName and Rules arguments for the create_call_analytics_category method. For more information, see CreateCallAnalyticsCategory, CategoryProperties, and Rule. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. The following example creates a category with the rules: • The customer was interrupted in the ﬁrst 60,000 milliseconds. The duration of these interruptions lasted at least 10,000 milliseconds. • There was a period of silence that lasted at least 20,000 milliseconds between 10% into the call and 80% into the call. • The agent had a negative sentiment at some point in the call. • The words "welcome" or "hello" were not used in the ﬁrst 10,000 milliseconds of the call. Creating categories 350 Amazon Transcribe Developer Guide from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') category_name = "my-new-category" transcribe.create_call_analytics_category( CategoryName = category_name, InputType = POST_CALL, Rules = [ { 'InterruptionFilter': { 'AbsoluteTimeRange': { 'First': 60000 }, 'Negate': False, 'ParticipantRole': 'CUSTOMER', 'Threshold': 10000 } }, { 'NonTalkTimeFilter': { 'Negate': False, 'RelativeTimeRange': { 'EndPercentage': 80, 'StartPercentage': 10 }, 'Threshold': 20000 } }, { 'SentimentFilter': { 'ParticipantRole': 'AGENT', 'Sentiments': [ 'NEGATIVE' ] } }, { 'TranscriptFilter': { 'Negate': True, 'AbsoluteTimeRange': { 'First': 10000 }, Creating categories 351 Amazon Transcribe Developer Guide 'Targets': [ 'welcome', 'hello' ], 'TranscriptFilterType': 'EXACT' } } ] ) result = transcribe.get_call_analytics_category(CategoryName = category_name) print(result) Rule criteria for post-call analytics categories This section outlines the types of custom POST_CALL rules that you can create using the CreateCallAnalyticsCategory API operation. Interruption match Rules using interruptions (InterruptionFilter data type) are designed to match: • Instances where an agent interrupts a customer • Instances where a customer interrupts an agent • Any participant interrupting the other • A lack of interruptions Here's an example of the parameters available with InterruptionFilter: "InterruptionFilter": { "AbsoluteTimeRange": { Specify the time frame, in milliseconds, when the match should occur }, "RelativeTimeRange": { Specify the time frame, in percentage, when the match should occur }, "Negate": Specify if you want to match the presence or absence of interruptions, "ParticipantRole": Specify if you want to match speech from the agent, the customer, or both, Creating categories 352 Amazon Transcribe Developer Guide "Threshold": Specify a threshold for the amount of time, in seconds, interruptions occurred during the call }, Refer to CreateCallAnalyticsCategory and InterruptionFilter for more information on these parameters and the valid values associated with each. Keyword match Rules using keywords (TranscriptFilter data type) are designed to match: • Custom words or phrases spoken by the agent, the customer, or both • Custom words or phrases not spoken by the agent, the customer, or both • Custom words or phrases that occur in a speciﬁc time frame Here's an example of the parameters available with TranscriptFilter: "TranscriptFilter": { "AbsoluteTimeRange": { Specify the time frame, in milliseconds, when the match should occur }, "RelativeTimeRange": { Specify the time frame, in percentage, when the match should occur }, "Negate": Specify if you want to match the presence or absence of your custom keywords, "ParticipantRole": Specify if you want to match speech from the agent, the customer, or both, "Targets": [ The custom words and phrases you want to match ], "TranscriptFilterType": Use this parameter to specify an exact match for the specified targets } Refer to CreateCallAnalyticsCategory and TranscriptFilter for more information on these parameters and the valid values associated with each. Non-talk time match Rules using non-talk time (NonTalkTimeFilter data type) are designed to match: • The presence of silence at speciﬁed periods throughout the call Creating categories 353 Amazon Transcribe Developer Guide • The presence of speech at speciﬁed periods throughout the call Here's an example of the parameters available with NonTalkTimeFilter: "NonTalkTimeFilter": { "AbsoluteTimeRange": { Specify the time frame, in milliseconds, when the match should occur }, "RelativeTimeRange": { Specify the time frame, in percentage, when the match should occur }, "Negate": Specify if you want to match the presence or absence of speech, "Threshold": Specify a threshold for the amount of time, in seconds, silence (or speech) occurred during the call }, Refer to CreateCallAnalyticsCategory and NonTalkTimeFilter for more information on these parameters and the valid values associated with each. Sentiment match Rules using sentiment (SentimentFilter data type) are designed to match: • The presence or absence of a positive sentiment expressed by the customer, agent, or both at speciﬁed points in the call • The presence or absence of a negative sentiment expressed by the customer, agent, or both at speciﬁed points in the call • The presence or absence of
transcribe-dg-073	transcribe-dg.pdf	73	speech, "Threshold": Specify a threshold for the amount of time, in seconds, silence (or speech) occurred during the call }, Refer to CreateCallAnalyticsCategory and NonTalkTimeFilter for more information on these parameters and the valid values associated with each. Sentiment match Rules using sentiment (SentimentFilter data type) are designed to match: • The presence or absence of a positive sentiment expressed by the customer, agent, or both at speciﬁed points in the call • The presence or absence of a negative sentiment expressed by the customer, agent, or both at speciﬁed points in the call • The presence or absence of a neutral sentiment expressed by the customer, agent, or both at speciﬁed points in the call • The presence or absence of a mixed sentiment expressed by the customer, agent, or both at speciﬁed points in the call Here's an example of the parameters available with SentimentFilter: "SentimentFilter": { "AbsoluteTimeRange": { Specify the time frame, in milliseconds, when the match should occur }, "RelativeTimeRange": { Creating categories 354 Amazon Transcribe Developer Guide Specify the time frame, in percentage, when the match should occur }, "Negate": Specify if you want to match the presence or absence of your chosen sentiment, "ParticipantRole": Specify if you want to match speech from the agent, the customer, or both, "Sentiments": [ The sentiments you want to match ] }, Refer to CreateCallAnalyticsCategory and SentimentFilter for more information on these parameters and the valid values associated with each. Starting a post-call analytics transcription Before starting a post-call analytics transcription, you must create all the categories you want Amazon Transcribe to match in your audio. Note Call Analytics transcripts can't be retroactively matched to new categories. Only the categories you create before starting a Call Analytics transcription can be applied to that transcription output. If you've created one or more categories, and your audio matches all the rules within at least one of your categories, Amazon Transcribe ﬂags your output with the matching category. If you choose not to use categories, or if your audio doesn't match the rules speciﬁed in your categories, your transcript isn't ﬂagged. To start a post-call analytics transcription, you can use the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console Use the following procedure to start a post-call analytics job. The calls that match all characteristics deﬁned by a category are labeled with that category. 1. In the navigation pane, under Amazon Transcribe Call Analytics, choose Call analytics jobs. 2. Choose Create job. Starting a transcription 355 Amazon Transcribe Developer Guide Starting a transcription 356 Amazon Transcribe Developer Guide 3. On the Specify job details page, provide information about your Call Analytics job, including the location of your input data. Starting a transcription 357 Amazon Transcribe Developer Guide Specify the desired Amazon S3 location of your output data and which IAM role to use. Starting a transcription 358 Amazon Transcribe 4. Choose Next. Developer Guide 5. For Conﬁgure job, turn on any optional features you want to include with your Call Analytics job. If you previously created categories, they appear in the Categories panel and are automatically applied to your Call Analytics job. Starting a transcription 359 Amazon Transcribe Developer Guide Starting a transcription 360 Amazon Transcribe 6. Choose Create job. AWS CLI Developer Guide This example uses the start-call-analytics-job command and channel-definitions parameter. For more information, see StartCallAnalyticsJob and ChannelDefinition. aws transcribe start-call-analytics-job \ --region us-west-2 \ --call-analytics-job-name my-first-call-analytics-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-location s3://amzn-s3-demo-bucket/my-output-files/ \ --data-access-role-arn arn:aws:iam::111122223333:role/ExampleRole \ --channel-definitions ChannelId=0,ParticipantRole=AGENT ChannelId=1,ParticipantRole=CUSTOMER Here's another example using the start-call-analytics-job command, and a request body that enables Call Analytics for that job. aws transcribe start-call-analytics-job \ --region us-west-2 \ --cli-input-json file://filepath/my-call-analytics-job.json The ﬁle my-call-analytics-job.json contains the following request body. { "CallAnalyticsJobName": "my-first-call-analytics-job", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputLocation": "s3://amzn-s3-demo-bucket/my-output-files/", "ChannelDefinitions": [ { "ChannelId": 0, "ParticipantRole": "AGENT" }, { "ChannelId": 1, "ParticipantRole": "CUSTOMER" } Starting a transcription 361 Amazon Transcribe ] } AWS SDK for Python (Boto3) Developer Guide This example uses the AWS SDK for Python (Boto3) to start a Call Analytics job using the start_call_analytics_job method. For more information, see StartCallAnalyticsJob and ChannelDefinition. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-call-analytics-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" output_location = "s3://amzn-s3-demo-bucket/my-output-files/" data_access_role = "arn:aws:iam::111122223333:role/ExampleRole" transcribe.start_call_analytics_job( CallAnalyticsJobName = job_name, Media = { 'MediaFileUri': job_uri }, DataAccessRoleArn = data_access_role, OutputLocation = output_location, ChannelDefinitions = [ { 'ChannelId': 0, 'ParticipantRole': 'AGENT' }, { 'ChannelId': 1, 'ParticipantRole': 'CUSTOMER' } ] ) while True: status = transcribe.get_call_analytics_job(CallAnalyticsJobName = job_name) if status['CallAnalyticsJob']['CallAnalyticsJobStatus'] in ['COMPLETED', 'FAILED']: break Starting a transcription 362 Amazon Transcribe Developer Guide print("Not ready yet...") time.sleep(5) print(status) Post-call
transcribe-dg-074	transcribe-dg.pdf	74	scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-call-analytics-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" output_location = "s3://amzn-s3-demo-bucket/my-output-files/" data_access_role = "arn:aws:iam::111122223333:role/ExampleRole" transcribe.start_call_analytics_job( CallAnalyticsJobName = job_name, Media = { 'MediaFileUri': job_uri }, DataAccessRoleArn = data_access_role, OutputLocation = output_location, ChannelDefinitions = [ { 'ChannelId': 0, 'ParticipantRole': 'AGENT' }, { 'ChannelId': 1, 'ParticipantRole': 'CUSTOMER' } ] ) while True: status = transcribe.get_call_analytics_job(CallAnalyticsJobName = job_name) if status['CallAnalyticsJob']['CallAnalyticsJobStatus'] in ['COMPLETED', 'FAILED']: break Starting a transcription 362 Amazon Transcribe Developer Guide print("Not ready yet...") time.sleep(5) print(status) Post-call analytics output Post-call analytics transcripts are displayed in a turn-by-turn format by segment. They include call categorization, call characteristics (loudness scores, interruptions, non-talk time, talk speed), call summarization (issues, outcomes, and action items), redaction, and sentiment. Additionally, a summary of conversation characteristics is provided at the end of the transcript. To increase accuracy and further customize your transcripts to your use case, such as including industry-speciﬁc terms, add custom vocabularies or custom language models to your Call Analytics request. To mask, remove, or tag words that you don't want in your transcription results, such as profanity, add vocabulary ﬁltering. If you are unsure of the language code to be passed to the media ﬁle, you can enable batch language identiﬁcation to automatically identify the language in your media ﬁle. The following sections show examples of JSON output at an insight level. For compiled output, see Compiled post-call analytics output. Call categorization Here's what a category match looks like in your transcription output. This example shows that the audio from the 40040 millisecond timestamp to the 42460 millisecond timestamp is a match to the 'positive-resolution' category. In this case, the custom 'positive-resolution' category required a positive sentiment in last few seconds of speech. "Categories": { "MatchedDetails": { "positive-resolution": { "PointsOfInterest": [ { "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460 } ] } }, "MatchedCategories": [ " positive-resolution" Post-call analytics output 363 Amazon Transcribe ] }, Call characteristics Developer Guide Here's what call characteristics look like in your transcription output. Note that loudness scores are provided for each conversation turn, while all other characteristics are provided at the end of the transcript. "LoudnessScores": [ 87.54, 88.74, 90.16, 86.36, 85.56, 85.52, 81.79, 87.74, 89.82 ], ... "ConversationCharacteristics": { "NonTalkTime": { "Instances": [], "TotalTimeMillis": 0 }, "Interruptions": { "TotalCount": 2, "TotalTimeMillis": 10700, "InterruptionsByInterrupter": { "AGENT": [ { "BeginOffsetMillis": 26040, "DurationMillis": 5510, "EndOffsetMillis": 31550 } ], "CUSTOMER": [ { "BeginOffsetMillis": 770, "DurationMillis": 5190, Post-call analytics output 364 Amazon Transcribe Developer Guide "EndOffsetMillis": 5960 } ] } }, "TotalConversationDurationMillis": 42460, ... "TalkSpeed": { "DetailsByParticipant": { "AGENT": { "AverageWordsPerMinute": 150 }, "CUSTOMER": { "AverageWordsPerMinute": 167 } } }, "TalkTime": { "DetailsByParticipant": { "AGENT": { "TotalTimeMillis": 32750 }, "CUSTOMER": { "TotalTimeMillis": 18010 } }, "TotalTimeMillis": 50760 } }, Issues, Action Items and Next Steps • In the following example, issues are identiﬁed as starting at character 7 and ending at character 51, which refers to this section of the text: "I would like to cancel my recipe subscription". "Content": "Well, I would like to cancel my recipe subscription.", "IssuesDetected": [ { "CharacterOffsets": { "Begin": 7, Post-call analytics output 365 Amazon Transcribe "End": 51 } } ], Developer Guide • In the following example, outcomes are identiﬁed as starting at character 12 and ending at character 78, which refers to this section of the text: "I made all changes to your account and now this discount is applied". "Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [ { "CharacterOffsets": { "Begin": 12, "End": 78 } } ], • In the following example, action items are identiﬁed as starting at character 0 and ending at character 103, which refers to this section of the text: "I will send an email with all the details to you today, and I will call you back next week to follow up". "Content": "I will send an email with all the details to you today, and I will call you back next week to follow up. Have a wonderful evening.", "ActionItemsDetected": [ { "CharacterOffsets": { "Begin": 0, "End": 103 } } ], Generative call summarization Here's what generative call summarization looks like in your transcription output: Post-call analytics output 366 Amazon Transcribe Developer Guide "ContactSummary": { "AutoGenerated": { "OverallSummary": { "Content": "A customer wanted to check to see if we had a bag allowance. We told them that we didn't have it, but we could add the bag from Canada to Calgary and then do the one coming back as well." } } } The analytics job will complete without summary generation in the following cases: • Insuﬃcient conversation content: The conversation must include at least one turn from both the agent and the customer. When there
transcribe-dg-075	transcribe-dg.pdf	75	generative call summarization looks like in your transcription output: Post-call analytics output 366 Amazon Transcribe Developer Guide "ContactSummary": { "AutoGenerated": { "OverallSummary": { "Content": "A customer wanted to check to see if we had a bag allowance. We told them that we didn't have it, but we could add the bag from Canada to Calgary and then do the one coming back as well." } } } The analytics job will complete without summary generation in the following cases: • Insuﬃcient conversation content: The conversation must include at least one turn from both the agent and the customer. When there is insuﬃcient conversation content, the service will return the error code INSUFFICIENT_CONVERSATION_CONTENT. • Safety guardrails: The conversation must meet safety guardrails in place to ensure appropriate summary is generated. When these guardrails are not met, the service will return the error code FAILED_SAFETY_GUIDELINES. The error code can be found in Skipped section within AnalyticsJobDetails in the output. You may also ﬁnd the error reason in CallAnalyticsJobDetails in the GetCallAnalyticsJob API Response. Sample Error Output { "JobStatus": "COMPLETED", "AnalyticsJobDetails": { "Skipped": [ { "Feature": "GENERATIVE_SUMMARIZATION", "ReasonCode": "INSUFFICIENT_CONVERSATION_CONTENT", "Message": "The conversation needs to have at least one turn from both the participants to generate summary" } ] }, "LanguageCode": "en-US", "AccountId": "***************", "JobName": "Test2-copy", ... Post-call analytics output 367 Amazon Transcribe } Sentiment analysis Developer Guide Here is what sentiment analysis looks like in your transcription output. • Qualitative turn-by-turn sentiment values: "Content": "That's very sad to hear. Can I offer you a 50% discount to have you stay with us?", ... "BeginOffsetMillis": 12180, "EndOffsetMillis": 16960, "Sentiment": "NEGATIVE", "ParticipantRole": "AGENT" ... "Content": "That is a very generous offer. And I accept.", ... "BeginOffsetMillis": 17140, "EndOffsetMillis": 19860, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" • Quantitative sentiment values for the entire call: "Sentiment": { "OverallSentiment": { "AGENT": 2.5, "CUSTOMER": 2.1 }, • Quantitative sentiment values per participant and per call quarter: "SentimentByPeriod": { "QUARTER": { "AGENT": [ { Post-call analytics output 368 Amazon Transcribe Developer Guide "Score": 0.0, "BeginOffsetMillis": 0, "EndOffsetMillis": 9862 }, { "Score": -5.0, "BeginOffsetMillis": 9862, "EndOffsetMillis": 19725 }, { "Score": 5.0, "BeginOffsetMillis": 19725, "EndOffsetMillis": 29587 }, { "Score": 5.0, "BeginOffsetMillis": 29587, "EndOffsetMillis": 39450 } ], "CUSTOMER": [ { "Score": -2.5, "BeginOffsetMillis": 0, "EndOffsetMillis": 10615 }, { "Score": 5.0, "BeginOffsetMillis": 10615, "EndOffsetMillis": 21230 }, { "Score": 2.5, "BeginOffsetMillis": 21230, "EndOffsetMillis": 31845 }, { "Score": 5.0, "BeginOffsetMillis": 31845, "EndOffsetMillis": 42460 } ] } Post-call analytics output 369 Developer Guide Amazon Transcribe } PII redaction Here is what PII redaction looks like in your transcription output. "Content": "[PII], my name is [PII], how can I help?", "Redaction": [{ "Confidence": "0.9998", "Type": "NAME", "Category": "PII" }] For more information, refer to Redacting PII in your batch job. Language identiﬁcation Here is what Language Identiﬁcation looks like in your transcription output if the feature is enabled. "LanguageIdentification": [{ "Code": "en-US", "Score": "0.8299" }, { "Code": "en-NZ", "Score": "0.0728" }, { "Code": "zh-TW", "Score": "0.0695" }, { "Code": "th-TH", "Score": "0.0156" }, { "Code": "en-ZA", "Score": "0.0121" }] In the above output example, Language Identiﬁcation will populate the language codes with conﬁdence scores. The result with the highest score will be selected as the language code for transcription. For mode details refer to Identifying the dominant languages in your media. Post-call analytics output 370 Amazon Transcribe Developer Guide Compiled post-call analytics output For brevity, some content is replaced with ellipses in the following transcription output. This sample includes optional feature - Generative call summarization. { "JobStatus": "COMPLETED", "LanguageCode": "en-US", "Transcript": [ { "LoudnessScores": [ 78.63, 78.37, 77.98, 74.18 ], "Content": "[PII], my name is [PII], how can I help?", ... "Content": "Well, I would like to cancel my recipe subscription.", "IssuesDetected": [ { "CharacterOffsets": { "Begin": 7, "End": 51 } } ], ... "Content": "That's very sad to hear. Can I offer you a 50% discount to have you stay with us?", "Items": [ ... ], "Id": "649afe93-1e59-4ae9-a3ba-a0a613868f5d", "BeginOffsetMillis": 12180, "EndOffsetMillis": 16960, "Sentiment": "NEGATIVE", "ParticipantRole": "AGENT" }, Post-call analytics output 371 Amazon Transcribe { "LoudnessScores": [ 80.22, 79.48, 82.81 ], Developer Guide "Content": "That is a very generous offer. And I accept.", "Items": [ ... ], "Id": "f9266cba-34df-4ca8-9cea-4f62a52a7981", "BeginOffsetMillis": 17140, "EndOffsetMillis": 19860, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" }, { ... "Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [ { "CharacterOffsets": { "Begin": 12, "End": 78 } } ], ... "Content": "I will send an email with all the details to you today, and I will call you back next week to follow up. Have a wonderful evening.", "Items": [ ... ], "Id": "78cd0923-cafd-44a5-a66e-09515796572f", "BeginOffsetMillis": 31800, "EndOffsetMillis": 39450, "Sentiment": "POSITIVE", "ParticipantRole": "AGENT" }, Post-call analytics output 372 Developer Guide Amazon Transcribe { "LoudnessScores": [ 78.54, 68.76, 67.76 ], "Content": "Thank you very much, sir. Goodbye.", "Items": [ ... ], "Id":
transcribe-dg-076	transcribe-dg.pdf	76	"Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [ { "CharacterOffsets": { "Begin": 12, "End": 78 } } ], ... "Content": "I will send an email with all the details to you today, and I will call you back next week to follow up. Have a wonderful evening.", "Items": [ ... ], "Id": "78cd0923-cafd-44a5-a66e-09515796572f", "BeginOffsetMillis": 31800, "EndOffsetMillis": 39450, "Sentiment": "POSITIVE", "ParticipantRole": "AGENT" }, Post-call analytics output 372 Developer Guide Amazon Transcribe { "LoudnessScores": [ 78.54, 68.76, 67.76 ], "Content": "Thank you very much, sir. Goodbye.", "Items": [ ... ], "Id": "5c5e6be0-8349-4767-8447-986f995af7c3", "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" } ], ... "Categories": { "MatchedDetails": { "positive-resolution": { "PointsOfInterest": [ { "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460 } ] } }, "MatchedCategories": [ "positive-resolution" ] }, ... "ConversationCharacteristics": { "NonTalkTime": { "Instances": [], "TotalTimeMillis": 0 }, "Interruptions": { Post-call analytics output 373 Amazon Transcribe Developer Guide "TotalCount": 2, "TotalTimeMillis": 10700, "InterruptionsByInterrupter": { "AGENT": [ { "BeginOffsetMillis": 26040, "DurationMillis": 5510, "EndOffsetMillis": 31550 } ], "CUSTOMER": [ { "BeginOffsetMillis": 770, "DurationMillis": 5190, "EndOffsetMillis": 5960 } ] } }, "TotalConversationDurationMillis": 42460, "Sentiment": { "OverallSentiment": { "AGENT": 2.5, "CUSTOMER": 2.1 }, "SentimentByPeriod": { "QUARTER": { "AGENT": [ { "Score": 0.0, "BeginOffsetMillis": 0, "EndOffsetMillis": 9862 }, { "Score": -5.0, "BeginOffsetMillis": 9862, "EndOffsetMillis": 19725 }, { "Score": 5.0, "BeginOffsetMillis": 19725, "EndOffsetMillis": 29587 }, { Post-call analytics output 374 Amazon Transcribe Developer Guide "Score": 5.0, "BeginOffsetMillis": 29587, "EndOffsetMillis": 39450 } ], "CUSTOMER": [ { "Score": -2.5, "BeginOffsetMillis": 0, "EndOffsetMillis": 10615 }, { "Score": 5.0, "BeginOffsetMillis": 10615, "EndOffsetMillis": 21230 }, { "Score": 2.5, "BeginOffsetMillis": 21230, "EndOffsetMillis": 31845 }, { "Score": 5.0, "BeginOffsetMillis": 31845, "EndOffsetMillis": 42460 } ] } } }, "TalkSpeed": { "DetailsByParticipant": { "AGENT": { "AverageWordsPerMinute": 150 }, "CUSTOMER": { "AverageWordsPerMinute": 167 } } }, "TalkTime": { "DetailsByParticipant": { "AGENT": { "TotalTimeMillis": 32750 Post-call analytics output 375 Amazon Transcribe }, "CUSTOMER": { "TotalTimeMillis": 18010 } }, "TotalTimeMillis": 50760 }, Developer Guide "ContactSummary": { // Optional feature - Generative call summarization "AutoGenerated": { "OverallSummary": { "Content": "The customer initially wanted to cancel but the agent convinced them to stay by offering a 50% discount, which the customer accepted after reconsidering cancelling given the significant savings. The agent ensured the discount was applied and said they would follow up to ensure the customer remained happy with the revised subscription." } } } }, "AnalyticsJobDetails": { "Skipped": [] }, ... } Enabling generative call summarization Note Powered by Amazon Bedrock: AWS implements automated abuse detection. Because post- contact summarization powered by generative AI is built on Amazon Bedrock, users can take full advantage of the controls implemented in Amazon Bedrock to enforce safety, security, and the responsible use of artiﬁcial intelligence (AI). To use generative call summarization with a post call analytics job, see the following for examples: AWS Management Console In the Summarization panel, enable Generative call summarization to receive summary in the output. Enabling generative call summarization 376 Amazon Transcribe Developer Guide Enabling generative call summarization 377 Amazon Transcribe AWS CLI Developer Guide This example uses the start-call-analytics-job command and Settings parameter with the Summarization sub-parameters. For more information, see StartCallAnalyticsJob. aws transcribe start-call-analytics-job \ --region us-west-2 \ --call-analytics-job-name my-first-call-analytics-job \ --media MediaFileUri=s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac \ --output-location s3://amzn-s3-demo-bucket/my-output-files/ \ --data-access-role-arn arn:aws:iam::111122223333:role/ExampleRole \ --channel-definitions ChannelId=0,ParticipantRole=AGENT ChannelId=1,ParticipantRole=CUSTOMER --settings '{"Summarization":{"GenerateAbstractiveSummary":true}}' Here's another example using the start-call-analytics-job command, and a request body that enables summarization for that job. aws transcribe start-call-analytics-job \ --region us-west-2 \ --cli-input-json file://filepath/my-call-analytics-job.json The ﬁle my-call-analytics-job.json contains the following request body. { "CallAnalyticsJobName": "my-first-call-analytics-job", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" }, "OutputLocation": "s3://amzn-s3-demo-bucket/my-output-files/", "ChannelDefinitions": [ { "ChannelId": 0, "ParticipantRole": "AGENT" }, { Enabling generative call summarization 378 Amazon Transcribe Developer Guide "ChannelId": 1, "ParticipantRole": "CUSTOMER" } ], "Settings": { "Summarization":{ "GenerateAbstractiveSummary": true } } } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to start a Call Analytics with summarization enabled using the start_call_analytics_job method. For more information, see StartCallAnalyticsJob. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. from __future__ import print_function from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-call-analytics-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" output_location = "s3://amzn-s3-demo-bucket/my-output-files/" data_access_role = "arn:aws:iam::111122223333:role/ExampleRole" transcribe.start_call_analytics_job( CallAnalyticsJobName = job_name, Media = { 'MediaFileUri': job_uri }, DataAccessRoleArn = data_access_role, OutputLocation = output_location, ChannelDefinitions = [ { 'ChannelId': 0, 'ParticipantRole': 'AGENT' }, { Enabling generative call summarization 379 Amazon Transcribe Developer Guide 'ChannelId': 1, 'ParticipantRole': 'CUSTOMER' } ], Settings = { "Summarization": { "GenerateAbstractiveSummary": true } } ) while True: status = transcribe.get_call_analytics_job(CallAnalyticsJobName = job_name) if status['CallAnalyticsJob']['CallAnalyticsJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Real-time Call Analytics Real-time Call Analytics provides real-time insights that can be used for addressing issues and mitigating escalations as they happen. The following
transcribe-dg-077	transcribe-dg.pdf	77	"s3://amzn-s3-demo-bucket/my-input-files/my-media-file.flac" output_location = "s3://amzn-s3-demo-bucket/my-output-files/" data_access_role = "arn:aws:iam::111122223333:role/ExampleRole" transcribe.start_call_analytics_job( CallAnalyticsJobName = job_name, Media = { 'MediaFileUri': job_uri }, DataAccessRoleArn = data_access_role, OutputLocation = output_location, ChannelDefinitions = [ { 'ChannelId': 0, 'ParticipantRole': 'AGENT' }, { Enabling generative call summarization 379 Amazon Transcribe Developer Guide 'ChannelId': 1, 'ParticipantRole': 'CUSTOMER' } ], Settings = { "Summarization": { "GenerateAbstractiveSummary": true } } ) while True: status = transcribe.get_call_analytics_job(CallAnalyticsJobName = job_name) if status['CallAnalyticsJob']['CallAnalyticsJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Real-time Call Analytics Real-time Call Analytics provides real-time insights that can be used for addressing issues and mitigating escalations as they happen. The following insights are available with real-time Call Analytics: • Category events that use rules to ﬂag speciﬁc keywords and phrases; category events can be used to create real-time alerts • Issue detection identiﬁes the issues spoken within each audio segment • PII (sensitive data) identiﬁcation in your text transcript • PII (sensitive data) redaction of your text transcript • Sentiment analysis for each speech segment In addition to real-time Call Analytics, Amazon Transcribe can also perform post-call analytics on your media stream. You can include post-call analytics in your real-time Call Analytics request using the PostCallAnalyticsSettings parameter. Real-time Call Analytics 380 Amazon Transcribe Real-time insights Developer Guide This section details the insights available for real-time Call Analytics transcriptions. Category events Using category events, you can match your transcription based on an exact keyword or phrase. For example, if you set a ﬁlter for the phrase "I want to speak to the manager", Amazon Transcribe ﬁlters for that exact phrase. Here's an output example. For more information on creating real-time Call Analytics categories, see Creating categories for real-time transcriptions. Tip Category events allow you to set real-time alerts; see Creating real-time alerts for category matches for more information. Issue detection Issue detection provides succinct summaries of detected issues within each audio segment. Using the issue detection feature, you can: • Reduce the need for manual note-taking during and after calls • Improve agent eﬃciency, allowing them to respond faster to customers Note Issue detection is supported with these English language dialects: Australian (en-AU), British (en-GB), and US (en-US). The issue detection feature works across all industries and business sectors, and is context-based. It works out-of-the-box and thus doesn't support customization, such as model training or custom categories. Real-time insights 381 Amazon Transcribe Developer Guide Issue detection with real-time Call Analytics is performed on each complete audio segment. Here's an output example. PII (sensitive data) identiﬁcation Sensitive data identiﬁcation labels personally identiﬁable information (PII) in the text transcript. This parameter is useful for protecting customer information. Note Real-time PII identiﬁcation is supported with these English language dialects: Australian (en-AU), British (en-GB), US (en-US) and with Spanish language dialect (es-US). PII identiﬁcation with real-time Call Analytics is performed on each complete audio segment. To view the list of PII that is identiﬁed using this feature, or to learn more about PII identiﬁcation with Amazon Transcribe, see Redacting or identifying personally identiﬁable information. Here is an output example. PII (sensitive data) redaction Sensitive data redaction replaces personally identiﬁable information (PII) in your text transcript with the type of PII (for example, [NAME]). This parameter is useful for protecting customer information. Note Real-time PII redaction is supported with these English language dialects: Australian (en- AU), British (en-GB), US (en-US) and with Spanish language dialect (es-US). PII redaction with real-time Call Analytics is performed on each complete audio segment. To view the list of PII that is redacted using this feature, or to learn more about redaction with Amazon Transcribe, see Redacting or identifying personally identiﬁable information. Here is an output example. Real-time insights 382 Amazon Transcribe Sentiment analysis Developer Guide Sentiment analysis estimates how the customer and agent are feeling throughout the call. This metric is provided for every speech segment and is represented as a qualitative value (positive, neutral, mixed, or negative). Using this parameter, you can qualitatively evaluate the overall sentiment for each call participant and the sentiment for each participant during each speech segment. This metric can help identify if your agent is able to delight an upset customer by the time the call ends. Sentiment analysis with real-time Call Analytics is performed on each complete audio segment. Sentiment analysis works out-of-the-box and thus doesn't support customization, such as model training or custom categories. Here's an output example. Creating categories for real-time transcriptions Real-time Call Analytics supports the creation of custom categories, which you can use to tailor your transcript analyses to best suit your speciﬁc business needs. You can create as many categories as you like to cover a range of diﬀerent scenarios. For each category you create, you must create between 1 and 20 rules. Real-time Call Analytics transcriptions only support rules that use TranscriptFilter (keyword matches). For more detail on
transcribe-dg-078	transcribe-dg.pdf	78	on each complete audio segment. Sentiment analysis works out-of-the-box and thus doesn't support customization, such as model training or custom categories. Here's an output example. Creating categories for real-time transcriptions Real-time Call Analytics supports the creation of custom categories, which you can use to tailor your transcript analyses to best suit your speciﬁc business needs. You can create as many categories as you like to cover a range of diﬀerent scenarios. For each category you create, you must create between 1 and 20 rules. Real-time Call Analytics transcriptions only support rules that use TranscriptFilter (keyword matches). For more detail on using rules with the CreateCallAnalyticsCategory operation, refer to the Rule criteria for real-time Call Analytics categories section. If the content in your media matches all the rules you've speciﬁed in a given category, Amazon Transcribe labels your output with that category. See category event output for an example of a category match in JSON output format. Here are a few examples of what you can do with custom categories: • Identify issues that warrant immediate attention by ﬂagging and tracking speciﬁc sets of keywords • Monitor compliance, such as an agent speaking (or omitting) a speciﬁc phrase • Flag speciﬁc words and phrases in real time; you can then set your category match to set an immediate alert. For example, if you create a real-time Call Analytics category for a customer saying "speak to a manager," you can set an event alert for this real-time category match that notiﬁes the on-duty manager. Creating categories 383 Amazon Transcribe Developer Guide Post-call versus real-time categories When creating a new category, you can specify whether you want it created as a post-call category (POST_CALL) or as a real-time category (REAL_TIME). If you don't specify an option, your category is created as a post-call category by default. Real-time category matches can be used to create real-time alerts. For more information, see Creating real-time alerts for category matches. To create a new category for real-time Call Analytics, you can use the AWS Management Console, AWS CLI, or AWS SDKs; see the following for examples: AWS Management Console 1. In the navigation pane, under Amazon Transcribe, choose Amazon Transcribe Call Analytics. 2. Choose Call analytics categories, which takes you to the Call analytics categories page. Select the Create category button. 3. You're now on the Create category page. Enter a name for your category, then choose 'Real time call analytics' in the Category type dropdown menu. Creating categories 384 Amazon Transcribe Developer Guide 4. You can choose a template to create your category or you can make one from scratch. If using a template: select Use a template (recommended), choose the template you want, then select Create category. 5. If creating a custom category: select Create from scratch. Creating categories 385 Amazon Transcribe Developer Guide 6. Add rules to your category using the dropdown menu. You can add up to 20 rules per category. With real-time Call Analytics transcriptions, you can only include rules that involve transcript content matches. Any matches are ﬂagged in real time. Creating categories 386 Amazon Transcribe Developer Guide 7. Here's an example of a category with one rule: a customer who says "speak to a manager" at any point in the call. 8. When you're ﬁnished adding rules to your category, choose Create category. AWS CLI This example uses the create-call-analytics-category command. For more information, see CreateCallAnalyticsCategory, CategoryProperties, and Rule. The following example creates a category with the rule: • The customer spoke the phrase "speak to the manager" at any point in the call. This example uses the create-call-analytics-category command, and a request body that adds a rule to your category. aws transcribe create-call-analytics-category \ Creating categories 387 Amazon Transcribe Developer Guide --cli-input-json file://filepath/my-first-analytics-category.json The ﬁle my-ﬁrst-analytics-category.json contains the following request body. { "CategoryName": "my-new-real-time-category", "InputType": "REAL_TIME", "Rules": [ { "TranscriptFilter": { "Negate": false, "Targets": [ "speak to the manager" ], "TranscriptFilterType": "EXACT" } } ] } AWS SDK for Python (Boto3) This example uses the AWS SDK for Python (Boto3) to create a category using the CategoryName and Rules arguments for the create_call_analytics_category method. For more information, see CreateCallAnalyticsCategory, CategoryProperties, and Rule. For additional examples using the AWS SDKs, including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. The following example creates a category with the rule: • The customer spoke the phrase "speak to the manager" at any point in the call. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') category_name = "my-new-real-time-category" transcribe.create_call_analytics_category( CategoryName = category_name, InputType = "REAL_TIME", Creating categories 388 Developer Guide Amazon Transcribe Rules = [ { 'TranscriptFilter': { 'Negate': False, 'Targets': [ 'speak to the manager' ], 'TranscriptFilterType': 'EXACT' } } ] ) result = transcribe.get_call_analytics_category(CategoryName = category_name) print(result) Rule
transcribe-dg-079	transcribe-dg.pdf	79	including feature-speciﬁc, scenario, and cross-service examples, refer to the Code examples for Amazon Transcribe using AWS SDKs chapter. The following example creates a category with the rule: • The customer spoke the phrase "speak to the manager" at any point in the call. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') category_name = "my-new-real-time-category" transcribe.create_call_analytics_category( CategoryName = category_name, InputType = "REAL_TIME", Creating categories 388 Developer Guide Amazon Transcribe Rules = [ { 'TranscriptFilter': { 'Negate': False, 'Targets': [ 'speak to the manager' ], 'TranscriptFilterType': 'EXACT' } } ] ) result = transcribe.get_call_analytics_category(CategoryName = category_name) print(result) Rule criteria for real-time Call Analytics categories This section outlines the types of custom REAL_TIME rules that you can create using the CreateCallAnalyticsCategory API operation. Issue detection occurs automatically, so you don't need to create any rules or categories to ﬂag issues. Note that only keyword matches are supported for real-time Call Analytics transcriptions. If you want to create categories that include interruptions, silence, or sentiment, refer to Rule criteria for post-call analytics categories. Keyword match Rules using keywords (TranscriptFilter data type) are designed to match: • Custom words or phrases spoken by the agent, the customer, or both • Custom words or phrases not spoken by the agent, the customer, or both • Custom words or phrases that occur in a speciﬁc time frame Here's an example of the parameters available with TranscriptFilter: "TranscriptFilter": { "AbsoluteTimeRange": { Specify the time frame, in milliseconds, when the match should occur Creating categories 389 Amazon Transcribe }, Developer Guide "RelativeTimeRange": { Specify the time frame, in percentage, when the match should occur }, "Negate": Specify if you want to match the presence or absence of your custom keywords, "ParticipantRole": Specify if you want to match speech from the agent, the customer, or both, "Targets": [ The custom words and phrases you want to match ], "TranscriptFilterType": Use this parameter to specify an exact match for the specified targets } Refer to CreateCallAnalyticsCategory and TranscriptFilter for more information on these parameters and the valid values associated with each. Post-call analytics with real-time transcriptions Post-call analytics is an optional feature available with real-time Call Analytics transcriptions. In addition to the standard real-time analytics insights, post-call analytics provides you with the following: • Action items: Lists any action items identiﬁed in the call • Interruptions: Measures if and when one participant cuts oﬀ the other participant midsentence • Issues: Provides the issues identiﬁed in the call • Loudness: Measures the volume at which each participant is speaking • Non-talk time: Measures periods of time that do not contain speech • Outcomes: Provides the outcome, or resolution, identiﬁed in the call • Talk speed: Measures the speed at which both participants are speaking • Talk time: Measures the amount of time (in milliseconds) each participant spoke during the call When enabled, post-call analytics from an audio stream produces a transcript similar to a post-call analytics from an audio ﬁle and stores it in the Amazon S3 bucket speciﬁed in OutputLocation. Additionally, post-call analytics records your audio stream and saves it as an audio ﬁle (WAV format) in the same Amazon S3 bucket. If you enable redaction, a redacted transcript and a redacted audio ﬁle are also stored in the speciﬁed Amazon S3 bucket. Enabling post-call analytics with your audio stream produces between two and four ﬁles, as described here: Post-call analytics with real-time transcriptions 390 Amazon Transcribe Developer Guide • If redaction is not enabled, your output ﬁles are: 1. An unredacted transcript 2. An unredacted audio ﬁle • If redaction is enabled without the unredacted option (redacted), your output ﬁles are: 1. A redacted transcript 2. A redacted audio ﬁle • If redaction is enabled with the unredacted option (redacted_and_unredacted), your output ﬁles are: 1. A redacted transcript 2. A redacted audio ﬁle 3. An unredacted transcript 4. An unredacted audio ﬁle Note that if you enable post-call analytics (PostCallAnalyticsSettings) with your request, and you're using FLAC or OPUS-OGG media, you do not get loudnessScore in your transcript and no audio recordings of your stream are created. Transcribe may also not be able to provide post- call analytics for long-running audio streams lasting longer than 90 minutes. For more information on the insights available with post-call analytics for audio streams, refer to the post-call analytics insights section. Tip If you enable post-call analytics with your real-time Call Analytics request, all of your POST_CALL and REAL-TIME categories are applied to your post-call analytics transcription. Enabling post-call analytics To enable post-call analytics, you must include the PostCallAnalyticsSettings parameter in your real-time Call Analytics request. The following parameters must be included when PostCallAnalyticsSettings is enabled: • OutputLocation: The Amazon S3 bucket where you want your post-call transcript stored. Post-call analytics with real-time transcriptions 391 Amazon Transcribe Developer Guide •
transcribe-dg-080	transcribe-dg.pdf	80	minutes. For more information on the insights available with post-call analytics for audio streams, refer to the post-call analytics insights section. Tip If you enable post-call analytics with your real-time Call Analytics request, all of your POST_CALL and REAL-TIME categories are applied to your post-call analytics transcription. Enabling post-call analytics To enable post-call analytics, you must include the PostCallAnalyticsSettings parameter in your real-time Call Analytics request. The following parameters must be included when PostCallAnalyticsSettings is enabled: • OutputLocation: The Amazon S3 bucket where you want your post-call transcript stored. Post-call analytics with real-time transcriptions 391 Amazon Transcribe Developer Guide • DataAccessRoleArn: The Amazon Resource Name (ARN) of the Amazon S3 role that has permissions to access the speciﬁed Amazon S3 bucket. Note that you must also use the Trust policy for real-time analytics. If you want a redacted version of your transcript, you can include ContentRedactionOutput or ContentRedactionType in your request. For more information on these parameters, see StartCallAnalyticsStreamTranscription in the API Reference. To start a real-time Call Analytics transcription with post-call analytics enabled, you can use the AWS Management Console (demo only), HTTP/2, or WebSockets. For examples, see Starting a real-time Call Analytics transcription. Important Currently, the AWS Management Console only oﬀers a demo for real-time Call Analytics with pre-loaded audio examples. If you want to use your own audio, you must use the API (HTTP/2, WebSockets, or an SDK). Example post-call analytics output Post-call transcripts are displayed in a turn-by-turn format by segment. They include call characteristics, sentiment, call summarization, issue detection, and (optionally) PII redaction. If any of your post-call categories are a match to the audio content, these are also present in your output. To increase accuracy and further customize your transcripts to your use case, such as including industry-speciﬁc terms, add custom vocabularies or custom language models to your Call Analytics request. To mask, remove, or tag words you don't want in your transcription results, such as profanity, add vocabulary ﬁltering. Here is a compiled post-call analytics output example: { "JobStatus": "COMPLETED", "LanguageCode": "en-US", "AccountId": "1234567890", "Channel": "VOICE", "Participants": [{ "ParticipantRole": "AGENT" }, Post-call analytics with real-time transcriptions 392 Amazon Transcribe { "ParticipantRole": "CUSTOMER" }], "SessionId": "12a3b45c-de6f-78g9-0123-45h6ab78c901", Developer Guide "ContentMetadata": { "Output": "Raw" } "Transcript": [{ "LoudnessScores": [ 78.63, 78.37, 77.98, 74.18 ], "Content": "[PII], my name is [PII], how can I help?", ... "Content": "Well, I would like to cancel my recipe subscription.", "IssuesDetected": [{ "CharacterOffsets": { "Begin": 7, "End": 51 } }], ... "Content": "That's very sad to hear. Can I offer you a 50% discount to have you stay with us?", "Id": "649afe93-1e59-4ae9-a3ba-a0a613868f5d", "BeginOffsetMillis": 12180, "EndOffsetMillis": 16960, "Sentiment": "NEGATIVE", "ParticipantRole": "AGENT" }, { "LoudnessScores": [ 80.22, 79.48, 82.81 ], "Content": "That is a very generous offer. And I accept.", "Id": "f9266cba-34df-4ca8-9cea-4f62a52a7981", Post-call analytics with real-time transcriptions 393 Amazon Transcribe Developer Guide "BeginOffsetMillis": 17140, "EndOffsetMillis": 19860, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" }, ... "Content": "Wonderful. I made all changes to your account and now this discount is applied, please check.", "OutcomesDetected": [{ "CharacterOffsets": { "Begin": 12, "End": 78 } }], ... "Content": "I will send an email with all the details to you today, and I will call you back next week to follow up. Have a wonderful evening.", "Id": "78cd0923-cafd-44a5-a66e-09515796572f", "BeginOffsetMillis": 31800, "EndOffsetMillis": 39450, "Sentiment": "POSITIVE", "ParticipantRole": "AGENT" }, { "LoudnessScores": [ 78.54, 68.76, 67.76 ], "Content": "Thank you very much, sir. Goodbye.", "Id": "5c5e6be0-8349-4767-8447-986f995af7c3", "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460, "Sentiment": "POSITIVE", "ParticipantRole": "CUSTOMER" } ], ... "Categories": { Post-call analytics with real-time transcriptions 394 Amazon Transcribe Developer Guide "MatchedDetails": { "positive-resolution": { "PointsOfInterest": [{ "BeginOffsetMillis": 40040, "EndOffsetMillis": 42460 }] } }, "MatchedCategories": [ "positive-resolution" ] }, ... "ConversationCharacteristics": { "NonTalkTime": { "Instances": [], "TotalTimeMillis": 0 }, "Interruptions": { "TotalCount": 2, "TotalTimeMillis": 10700, "InterruptionsByInterrupter": { "AGENT": [{ "BeginOffsetMillis": 26040, "DurationMillis": 5510, "EndOffsetMillis": 31550 }], "CUSTOMER": [{ "BeginOffsetMillis": 770, "DurationMillis": 5190, "EndOffsetMillis": 5960 }] } }, "TotalConversationDurationMillis": 42460, "Sentiment": { "OverallSentiment": { "AGENT": 2.5, "CUSTOMER": 2.1 }, "SentimentByPeriod": { "QUARTER": { Post-call analytics with real-time transcriptions 395 Amazon Transcribe Developer Guide "AGENT": [{ "Score": 0.0, "BeginOffsetMillis": 0, "EndOffsetMillis": 9862 }, { "Score": -5.0, "BeginOffsetMillis": 9862, "EndOffsetMillis": 19725 }, { "Score": 5.0, "BeginOffsetMillis": 19725, "EndOffsetMillis": 29587 }, { "Score": 5.0, "BeginOffsetMillis": 29587, "EndOffsetMillis": 39450 } ], "CUSTOMER": [{ "Score": -2.5, "BeginOffsetMillis": 0, "EndOffsetMillis": 10615 }, { "Score": 5.0, "BeginOffsetMillis": 10615, "EndOffsetMillis": 21230 }, { "Score": 2.5, "BeginOffsetMillis": 21230, "EndOffsetMillis": 31845 }, { "Score": 5.0, "BeginOffsetMillis": 31845, "EndOffsetMillis": 42460 } ] } } Post-call analytics with real-time transcriptions 396 Developer Guide Amazon Transcribe }, "TalkSpeed": { "DetailsByParticipant": { "AGENT": { "AverageWordsPerMinute": 150 }, "CUSTOMER": { "AverageWordsPerMinute": 167 } } }, "TalkTime": { "DetailsByParticipant": { "AGENT": { "TotalTimeMillis": 32750 }, "CUSTOMER": { "TotalTimeMillis": 18010 } }, "TotalTimeMillis": 50760 } }, ... } Starting a real-time Call Analytics transcription Before starting a real-time Call Analytics transcription, you must create
transcribe-dg-081	transcribe-dg.pdf	81	"CUSTOMER": [{ "Score": -2.5, "BeginOffsetMillis": 0, "EndOffsetMillis": 10615 }, { "Score": 5.0, "BeginOffsetMillis": 10615, "EndOffsetMillis": 21230 }, { "Score": 2.5, "BeginOffsetMillis": 21230, "EndOffsetMillis": 31845 }, { "Score": 5.0, "BeginOffsetMillis": 31845, "EndOffsetMillis": 42460 } ] } } Post-call analytics with real-time transcriptions 396 Developer Guide Amazon Transcribe }, "TalkSpeed": { "DetailsByParticipant": { "AGENT": { "AverageWordsPerMinute": 150 }, "CUSTOMER": { "AverageWordsPerMinute": 167 } } }, "TalkTime": { "DetailsByParticipant": { "AGENT": { "TotalTimeMillis": 32750 }, "CUSTOMER": { "TotalTimeMillis": 18010 } }, "TotalTimeMillis": 50760 } }, ... } Starting a real-time Call Analytics transcription Before starting a real-time Call Analytics transcription, you must create all the categories you want Amazon Transcribe to match in your call. Note Call Analytics transcripts can't be retroactively matched to new categories. Only the categories you create before starting a Call Analytics transcription can be applied to that transcription output. If you've created one or more categories, and your audio matches all the rules within at least one of your categories, Amazon Transcribe ﬂags your output with the matching categories. If you choose Starting a transcription 397 Amazon Transcribe Developer Guide not to use categories, or if your audio doesn't match the rules speciﬁed in your categories, your transcript isn't ﬂagged. To include post-call analytics with your real-time Call Analytics transcription, you must provide an Amazon S3 bucket in your request using the OutputLocation parameter. You must also include a DataAccessRoleArn that has write permissions to the speciﬁed bucket. A separate transcript is produced and stored in the speciﬁed bucket upon completion of your real-time Call Analytics streaming session. With real-time Call Analytics, you also have the option to create real-time category alerts; refer to Creating real-time alerts for category matches for instructions. To start a real-time Call Analytics transcription, you can use the AWS Management Console, HTTP/2, or WebSockets; see the following for examples: Important Currently, the AWS Management Console only oﬀers a demo for real-time Call Analytics with pre-loaded audio examples. If you want to use your own audio, you must use the API (HTTP/2, WebSockets, or an SDK). AWS Management Console Use the following procedure to start a Call Analytics request. The calls that match all characteristics deﬁned by a category are labeled with that category. Note Only a demo is available in the AWS Management Console. To start a custom real-time analytics transcription, you must use the API. 1. In the navigation pane, under Amazon Transcribe Call Analytics, choose Analyze a real-time call. Starting a transcription 398 Amazon Transcribe Developer Guide 2. For Step 1: Specify input audio, choose a demo test ﬁle from the dropdown menu. 3. For Step 2: Review call categories, you have the option to review the real-time Call Analytics categories you previously created. All real-time Call Analytics categories are applied to your transcription. Choosing View categories opens a new pane that shows your existing real-time Call Analytics categories and provides a link to create new ones. Starting a transcription 399 Amazon Transcribe Developer Guide 4. For Step 3: Conﬁgure input and output, you have the option to apply additional settings. Choosing Conﬁgure advanced settings opens a new pane where you can specify content redaction settings. Starting a transcription 400 Amazon Transcribe Developer Guide Once you've made all your selections, choose Save to return to the main page. 5. To apply additional analytics, you can toggle on Post-call Analytics. This provides you with the same analytics as a post-call analytics transcription, including interruptions, loudness, non- talk time, talk speed, talk time, issues, action items, and outcomes. Post-call analytics output is stored in a separate ﬁle from your real-time Call Analytics transcript. If you apply post-call analytics, you must specify an Amazon S3 output ﬁle destination and an IAM role. You can optionally choose to encrypt your output. Starting a transcription 401 Amazon Transcribe Developer Guide 6. Choose Start streaming. HTTP/2 stream This example creates an HTTP/2 request with Call Analytics enabled. For more information on using HTTP/2 streaming with Amazon Transcribe, see Setting up an HTTP/2 stream. For more detail on parameters and headers speciﬁc to Amazon Transcribe, see StartCallAnalyticsStreamTranscription. This example includes post-call analytics. If you don't want post-call analytics, remove the PostCallAnalyticsSettings section from the request. Note that the conﬁguration event shown in the following example needs to be passed as the ﬁrst event in the stream. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartCallAnalyticsStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 transfer-encoding: chunked Starting a transcription 402 Amazon Transcribe Developer Guide { "AudioStream": { "AudioEvent": { "AudioChunk": blob }, "ConfigurationEvent": { "ChannelDefinitions": [ { "ChannelId": 0, "ParticipantRole": "AGENT" }, { "ChannelId": 1, "ParticipantRole": "CUSTOMER" } ], "PostCallAnalyticsSettings": { "OutputLocation": "s3://amzn-s3-demo-bucket/my-output-files/", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole" } } } } Parameter deﬁnitions can be found in the API Reference; parameters common
transcribe-dg-082	transcribe-dg.pdf	82	conﬁguration event shown in the following example needs to be passed as the ﬁrst event in the stream. POST /stream-transcription HTTP/2 host: transcribestreaming.us-west-2.amazonaws.com X-Amz-Target: com.amazonaws.transcribe.Transcribe.StartCallAnalyticsStreamTranscription Content-Type: application/vnd.amazon.eventstream X-Amz-Content-Sha256: string X-Amz-Date: 20220208T235959Z Authorization: AWS4-HMAC-SHA256 Credential=access-key/20220208/us-west-2/transcribe/ aws4_request, SignedHeaders=content-type;host;x-amz-content-sha256;x-amz-date;x-amz- target;x-amz-security-token, Signature=string x-amzn-transcribe-language-code: en-US x-amzn-transcribe-media-encoding: flac x-amzn-transcribe-sample-rate: 16000 transfer-encoding: chunked Starting a transcription 402 Amazon Transcribe Developer Guide { "AudioStream": { "AudioEvent": { "AudioChunk": blob }, "ConfigurationEvent": { "ChannelDefinitions": [ { "ChannelId": 0, "ParticipantRole": "AGENT" }, { "ChannelId": 1, "ParticipantRole": "CUSTOMER" } ], "PostCallAnalyticsSettings": { "OutputLocation": "s3://amzn-s3-demo-bucket/my-output-files/", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole" } } } } Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. WebSocket stream This example creates a presigned URL that uses Call Analytics in a WebSocket stream. Line breaks have been added for readability. For more information on using WebSocket streams with Amazon Transcribe, see Setting up a WebSocket stream. For more detail on parameters, see StartCallAnalyticsStreamTranscription. This example includes post-call analytics. If you don't want post-call analytics, remove the PostCallAnalyticsSettings section from the request. Note that the conﬁguration event shown in the following example needs to be passed as the ﬁrst event in the stream. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/call-analytics-stream- transcription-websocket? Starting a transcription 403 Amazon Transcribe Developer Guide &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=string &X-Amz-SignedHeaders=content-type%3Bhost%3Bx-amz-date &language-code=en-US &media-encoding=flac &sample-rate=16000 { "AudioStream": { "AudioEvent": { "AudioChunk": blob }, "ConfigurationEvent": { "ChannelDefinitions": [ { "ChannelId": 0, "ParticipantRole": "AGENT" }, { "ChannelId": 1, "ParticipantRole": "CUSTOMER" } ], "PostCallAnalyticsSettings": { "OutputLocation": "s3://amzn-s3-demo-bucket/my-output-files/", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/ExampleRole" } } } } Parameter deﬁnitions can be found in the API Reference; parameters common to all AWS API operations are listed in the Common Parameters section. Tip The above HTTP/2 and WebSocket examples include post-call analytics. If you don't want post-call analytics, remove the PostCallAnalyticsSettings section from the request. Starting a transcription 404 Amazon Transcribe Developer Guide If you enable PostCallAnalyticsSettings, you must send a conﬁguration event as the ﬁrst event. Your conﬁguration event includes settings for ChannelDenifitions and PostStreamAnalyticsSettings, as shown in the preceding examples. Binary data are passed as a binary message with content-type application/octet- stream and the conﬁguration event is passed as a text message with content-type application/json. For more information, see Setting up a streaming transcription. Creating real-time alerts for category matches To set up real-time alerts, you must ﬁrst create a TranscriptFilterType category with the REAL_TIME ﬂag. This ﬂag allows your category to be applied to real-time Call Analytics transcriptions. For instructions on creating a new category, see Creating categories for real-time transcriptions. When you start your real-time Call Analytics transcription, all categories that have the REAL_TIME ﬂag are automatically applied to your transcription output at a segment level. If a TranscriptFilterType match occurs, it appears under the CategoryEvent section of your transcript. You can then use this parameter and its sub-parameters, MatchedCategories and MatchedDetails, to set up custom real-time alerts. Here's an example of real-time Call Analytics transcription output for a CategoryEvent match: "CategoryEvent": { "MatchedCategories": [ "shipping-complaint" ], "MatchedDetails": { "my package never arrived" : { "TimestampRanges": [ { "BeginOffsetMillis": 19010, "EndOffsetMillis": 22690 } ] } } }, Starting a transcription 405 Amazon Transcribe Developer Guide The previous example represents an exact text match to the speech "my package never arrived," which represents a rule within the 'shipping-complaint' category. You can set up your real-time alert to include any combination of the listed parameters. For example, you could set your alert to include only the phrase that was matched (MatchedDetails) or only the category name (MatchedCategories). Or you could set your alert to include all parameters. How you set up your real-time alerts depends on your organization's interfaces and your desired alert type. For example, you could set a CategoryEvent match to send a pop-up notiﬁcation, an email, a text, or any other alert your system can accept. Real-time Call Analytics output Real-time Call Analytics transcripts are displayed in a turn-by-turn format by segment. They include category events, issue detection, sentiment, and PII identiﬁcation and redaction. Category events allow you to set real-time alerts; see Creating real-time alerts for category matches for more information. To increase accuracy and further customize your transcripts to your use case, such as including industry-speciﬁc terms, add custom vocabularies or custom language models to your Call Analytics request. To mask, remove, or tag words you don't want in your transcription results, such as profanity, add vocabulary ﬁltering. The following sections show examples of JSON output for real-time Call Analytics transcriptions. Category events Here's what a category match looks like in your transcription output. This example shows that the audio from the 19010 millisecond timestamp to the 22690 millisecond timestamp is a match to the 'network-complaint' category. In this case, the custom 'network-complaint' category required that the customer said "network issues" (exact word match). "CategoryEvent": { "MatchedCategories": [
transcribe-dg-083	transcribe-dg.pdf	83	custom vocabularies or custom language models to your Call Analytics request. To mask, remove, or tag words you don't want in your transcription results, such as profanity, add vocabulary ﬁltering. The following sections show examples of JSON output for real-time Call Analytics transcriptions. Category events Here's what a category match looks like in your transcription output. This example shows that the audio from the 19010 millisecond timestamp to the 22690 millisecond timestamp is a match to the 'network-complaint' category. In this case, the custom 'network-complaint' category required that the customer said "network issues" (exact word match). "CategoryEvent": { "MatchedCategories": [ "network-complaint" ], "MatchedDetails": { "network issues" : { "TimestampRanges": [ { Real-time Call Analytics output 406 Amazon Transcribe Developer Guide "BeginOffsetMillis": 9299375, "EndOffsetMillis": 7899375 } ] } } }, Issue detection Here's what an issue detection match looks like in your transcription output. This example shows that the text from character 26 to character 62 describes an issue. "UtteranceEvent": { ... "Transcript": "Wang Xiulan I'm tired of the network issues my phone is having.", ... "IssuesDetected": [ { "CharacterOffsets": { "BeginOffsetChar": 26, "EndOffsetChar": 62 } } ] }, Sentiment Here's what sentiment analysis looks like in your transcription output. "UtteranceEvent": { ... "Sentiment": "NEGATIVE", "Items": [{ ... PII identiﬁcation Here's what PII identiﬁcation looks like in your transcription output. "Entities": [ Real-time Call Analytics output 407 Developer Guide Amazon Transcribe { "Content": "Wang Xiulan", "Category": "PII", "Type": "NAME", "BeginOffsetMillis": 7999375, "EndOffsetMillis": 199375, "Confidence": 0.9989 } ], PII redaction Here's what PII redaction looks like in your transcription output. "Content": "[NAME]. Hi, [NAME]. I'm [NAME] Happy to be helping you today.", "Redaction": { "RedactedTimestamps": [ { "BeginOffsetMillis": 32670, "EndOffsetMillis": 33343 }, { "BeginOffsetMillis": 33518, "EndOffsetMillis": 33858 }, { "BeginOffsetMillis": 34068, "EndOffsetMillis": 34488 } ] }, Compiled real-time Call Analytics output For brevity, some content is replaced with ellipses in the following transcription output. { "CallAnalyticsTranscriptResultStream": { "BadRequestException": {}, "ConflictException": {}, "InternalFailureException": {}, "LimitExceededException": {}, "ServiceUnavailableException": {}, Real-time Call Analytics output 408 Amazon Transcribe "UtteranceEvent": { Developer Guide "UtteranceId": "58c27f92-7277-11ec-90d6-0242ac120003", "ParticipantRole": "CUSTOMER", "IsPartial": false, "Transcript": "Wang Xiulan I'm tired of the network issues my phone is having.", "BeginOffsetMillis": 19010, "EndOffsetMillis": 22690, "Sentiment": "NEGATIVE", "Items": [{ "Content": "Wang", "BeginOffsetMillis": 379937, "EndOffsetMillis": 299375, "Type": "pronunciation", "Confidence": 0.9961, "VocabularyFilterMatch": false }, { "Content": "Xiulan", "EndOffsetMillis": 5899375, "BeginOffsetMillis": 3899375, "Type": "pronunciation", "Confidence": 0.9961, "VocabularyFilterMatch": false }, ... { "Content": "network", "EndOffsetMillis": 199375, "BeginOffsetMillis": 9299375, "Type": "pronunciation", "Confidence": 0.9961, "VocabularyFilterMatch": false }, { "Content": "issues", "EndOffsetMillis": 7899375, "BeginOffsetMillis": 5999375, "Type": "pronunciation", "Confidence": 0.9961, "VocabularyFilterMatch": false }, { "Content": "my", Real-time Call Analytics output 409 Amazon Transcribe Developer Guide "EndOffsetMillis": 9199375, "BeginOffsetMillis": 7999375, "Type": "pronunciation", "Confidence": 0.9961, "VocabularyFilterMatch": false }, { "Content": "phone", "EndOffsetMillis": 199375, "BeginOffsetMillis": 9299375, "Type": "pronunciation", "Confidence": 0.9961, "VocabularyFilterMatch": false }, ... ], "Entities": [{ "Content": "Wang Xiulan", "Category": "PII", "Type": "NAME", "BeginOffsetMillis": 7999375, "EndOffsetMillis": 199375, "Confidence": 0.9989 }], "IssuesDetected": [{ "CharacterOffsets": { "BeginOffsetChar": 26, "EndOffsetChar": 62 } }] }, "CategoryEvent": { "MatchedCategories": [ "network-complaint" ], "MatchedDetails": { "network issues" : { "TimestampRanges": [ { "BeginOffsetMillis": 9299375, "EndOffsetMillis": 7899375 } ] } Real-time Call Analytics output 410 Amazon Transcribe } } } } Developer Guide Real-time Call Analytics output 411 Amazon Transcribe Developer Guide Transcribing your Amazon Chime calls in real time Amazon Transcribe is integrated with the Amazon Chime SDK, facilitating real-time transcriptions of your Amazon Chime calls. When you request a transcription using the Amazon Chime SDK API, Amazon Chime begins streaming audio to Amazon Transcribe and continues to do so for the duration of the call. The Amazon Chime SDK uses its 'active talker' algorithm to select the top two active talkers, and then sends their audio to Amazon Transcribe as two separate channels via a single stream. Meeting participants receive user-attributed transcriptions via Amazon Chime SDK data messages. You can view delivery examples in the Amazon Chime SDK Developer Guide. The data ﬂow of an Amazon Chime transcription is depicted in the following diagram: For additional information and detailed instructions on how to set up real-time Amazon Chime transcriptions, refer to Using Amazon Chime SDK live transcription in the Amazon Chime SDK Developer Guide. For API operations, refer to the Amazon Chime SDK API Reference. Dive deeper with the AWS Machine Learning Blog To learn more about improving accuracy with real-time transcriptions, see: • Amazon Chime SDK meetings now support live transcription with Amazon Transcribe and Amazon Transcribe Medical 412 Amazon Transcribe Developer Guide • Amazon Chime SDK for Telemedicine Solution 413 Amazon Transcribe Developer Guide Code examples for Amazon Transcribe using AWS SDKs The following code examples show how to use Amazon Transcribe with an AWS software development kit (SDK). Actions are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in
transcribe-dg-084	transcribe-dg.pdf	84	Learning Blog To learn more about improving accuracy with real-time transcriptions, see: • Amazon Chime SDK meetings now support live transcription with Amazon Transcribe and Amazon Transcribe Medical 412 Amazon Transcribe Developer Guide • Amazon Chime SDK for Telemedicine Solution 413 Amazon Transcribe Developer Guide Code examples for Amazon Transcribe using AWS SDKs The following code examples show how to use Amazon Transcribe with an AWS software development kit (SDK). Actions are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios. Scenarios are code examples that show you how to accomplish speciﬁc tasks by calling multiple functions within a service or combined with other AWS services. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Code examples • Basic examples for Amazon Transcribe using AWS SDKs • Actions for Amazon Transcribe using AWS SDKs • Use CreateVocabulary with an AWS SDK or CLI • Use DeleteMedicalTranscriptionJob with an AWS SDK or CLI • Use DeleteTranscriptionJob with an AWS SDK or CLI • Use DeleteVocabulary with an AWS SDK or CLI • Use GetTranscriptionJob with an AWS SDK or CLI • Use GetVocabulary with an AWS SDK or CLI • Use ListMedicalTranscriptionJobs with an AWS SDK or CLI • Use ListTranscriptionJobs with an AWS SDK or CLI • Use ListVocabularies with an AWS SDK or CLI • Use StartMedicalTranscriptionJob with an AWS SDK or CLI • Use StartTranscriptionJob with an AWS SDK or CLI • Use UpdateVocabulary with an AWS SDK or CLI • Scenarios for Amazon Transcribe using AWS SDKs • Build an Amazon Transcribe streaming app • Convert text to speech and back to text using an AWS SDK • Create and reﬁne an Amazon Transcribe custom vocabulary using an AWS SDK 414 Amazon Transcribe Developer Guide • Transcribe audio and get job data with Amazon Transcribe using an AWS SDK Basic examples for Amazon Transcribe using AWS SDKs The following code examples show how to use the basics of Amazon Transcribe with AWS SDKs. Examples • Actions for Amazon Transcribe using AWS SDKs • Use CreateVocabulary with an AWS SDK or CLI • Use DeleteMedicalTranscriptionJob with an AWS SDK or CLI • Use DeleteTranscriptionJob with an AWS SDK or CLI • Use DeleteVocabulary with an AWS SDK or CLI • Use GetTranscriptionJob with an AWS SDK or CLI • Use GetVocabulary with an AWS SDK or CLI • Use ListMedicalTranscriptionJobs with an AWS SDK or CLI • Use ListTranscriptionJobs with an AWS SDK or CLI • Use ListVocabularies with an AWS SDK or CLI • Use StartMedicalTranscriptionJob with an AWS SDK or CLI • Use StartTranscriptionJob with an AWS SDK or CLI • Use UpdateVocabulary with an AWS SDK or CLI Actions for Amazon Transcribe using AWS SDKs The following code examples demonstrate how to perform individual Amazon Transcribe actions with AWS SDKs. Each example includes a link to GitHub, where you can ﬁnd instructions for setting up and running the code. These excerpts call the Amazon Transcribe API and are code excerpts from larger programs that must be run in context. You can see actions in context in Scenarios for Amazon Transcribe using AWS SDKs . The following examples include only the most commonly used actions. For a complete list, see the Amazon Transcribe API Reference. Examples Basics 415 Amazon Transcribe Developer Guide • Use CreateVocabulary with an AWS SDK or CLI • Use DeleteMedicalTranscriptionJob with an AWS SDK or CLI • Use DeleteTranscriptionJob with an AWS SDK or CLI • Use DeleteVocabulary with an AWS SDK or CLI • Use GetTranscriptionJob with an AWS SDK or CLI • Use GetVocabulary with an AWS SDK or CLI • Use ListMedicalTranscriptionJobs with an AWS SDK or CLI • Use ListTranscriptionJobs with an AWS SDK or CLI • Use ListVocabularies with an AWS SDK or CLI • Use StartMedicalTranscriptionJob with an AWS SDK or CLI • Use StartTranscriptionJob with an AWS SDK or CLI • Use UpdateVocabulary with an AWS SDK or CLI Use CreateVocabulary with an AWS SDK or CLI The following code examples show how to use CreateVocabulary. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: • Create and reﬁne a custom vocabulary .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Create a custom vocabulary using a list of phrases. Custom vocabularies /// improve transcription accuracy for one or more
transcribe-dg-085	transcribe-dg.pdf	85	CLI Use CreateVocabulary with an AWS SDK or CLI The following code examples show how to use CreateVocabulary. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: • Create and reﬁne a custom vocabulary .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Create a custom vocabulary using a list of phrases. Custom vocabularies /// improve transcription accuracy for one or more specific words. Actions 416 Amazon Transcribe /// </summary> Developer Guide /// <param name="languageCode">The language code of the vocabulary.</param> /// <param name="phrases">Phrases to use in the vocabulary.</param> /// <param name="vocabularyName">Name for the vocabulary.</param> /// <returns>The state of the custom vocabulary.</returns> public async Task<VocabularyState> CreateCustomVocabulary(LanguageCode languageCode, List<string> phrases, string vocabularyName) { var response = await _amazonTranscribeService.CreateVocabularyAsync( new CreateVocabularyRequest { LanguageCode = languageCode, Phrases = phrases, VocabularyName = vocabularyName }); return response.VocabularyState; } • For API details, see CreateVocabulary in AWS SDK for .NET API Reference. CLI AWS CLI To create a custom vocabulary The following create-vocabulary example creates a custom vocabulary. To create a custom vocabulary, you must have created a text ﬁle with all the terms that you want to transcribe more accurately. For vocabulary-ﬁle-uri, specify the Amazon Simple Storage Service (Amazon S3) URI of that text ﬁle. For language-code, specify a language code corresponding to the language of your custom vocabulary. For vocabulary-name, specify what you want to call your custom vocabulary. aws transcribe create-vocabulary \ --language-code language-code \ --vocabulary-name cli-vocab-example \ --vocabulary-file-uri s3://amzn-s3-demo-bucket/Amazon-S3-prefix/the-text- file-for-the-custom-vocabulary.txt Actions 417 Amazon Transcribe Output: { "VocabularyName": "cli-vocab-example", "LanguageCode": "language-code", "VocabularyState": "PENDING" } Developer Guide For more information, see Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see CreateVocabulary in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def create_vocabulary( vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None ): """ Creates a custom vocabulary that can be used to improve the accuracy of transcription jobs. This function returns as soon as the vocabulary processing is started. Call get_vocabulary to get the current status of the vocabulary. The vocabulary is ready to use when its status is 'READY'. :param vocabulary_name: The name of the custom vocabulary. :param language_code: The language code of the vocabulary. For example, en-US or nl-NL. :param transcribe_client: The Boto3 Transcribe client. :param phrases: A list of comma-separated phrases to include in the vocabulary. :param table_uri: A table of phrases and pronunciation hints to include in the Actions 418 Amazon Transcribe Developer Guide vocabulary. :return: Information about the newly created vocabulary. """ try: vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code} if phrases is not None: vocab_args["Phrases"] = phrases elif table_uri is not None: vocab_args["VocabularyFileUri"] = table_uri response = transcribe_client.create_vocabulary(**vocab_args) logger.info("Created custom vocabulary %s.", response["VocabularyName"]) except ClientError: logger.exception("Couldn't create custom vocabulary %s.", vocabulary_name) raise else: return response • For API details, see CreateVocabulary in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DeleteMedicalTranscriptionJob with an AWS SDK or CLI The following code examples show how to use DeleteMedicalTranscriptionJob. .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Actions 419 Amazon Transcribe Developer Guide /// <summary> /// Delete a medical transcription job. Also deletes the transcript associated with the job. /// </summary> /// <param name="jobName">Name of the medical transcription job to delete.</ param> /// <returns>True if successful.</returns> public async Task<bool> DeleteMedicalTranscriptionJob(string jobName) { var response = await _amazonTranscribeService.DeleteMedicalTranscriptionJobAsync( new DeleteMedicalTranscriptionJobRequest() { MedicalTranscriptionJobName = jobName }); return response.HttpStatusCode == HttpStatusCode.OK; } • For API details, see DeleteMedicalTranscriptionJob in AWS SDK for .NET API Reference. CLI AWS CLI To delete a medical transcription job The following delete-medical-transcription-job example deletes a medical transcription job. aws transcribe delete-medical-transcription-job \ --medical-transcription-job-name medical-transcription-job-name This command produces no output. For more information, see DeleteMedicalTranscriptionJob in the Amazon Transcribe Developer Guide. • For API details, see DeleteMedicalTranscriptionJob in AWS CLI Command Reference. Actions 420 Amazon Transcribe JavaScript SDK for JavaScript (v3) Note Developer Guide There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; Delete
transcribe-dg-086	transcribe-dg.pdf	86	produces no output. For more information, see DeleteMedicalTranscriptionJob in the Amazon Transcribe Developer Guide. • For API details, see DeleteMedicalTranscriptionJob in AWS CLI Command Reference. Actions 420 Amazon Transcribe JavaScript SDK for JavaScript (v3) Note Developer Guide There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; Delete a medical transcription job. // Import the required AWS SDK clients and commands for Node.js import { DeleteMedicalTranscriptionJobCommand } from "@aws-sdk/client- transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params = { MedicalTranscriptionJobName: "MEDICAL_JOB_NAME", // For example, 'medical_transciption_demo' }; export const run = async () => { try { const data = await transcribeClient.send( new DeleteMedicalTranscriptionJobCommand(params), ); console.log("Success - deleted"); return data; // For unit tests. } catch (err) { console.log("Error", err); Actions 421 Amazon Transcribe } }; run(); Developer Guide • For more information, see AWS SDK for JavaScript Developer Guide. • For API details, see DeleteMedicalTranscriptionJob in AWS SDK for JavaScript API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DeleteTranscriptionJob with an AWS SDK or CLI The following code examples show how to use DeleteTranscriptionJob. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: • Create and reﬁne a custom vocabulary .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Delete a transcription job. Also deletes the transcript associated with the job. /// </summary> /// <param name="jobName">Name of the transcription job to delete.</param> /// <returns>True if successful.</returns> Actions 422 Amazon Transcribe Developer Guide public async Task<bool> DeleteTranscriptionJob(string jobName) { var response = await _amazonTranscribeService.DeleteTranscriptionJobAsync( new DeleteTranscriptionJobRequest() { TranscriptionJobName = jobName }); return response.HttpStatusCode == HttpStatusCode.OK; } • For API details, see DeleteTranscriptionJob in AWS SDK for .NET API Reference. CLI AWS CLI To delete one of your transcription jobs The following delete-transcription-job example deletes one of your transcription jobs. aws transcribe delete-transcription-job \ --transcription-job-name your-transcription-job This command produces no output. For more information, see DeleteTranscriptionJob in the Amazon Transcribe Developer Guide. • For API details, see DeleteTranscriptionJob in AWS CLI Command Reference. JavaScript SDK for JavaScript (v3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Actions 423 Amazon Transcribe Developer Guide Delete a transcription job. // Import the required AWS SDK clients and commands for Node.js import { DeleteTranscriptionJobCommand } from "@aws-sdk/client-transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params = { TranscriptionJobName: "JOB_NAME", // Required. For example, 'transciption_demo' }; export const run = async () => { try { const data = await transcribeClient.send( new DeleteTranscriptionJobCommand(params), ); console.log("Success - deleted"); return data; // For unit tests. } catch (err) { console.log("Error", err); } }; run(); Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; • For more information, see AWS SDK for JavaScript Developer Guide. • For API details, see DeleteTranscriptionJob in AWS SDK for JavaScript API Reference. Actions 424 Amazon Transcribe Python SDK for Python (Boto3) Note Developer Guide There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def delete_job(job_name, transcribe_client): """ Deletes a transcription job. This also deletes the transcript associated with the job. :param job_name: The name of the job to delete. :param transcribe_client: The Boto3 Transcribe client. """ try: transcribe_client.delete_transcription_job(TranscriptionJobName=job_name) logger.info("Deleted job %s.", job_name) except ClientError: logger.exception("Couldn't delete job %s.", job_name) raise • For API details, see DeleteTranscriptionJob in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DeleteVocabulary with an AWS SDK or CLI The following code examples show how to use DeleteVocabulary. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: Actions 425 Amazon Transcribe
transcribe-dg-087	transcribe-dg.pdf	87	delete job %s.", job_name) raise • For API details, see DeleteTranscriptionJob in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DeleteVocabulary with an AWS SDK or CLI The following code examples show how to use DeleteVocabulary. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: Actions 425 Amazon Transcribe Developer Guide • Create and reﬁne a custom vocabulary .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Delete an existing custom vocabulary. /// </summary> /// <param name="vocabularyName">Name of the vocabulary to delete.</param> /// <returns>True if successful.</returns> public async Task<bool> DeleteCustomVocabulary(string vocabularyName) { var response = await _amazonTranscribeService.DeleteVocabularyAsync( new DeleteVocabularyRequest { VocabularyName = vocabularyName }); return response.HttpStatusCode == HttpStatusCode.OK; } • For API details, see DeleteVocabulary in AWS SDK for .NET API Reference. CLI AWS CLI To delete a custom vocabulary The following delete-vocabulary example deletes a custom vocabulary. aws transcribe delete-vocabulary \ Actions 426 Amazon Transcribe Developer Guide --vocabulary-name vocabulary-name This command produces no output. For more information, see Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see DeleteVocabulary in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def delete_vocabulary(vocabulary_name, transcribe_client): """ Deletes a custom vocabulary. :param vocabulary_name: The name of the vocabulary to delete. :param transcribe_client: The Boto3 Transcribe client. """ try: transcribe_client.delete_vocabulary(VocabularyName=vocabulary_name) logger.info("Deleted vocabulary %s.", vocabulary_name) except ClientError: logger.exception("Couldn't delete vocabulary %s.", vocabulary_name) raise • For API details, see DeleteVocabulary in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Actions 427 Amazon Transcribe Developer Guide Use GetTranscriptionJob with an AWS SDK or CLI The following code examples show how to use GetTranscriptionJob. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code examples: • Create and reﬁne a custom vocabulary • Transcribe audio and get job data .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Get details about a transcription job. /// </summary> /// <param name="jobName">A unique name for the transcription job.</param> /// <returns>A TranscriptionJob instance with information on the requested job.</returns> public async Task<TranscriptionJob> GetTranscriptionJob(string jobName) { var response = await _amazonTranscribeService.GetTranscriptionJobAsync( new GetTranscriptionJobRequest() { TranscriptionJobName = jobName }); return response.TranscriptionJob; } • For API details, see GetTranscriptionJob in AWS SDK for .NET API Reference. Actions 428 Amazon Transcribe CLI AWS CLI Developer Guide To get information about a speciﬁc transcription job The following get-transcription-job example gets information about a speciﬁc transcription job. To access the transcription results, use the TranscriptFileUri parameter. Use the MediaFileUri parameter to see which audio ﬁle you transcribed with this job. You can use the Settings object to see the optional features you've enabled in the transcription job. aws transcribe get-transcription-job \ --transcription-job-name your-transcription-job Output: { "TranscriptionJob": { "TranscriptionJobName": "your-transcription-job", "TranscriptionJobStatus": "COMPLETED", "LanguageCode": "language-code", "MediaSampleRateHertz": 48000, "MediaFormat": "mp4", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.file- extension" }, "Transcript": { "TranscriptFileUri": "https://Amazon-S3-file-location-of- transcription-output" }, "StartTime": "2020-09-18T22:27:23.970000+00:00", "CreationTime": "2020-09-18T22:27:23.948000+00:00", "CompletionTime": "2020-09-18T22:28:21.197000+00:00", "Settings": { "ChannelIdentification": false, "ShowAlternatives": false }, "IdentifyLanguage": true, "IdentifiedLanguageScore": 0.8672199249267578 } } Actions 429 Amazon Transcribe Developer Guide For more information, see Getting Started (AWS Command Line Interface) in the Amazon Transcribe Developer Guide. • For API details, see GetTranscriptionJob in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def get_job(job_name, transcribe_client): """ Gets details about a transcription job. :param job_name: The name of the job to retrieve. :param transcribe_client: The Boto3 Transcribe client. :return: The retrieved transcription job. """ try: response = transcribe_client.get_transcription_job( TranscriptionJobName=job_name ) job = response["TranscriptionJob"] logger.info("Got job %s.", job["TranscriptionJobName"]) except ClientError: logger.exception("Couldn't get job %s.", job_name) raise else: return job • For API details, see GetTranscriptionJob in AWS SDK for Python (Boto3) API Reference. Actions 430 Amazon Transcribe Developer Guide For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting
transcribe-dg-088	transcribe-dg.pdf	88	transcribe_client): """ Gets details about a transcription job. :param job_name: The name of the job to retrieve. :param transcribe_client: The Boto3 Transcribe client. :return: The retrieved transcription job. """ try: response = transcribe_client.get_transcription_job( TranscriptionJobName=job_name ) job = response["TranscriptionJob"] logger.info("Got job %s.", job["TranscriptionJobName"]) except ClientError: logger.exception("Couldn't get job %s.", job_name) raise else: return job • For API details, see GetTranscriptionJob in AWS SDK for Python (Boto3) API Reference. Actions 430 Amazon Transcribe Developer Guide For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use GetVocabulary with an AWS SDK or CLI The following code examples show how to use GetVocabulary. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: • Create and reﬁne a custom vocabulary .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Get information about a custom vocabulary. /// </summary> /// <param name="vocabularyName">Name of the vocabulary.</param> /// <returns>The state of the custom vocabulary.</returns> public async Task<VocabularyState> GetCustomVocabulary(string vocabularyName) { var response = await _amazonTranscribeService.GetVocabularyAsync( new GetVocabularyRequest() { VocabularyName = vocabularyName }); return response.VocabularyState; } Actions 431 Amazon Transcribe Developer Guide • For API details, see GetVocabulary in AWS SDK for .NET API Reference. CLI AWS CLI To get information about a custom vocabulary The following get-vocabulary example gets information on a previously created custom vocabulary. aws transcribe get-vocabulary \ --vocabulary-name cli-vocab-1 Output: { "VocabularyName": "cli-vocab-1", "LanguageCode": "language-code", "VocabularyState": "READY", "LastModifiedTime": "2020-09-19T23:22:32.836000+00:00", "DownloadUri": "https://link-to-download-the-text-file-used-to-create-your- custom-vocabulary" } For more information, see Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see GetVocabulary in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def get_vocabulary(vocabulary_name, transcribe_client): """ Actions 432 Amazon Transcribe Developer Guide Gets information about a custom vocabulary. :param vocabulary_name: The name of the vocabulary to retrieve. :param transcribe_client: The Boto3 Transcribe client. :return: Information about the vocabulary. """ try: response = transcribe_client.get_vocabulary(VocabularyName=vocabulary_name) logger.info("Got vocabulary %s.", response["VocabularyName"]) except ClientError: logger.exception("Couldn't get vocabulary %s.", vocabulary_name) raise else: return response • For API details, see GetVocabulary in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use ListMedicalTranscriptionJobs with an AWS SDK or CLI The following code examples show how to use ListMedicalTranscriptionJobs. .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> Actions 433 Amazon Transcribe Developer Guide /// List medical transcription jobs, optionally with a name filter. /// </summary> /// <param name="jobNameContains">Optional name filter for the medical transcription jobs.</param> /// <returns>A list of summaries about medical transcription jobs.</returns> public async Task<List<MedicalTranscriptionJobSummary>> ListMedicalTranscriptionJobs( string? jobNameContains = null) { var response = await _amazonTranscribeService.ListMedicalTranscriptionJobsAsync( new ListMedicalTranscriptionJobsRequest() { JobNameContains = jobNameContains }); return response.MedicalTranscriptionJobSummaries; } • For API details, see ListMedicalTranscriptionJobs in AWS SDK for .NET API Reference. CLI AWS CLI To list your medical transcription jobs The following list-medical-transcription-jobs example lists the medical transcription jobs associated with your AWS account and Region. To get more information about a particular transcription job, copy the value of a MedicalTranscriptionJobName parameter in the transcription output, and specify that value for the MedicalTranscriptionJobName option of the get-medical-transcription- job command. To see more of your transcription jobs, copy the value of the NextToken parameter, run the list-medical-transcription-jobs command again, and specify that value in the --next-token option. aws transcribe list-medical-transcription-jobs Output: Actions 434 Amazon Transcribe Developer Guide { "NextToken": "3/PblzkiGhzjER3KHuQt2fmbPLF7cDYafjFMEoGn44ON/ gsuUSTIkGyanvRE6WMXFd/ZTEc2EZj+P9eii/ z1O2FDYli6RLI0WoRX4RwMisVrh9G0Kie0Y8ikBCdtqlZB10Wa9McC+ebOl +LaDtZPC4u6ttoHLRlEfzqstHXSgapXg3tEBtm9piIaPB6MOM5BB6t86+qtmocTR/ qrteHZBBudhTfbCwhsxaqujHiiUvFdm3BQbKKWIW06yV9b+4f38oD2lVIan +vfUs3gBYAl5VTDmXXzQPBQOHPjtwmFI+IWX15nSUjWuN3TUylHgPWzDaYT8qBtu0Z+3UG4V6b +K2CC0XszXg5rBq9hYgNzy4XoFh/6s5DoSnzq49Q9xHgHdT2yBADFmvFK7myZBsj75+2vQZOSVpWUPy3WT/32zFAcoELHR4unuWhXPwjbKU +mFYfUjtTZ8n/jq7aQEjQ42A +X/7K6JgOcdVPtEg8PlDr5kgYYG3q3OmYXX37U3FZuJmnTI63VtIXsNnOU5eGoYObtpk00Nq9UkzgSJxqj84ZD5n +S0EGy9ZUYBJRRcGeYUM3Q4DbSJfUwSAqcFdLIWZdp8qIREMQIBWy7BLwSdyqsQo2vRrd53hm5aWM7SVf6pPq6X/ IXR5+1eUOOD8/coaTT4ES2DerbV6RkV4o0VT1d0SdVX/ MmtkNG8nYj8PqU07w7988quh1ZP6D80veJS1q73tUUR9MjnGernW2tAnvnLNhdefBcD +sZVfYq3iBMFY7wTy1P1G6NqW9GrYDYoX3tTPWlD7phpbVSyKrh/ PdYrps5UxnsGoA1b7L/FfAXDfUoGrGUB4N3JsPYXX9D++g+6gV1qBBs/ WfF934aKqfD6UTggm/zV3GAOWiBpfvAZRvEb924i6yGHyMC7y54O1ZAwSBupmI +FFd13CaPO4kN1vJlth6aM5vUPXg4BpyUhtbRhwD/KxCvf9K0tLJGyL1A==", "MedicalTranscriptionJobSummaries": [ { "MedicalTranscriptionJobName": "vocabulary-dictation-medical- transcription-job", "CreationTime": "2020-09-21T21:17:27.016000+00:00", "StartTime": "2020-09-21T21:17:27.045000+00:00", "CompletionTime": "2020-09-21T21:17:59.561000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "DICTATION" }, { "MedicalTranscriptionJobName": "alternatives-dictation-medical- transcription-job", "CreationTime": "2020-09-21T21:01:14.569000+00:00", "StartTime": "2020-09-21T21:01:14.592000+00:00", "CompletionTime": "2020-09-21T21:01:43.606000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "DICTATION" }, { Actions 435 Amazon Transcribe Developer Guide "MedicalTranscriptionJobName": "alternatives-conversation-medical- transcription-job", "CreationTime": "2020-09-21T19:09:18.171000+00:00", "StartTime": "2020-09-21T19:09:18.199000+00:00", "CompletionTime": "2020-09-21T19:10:22.516000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" }, { "MedicalTranscriptionJobName": "speaker-id-conversation-medical- transcription-job", "CreationTime": "2020-09-21T18:43:37.157000+00:00", "StartTime": "2020-09-21T18:43:37.265000+00:00", "CompletionTime": "2020-09-21T18:44:21.192000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" }, { "MedicalTranscriptionJobName": "multichannel-conversation-medical- transcription-job", "CreationTime": "2020-09-20T23:46:44.053000+00:00", "StartTime": "2020-09-20T23:46:44.081000+00:00", "CompletionTime": "2020-09-20T23:47:35.851000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } ] } For more information, see https://docs.aws.amazon.com/transcribe/latest/dg/batch-med- transcription.html> in
transcribe-dg-089	transcribe-dg.pdf	89	}, { "MedicalTranscriptionJobName": "alternatives-dictation-medical- transcription-job", "CreationTime": "2020-09-21T21:01:14.569000+00:00", "StartTime": "2020-09-21T21:01:14.592000+00:00", "CompletionTime": "2020-09-21T21:01:43.606000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "DICTATION" }, { Actions 435 Amazon Transcribe Developer Guide "MedicalTranscriptionJobName": "alternatives-conversation-medical- transcription-job", "CreationTime": "2020-09-21T19:09:18.171000+00:00", "StartTime": "2020-09-21T19:09:18.199000+00:00", "CompletionTime": "2020-09-21T19:10:22.516000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" }, { "MedicalTranscriptionJobName": "speaker-id-conversation-medical- transcription-job", "CreationTime": "2020-09-21T18:43:37.157000+00:00", "StartTime": "2020-09-21T18:43:37.265000+00:00", "CompletionTime": "2020-09-21T18:44:21.192000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" }, { "MedicalTranscriptionJobName": "multichannel-conversation-medical- transcription-job", "CreationTime": "2020-09-20T23:46:44.053000+00:00", "StartTime": "2020-09-20T23:46:44.081000+00:00", "CompletionTime": "2020-09-20T23:47:35.851000+00:00", "LanguageCode": "en-US", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "CUSTOMER_BUCKET", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } ] } For more information, see https://docs.aws.amazon.com/transcribe/latest/dg/batch-med- transcription.html> in the Amazon Transcribe Developer Guide. • For API details, see ListMedicalTranscriptionJobs in AWS CLI Command Reference. Actions 436 Amazon Transcribe JavaScript SDK for JavaScript (v3) Note Developer Guide There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; List medical transcription jobs. // Import the required AWS SDK clients and commands for Node.js import { StartMedicalTranscriptionJobCommand } from "@aws-sdk/client-transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params = { MedicalTranscriptionJobName: "MEDICAL_JOB_NAME", // Required OutputBucketName: "OUTPUT_BUCKET_NAME", // Required Specialty: "PRIMARYCARE", // Required. Possible values are 'PRIMARYCARE' Type: "JOB_TYPE", // Required. Possible values are 'CONVERSATION' and 'DICTATION' LanguageCode: "LANGUAGE_CODE", // For example, 'en-US' MediaFormat: "SOURCE_FILE_FORMAT", // For example, 'wav' Media: { MediaFileUri: "SOURCE_FILE_LOCATION", // The S3 object location of the input media file. The URI must be in the same region // as the API endpoint that you are calling.For example, // "https://transcribe-demo.s3-REGION.amazonaws.com/hello_world.wav" }, Actions 437 Amazon Transcribe }; Developer Guide export const run = async () => { try { const data = await transcribeClient.send( new StartMedicalTranscriptionJobCommand(params), ); console.log("Success - put", data); return data; // For unit tests. } catch (err) { console.log("Error", err); } }; run(); • For more information, see AWS SDK for JavaScript Developer Guide. • For API details, see ListMedicalTranscriptionJobs in AWS SDK for JavaScript API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use ListTranscriptionJobs with an AWS SDK or CLI The following code examples show how to use ListTranscriptionJobs. .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// List transcription jobs, optionally with a name filter. /// </summary> Actions 438 Amazon Transcribe Developer Guide /// <param name="jobNameContains">Optional name filter for the transcription jobs.</param> /// <returns>A list of transcription job summaries.</returns> public async Task<List<TranscriptionJobSummary>> ListTranscriptionJobs(string? jobNameContains = null) { var response = await _amazonTranscribeService.ListTranscriptionJobsAsync( new ListTranscriptionJobsRequest() { JobNameContains = jobNameContains }); return response.TranscriptionJobSummaries; } • For API details, see ListTranscriptionJobs in AWS SDK for .NET API Reference. CLI AWS CLI To list your transcription jobs The following list-transcription-jobs example lists the transcription jobs associated with your AWS account and Region. aws transcribe list-transcription-jobs Output: { "NextToken": "NextToken", "TranscriptionJobSummaries": [ { "TranscriptionJobName": "speak-id-job-1", "CreationTime": "2020-08-17T21:06:15.391000+00:00", "StartTime": "2020-08-17T21:06:15.416000+00:00", "CompletionTime": "2020-08-17T21:07:05.098000+00:00", "LanguageCode": "language-code", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "SERVICE_BUCKET" Actions 439 Amazon Transcribe }, Developer Guide { "TranscriptionJobName": "job-1", "CreationTime": "2020-08-17T20:50:24.207000+00:00", "StartTime": "2020-08-17T20:50:24.230000+00:00", "CompletionTime": "2020-08-17T20:52:18.737000+00:00", "LanguageCode": "language-code", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "SERVICE_BUCKET" }, { "TranscriptionJobName": "sdk-test-job-4", "CreationTime": "2020-08-17T20:32:27.917000+00:00", "StartTime": "2020-08-17T20:32:27.956000+00:00", "CompletionTime": "2020-08-17T20:33:15.126000+00:00", "LanguageCode": "language-code", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "SERVICE_BUCKET" }, { "TranscriptionJobName": "Diarization-speak-id", "CreationTime": "2020-08-10T22:10:09.066000+00:00", "StartTime": "2020-08-10T22:10:09.116000+00:00", "CompletionTime": "2020-08-10T22:26:48.172000+00:00", "LanguageCode": "language-code", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "SERVICE_BUCKET" }, { "TranscriptionJobName": "your-transcription-job-name", "CreationTime": "2020-07-29T17:45:09.791000+00:00", "StartTime": "2020-07-29T17:45:09.826000+00:00", "CompletionTime": "2020-07-29T17:46:20.831000+00:00", "LanguageCode": "language-code", "TranscriptionJobStatus": "COMPLETED", "OutputLocationType": "SERVICE_BUCKET" } ] } For more information, see Getting Started (AWS Command Line Interface) in the Amazon Transcribe Developer Guide. • For API details, see ListTranscriptionJobs in AWS CLI Command Reference. Actions 440 Amazon Transcribe Java SDK for Java 2.x Note Developer Guide There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. public class ListTranscriptionJobs { public static void main(String[] args) { TranscribeClient transcribeClient = TranscribeClient.builder() .region(Region.US_EAST_1) .build(); listTranscriptionJobs(transcribeClient); } public static void listTranscriptionJobs(TranscribeClient transcribeClient) { ListTranscriptionJobsRequest listJobsRequest = ListTranscriptionJobsRequest.builder() .build(); transcribeClient.listTranscriptionJobsPaginator(listJobsRequest).stream() .flatMap(response -> response.transcriptionJobSummaries().stream()) .forEach(jobSummary -> { System.out.println("Job Name: " + jobSummary.transcriptionJobName()); System.out.println("Job Status: " + jobSummary.transcriptionJobStatus()); System.out.println("Output Location: " + jobSummary.outputLocationType()); // Add more information as needed // Retrieve additional details for the job if necessary GetTranscriptionJobResponse jobDetails = transcribeClient.getTranscriptionJob( GetTranscriptionJobRequest.builder() Actions 441 Amazon Transcribe Developer Guide .transcriptionJobName(jobSummary.transcriptionJobName()) .build()); // Display additional details System.out.println("Language Code: " + jobDetails.transcriptionJob().languageCode()); System.out.println("Media Format: " + jobDetails.transcriptionJob().mediaFormat()); // Add more details as needed System.out.println("--------------"); }); } }
transcribe-dg-090	transcribe-dg.pdf	90	{ public static void main(String[] args) { TranscribeClient transcribeClient = TranscribeClient.builder() .region(Region.US_EAST_1) .build(); listTranscriptionJobs(transcribeClient); } public static void listTranscriptionJobs(TranscribeClient transcribeClient) { ListTranscriptionJobsRequest listJobsRequest = ListTranscriptionJobsRequest.builder() .build(); transcribeClient.listTranscriptionJobsPaginator(listJobsRequest).stream() .flatMap(response -> response.transcriptionJobSummaries().stream()) .forEach(jobSummary -> { System.out.println("Job Name: " + jobSummary.transcriptionJobName()); System.out.println("Job Status: " + jobSummary.transcriptionJobStatus()); System.out.println("Output Location: " + jobSummary.outputLocationType()); // Add more information as needed // Retrieve additional details for the job if necessary GetTranscriptionJobResponse jobDetails = transcribeClient.getTranscriptionJob( GetTranscriptionJobRequest.builder() Actions 441 Amazon Transcribe Developer Guide .transcriptionJobName(jobSummary.transcriptionJobName()) .build()); // Display additional details System.out.println("Language Code: " + jobDetails.transcriptionJob().languageCode()); System.out.println("Media Format: " + jobDetails.transcriptionJob().mediaFormat()); // Add more details as needed System.out.println("--------------"); }); } } • For API details, see ListTranscriptionJobs in AWS SDK for Java 2.x API Reference. JavaScript SDK for JavaScript (v3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. List transcription jobs. // Import the required AWS SDK clients and commands for Node.js import { ListTranscriptionJobsCommand } from "@aws-sdk/client-transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params = { JobNameContains: "KEYWORD", // Not required. Returns only transcription // job names containing this string }; Actions 442 Amazon Transcribe Developer Guide export const run = async () => { try { const data = await transcribeClient.send( new ListTranscriptionJobsCommand(params), ); console.log("Success", data.TranscriptionJobSummaries); return data; // For unit tests. } catch (err) { console.log("Error", err); } }; run(); Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; • For more information, see AWS SDK for JavaScript Developer Guide. • For API details, see ListTranscriptionJobs in AWS SDK for JavaScript API Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def list_jobs(job_filter, transcribe_client): """ Lists summaries of the transcription jobs for the current AWS account. Actions 443 Amazon Transcribe Developer Guide :param job_filter: The list of returned jobs must contain this string in their names. :param transcribe_client: The Boto3 Transcribe client. :return: The list of retrieved transcription job summaries. """ try: response = transcribe_client.list_transcription_jobs(JobNameContains=job_filter) jobs = response["TranscriptionJobSummaries"] next_token = response.get("NextToken") while next_token is not None: response = transcribe_client.list_transcription_jobs( JobNameContains=job_filter, NextToken=next_token ) jobs += response["TranscriptionJobSummaries"] next_token = response.get("NextToken") logger.info("Got %s jobs with filter %s.", len(jobs), job_filter) except ClientError: logger.exception("Couldn't get jobs with filter %s.", job_filter) raise else: return jobs • For API details, see ListTranscriptionJobs in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use ListVocabularies with an AWS SDK or CLI The following code examples show how to use ListVocabularies. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: • Create and reﬁne a custom vocabulary Actions 444 Amazon Transcribe .NET SDK for .NET Note Developer Guide There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// List custom vocabularies for the current account. Optionally specify a name /// filter and a specific state to filter the vocabularies list. /// </summary> /// <param name="nameContains">Optional string the vocabulary name must contain.</param> /// <param name="stateEquals">Optional state of the vocabulary.</param> /// <returns>List of information about the vocabularies.</returns> public async Task<List<VocabularyInfo>> ListCustomVocabularies(string? nameContains = null, VocabularyState? stateEquals = null) { var response = await _amazonTranscribeService.ListVocabulariesAsync( new ListVocabulariesRequest() { NameContains = nameContains, StateEquals = stateEquals }); return response.Vocabularies; } • For API details, see ListVocabularies in AWS SDK for .NET API Reference. CLI AWS CLI To list your custom vocabularies Actions 445 Amazon Transcribe Developer Guide The following list-vocabularies example lists the custom vocabularies associated with your AWS account and Region. aws transcribe list-vocabularies Output: { "NextToken": "NextToken", "Vocabularies": [ { "VocabularyName": "ards-test-1", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-27T22:00:27.330000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "sample-test", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T23:04:11.044000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "CRLF-to-LF-test-3-1", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T22:12:22.277000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "CRLF-to-LF-test-2", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T21:53:50.455000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "CRLF-to-LF-1-1", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T21:39:33.356000+00:00", "VocabularyState": "READY" } ] } Actions 446 Amazon Transcribe Developer Guide For more information, see Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see ListVocabularies in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def list_vocabularies(vocabulary_filter, transcribe_client): """ Lists the
transcribe-dg-091	transcribe-dg.pdf	91	"LastModifiedTime": "2020-04-24T23:04:11.044000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "CRLF-to-LF-test-3-1", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T22:12:22.277000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "CRLF-to-LF-test-2", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T21:53:50.455000+00:00", "VocabularyState": "READY" }, { "VocabularyName": "CRLF-to-LF-1-1", "LanguageCode": "language-code", "LastModifiedTime": "2020-04-24T21:39:33.356000+00:00", "VocabularyState": "READY" } ] } Actions 446 Amazon Transcribe Developer Guide For more information, see Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see ListVocabularies in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def list_vocabularies(vocabulary_filter, transcribe_client): """ Lists the custom vocabularies created for this AWS account. :param vocabulary_filter: The returned vocabularies must contain this string in their names. :param transcribe_client: The Boto3 Transcribe client. :return: The list of retrieved vocabularies. """ try: response = transcribe_client.list_vocabularies(NameContains=vocabulary_filter) vocabs = response["Vocabularies"] next_token = response.get("NextToken") while next_token is not None: response = transcribe_client.list_vocabularies( NameContains=vocabulary_filter, NextToken=next_token ) vocabs += response["Vocabularies"] next_token = response.get("NextToken") logger.info( "Got %s vocabularies with filter %s.", len(vocabs), vocabulary_filter ) except ClientError: logger.exception( "Couldn't list vocabularies with filter %s.", vocabulary_filter ) Actions 447 Amazon Transcribe Developer Guide raise else: return vocabs • For API details, see ListVocabularies in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use StartMedicalTranscriptionJob with an AWS SDK or CLI The following code examples show how to use StartMedicalTranscriptionJob. .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Start a medical transcription job for a media file. This method returns /// as soon as the job is started. /// </summary> /// <param name="jobName">A unique name for the medical transcription job.</ param> /// <param name="mediaFileUri">The URI of the media file, typically an Amazon S3 location.</param> /// <param name="mediaFormat">The format of the media file.</param> /// <param name="outputBucketName">Location for the output, typically an Amazon S3 location.</param> /// <param name="transcriptionType">Conversation or dictation transcription type.</param> Actions 448 Amazon Transcribe Developer Guide /// <returns>A MedicalTransactionJob instance with information on the new job.</returns> public async Task<MedicalTranscriptionJob> StartMedicalTranscriptionJob( string jobName, string mediaFileUri, MediaFormat mediaFormat, string outputBucketName, Amazon.TranscribeService.Type transcriptionType) { var response = await _amazonTranscribeService.StartMedicalTranscriptionJobAsync( new StartMedicalTranscriptionJobRequest() { MedicalTranscriptionJobName = jobName, Media = new Media() { MediaFileUri = mediaFileUri }, MediaFormat = mediaFormat, LanguageCode = LanguageCode .EnUS, // The value must be en-US for medical transcriptions. OutputBucketName = outputBucketName, OutputKey = jobName, // The value is a key used to fetch the output of the transcription. Specialty = Specialty.PRIMARYCARE, // The value PRIMARYCARE must be set. Type = transcriptionType }); return response.MedicalTranscriptionJob; } • For API details, see StartMedicalTranscriptionJob in AWS SDK for .NET API Reference. CLI AWS CLI Example 1: To transcribe a medical dictation stored as an audio ﬁle Actions 449 Amazon Transcribe Developer Guide The following start-medical-transcription-job example transcribes an audio ﬁle. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://myfile.json Contents of myfile.json: { "MedicalTranscriptionJobName": "simple-dictation-medical-transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "DICTATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "simple-dictation-medical-transcription- job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-20T00:35:22.256000+00:00", "CreationTime": "2020-09-20T00:35:22.218000+00:00", "Specialty": "PRIMARYCARE", "Type": "DICTATION" } } For more information, see Batch Transcription Overview in the Amazon Transcribe Developer Guide. Example 2: To transcribe a clinician-patient dialogue stored as an audio ﬁle Actions 450 Amazon Transcribe Developer Guide The following start-medical-transcription-job example transcribes an audio ﬁle containing a clinician-patient dialogue. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://mysecondfile.json Contents of mysecondfile.json: { "MedicalTranscriptionJobName": "simple-dictation-medical-transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "simple-conversation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-20T23:19:49.965000+00:00", "CreationTime": "2020-09-20T23:19:49.941000+00:00", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } } For more information, see Batch Transcription Overview in the Amazon Transcribe Developer Guide. Example 3: To transcribe a multichannel audio ﬁle of a clinician-patient dialogue Actions 451 Amazon Transcribe Developer Guide The following start-medical-transcription-job example transcribes the audio from each channel in the audio ﬁle and merges the separate transcriptions from each channel into a single transcription output. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://mythirdfile.json Contents of mythirdfile.json: { "MedicalTranscriptionJobName": "multichannel-conversation-medical- transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "Settings":{ "ChannelIdentification": true } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "multichannel-conversation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-20T23:46:44.081000+00:00", "CreationTime": "2020-09-20T23:46:44.053000+00:00", "Settings": { "ChannelIdentification": true }, "Specialty": "PRIMARYCARE", Actions
transcribe-dg-092	transcribe-dg.pdf	92	Transcribe Developer Guide The following start-medical-transcription-job example transcribes the audio from each channel in the audio ﬁle and merges the separate transcriptions from each channel into a single transcription output. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://mythirdfile.json Contents of mythirdfile.json: { "MedicalTranscriptionJobName": "multichannel-conversation-medical- transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "Settings":{ "ChannelIdentification": true } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "multichannel-conversation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-20T23:46:44.081000+00:00", "CreationTime": "2020-09-20T23:46:44.053000+00:00", "Settings": { "ChannelIdentification": true }, "Specialty": "PRIMARYCARE", Actions 452 Amazon Transcribe Developer Guide "Type": "CONVERSATION" } } For more information, see Channel Identiﬁcation in the Amazon Transcribe Developer Guide. Example 4: To transcribe an audio ﬁle of a clinician-patient dialogue and identify the speakers in the transcription output The following start-medical-transcription-job example transcribes an audio ﬁle and labels the speech of each speaker in the transcription output. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://myfourthfile.json Contents of myfourthfile.json: { "MedicalTranscriptionJobName": "speaker-id-conversation-medical- transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "Settings":{ "ShowSpeakerLabels": true, "MaxSpeakerLabels": 2 } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "speaker-id-conversation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", Actions 453 Amazon Transcribe Developer Guide "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-21T18:43:37.265000+00:00", "CreationTime": "2020-09-21T18:43:37.157000+00:00", "Settings": { "ShowSpeakerLabels": true, "MaxSpeakerLabels": 2 }, "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } } For more information, see Identifying Speakers in the Amazon Transcribe Developer Guide. Example 5: To transcribe a medical conversation stored as an audio ﬁle with up to two transcription alternatives The following start-medical-transcription-job example creates up to two alternative transcriptions from a single audio ﬁle. Every transcriptions has a level of conﬁdence associated with it. By default, Amazon Transcribe returns the transcription with the highest conﬁdence level. You can specify that Amazon Transcribe return additional transcriptions with lower conﬁdence levels. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://myfifthfile.json Contents of myfifthfile.json: { "MedicalTranscriptionJobName": "alternatives-conversation-medical- transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" Actions 454 Developer Guide Amazon Transcribe }, "Settings":{ "ShowAlternatives": true, "MaxAlternatives": 2 } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "alternatives-conversation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-21T19:09:18.199000+00:00", "CreationTime": "2020-09-21T19:09:18.171000+00:00", "Settings": { "ShowAlternatives": true, "MaxAlternatives": 2 }, "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } } For more information, see Alternative Transcriptions in the Amazon Transcribe Developer Guide. Example 6: To transcribe an audio ﬁle of a medical dictation with up to two alternative transcriptions The following start-medical-transcription-job example transcribes an audio ﬁle and uses a vocabulary ﬁlter to mask any unwanted words. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-medical-transcription-job \ --cli-input-json file://mysixthfile.json Actions 455 Amazon Transcribe Developer Guide Contents of mysixthfile.json: { "MedicalTranscriptionJobName": "alternatives-conversation-medical- transcription-job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "DICTATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "Settings":{ "ShowAlternatives": true, "MaxAlternatives": 2 } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "alternatives-dictation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-21T21:01:14.592000+00:00", "CreationTime": "2020-09-21T21:01:14.569000+00:00", "Settings": { "ShowAlternatives": true, "MaxAlternatives": 2 }, "Specialty": "PRIMARYCARE", "Type": "DICTATION" } } For more information, see Alternative Transcriptions in the Amazon Transcribe Developer Guide. Actions 456 Amazon Transcribe Developer Guide Example 7: To transcribe an audio ﬁle of a medical dictation with increased accuracy by using a custom vocabulary The following start-medical-transcription-job example transcribes an audio ﬁle and uses a medical custom vocabulary you've previously created to increase the transcription accuracy. You specify the location of the transcription output in the OutputBucketName parameter. aws transcribe start-transcription-job \ --cli-input-json file://myseventhfile.json Contents of mysixthfile.json: { "MedicalTranscriptionJobName": "vocabulary-dictation-medical-transcription- job", "LanguageCode": "language-code", "Specialty": "PRIMARYCARE", "Type": "DICTATION", "OutputBucketName":"amzn-s3-demo-bucket", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "Settings":{ "VocabularyName": "cli-medical-vocab-1" } } Output: { "MedicalTranscriptionJob": { "MedicalTranscriptionJobName": "vocabulary-dictation-medical- transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.extension" }, "StartTime": "2020-09-21T21:17:27.045000+00:00", "CreationTime": "2020-09-21T21:17:27.016000+00:00", "Settings": { Actions 457 Amazon Transcribe Developer Guide "VocabularyName": "cli-medical-vocab-1" }, "Specialty": "PRIMARYCARE", "Type": "DICTATION" } } For more information, see Medical Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see StartMedicalTranscriptionJob in AWS CLI Command Reference. JavaScript SDK for JavaScript (v3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; Start a medical transcription job. // Import the required AWS SDK clients and commands for Node.js import { StartMedicalTranscriptionJobCommand } from "@aws-sdk/client-transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params
transcribe-dg-093	transcribe-dg.pdf	93	JavaScript (v3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; Start a medical transcription job. // Import the required AWS SDK clients and commands for Node.js import { StartMedicalTranscriptionJobCommand } from "@aws-sdk/client-transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params = { MedicalTranscriptionJobName: "MEDICAL_JOB_NAME", // Required Actions 458 Amazon Transcribe Developer Guide OutputBucketName: "OUTPUT_BUCKET_NAME", // Required Specialty: "PRIMARYCARE", // Required. Possible values are 'PRIMARYCARE' Type: "JOB_TYPE", // Required. Possible values are 'CONVERSATION' and 'DICTATION' LanguageCode: "LANGUAGE_CODE", // For example, 'en-US' MediaFormat: "SOURCE_FILE_FORMAT", // For example, 'wav' Media: { MediaFileUri: "SOURCE_FILE_LOCATION", // The S3 object location of the input media file. The URI must be in the same region // as the API endpoint that you are calling.For example, // "https://transcribe-demo.s3-REGION.amazonaws.com/hello_world.wav" }, }; export const run = async () => { try { const data = await transcribeClient.send( new StartMedicalTranscriptionJobCommand(params), ); console.log("Success - put", data); return data; // For unit tests. } catch (err) { console.log("Error", err); } }; run(); • For more information, see AWS SDK for JavaScript Developer Guide. • For API details, see StartMedicalTranscriptionJob in AWS SDK for JavaScript API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use StartTranscriptionJob with an AWS SDK or CLI The following code examples show how to use StartTranscriptionJob. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code examples: Actions 459 Amazon Transcribe Developer Guide • Create and reﬁne a custom vocabulary • Transcribe audio and get job data .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Start a transcription job for a media file. This method returns /// as soon as the job is started. /// </summary> /// <param name="jobName">A unique name for the transcription job.</param> /// <param name="mediaFileUri">The URI of the media file, typically an Amazon S3 location.</param> /// <param name="mediaFormat">The format of the media file.</param> /// <param name="languageCode">The language code of the media file, such as en-US.</param> /// <param name="vocabularyName">Optional name of a custom vocabulary.</ param> /// <returns>A TranscriptionJob instance with information on the new job.</ returns> public async Task<TranscriptionJob> StartTranscriptionJob(string jobName, string mediaFileUri, MediaFormat mediaFormat, LanguageCode languageCode, string? vocabularyName) { var response = await _amazonTranscribeService.StartTranscriptionJobAsync( new StartTranscriptionJobRequest() { TranscriptionJobName = jobName, Media = new Media() { MediaFileUri = mediaFileUri }, Actions 460 Amazon Transcribe Developer Guide MediaFormat = mediaFormat, LanguageCode = languageCode, Settings = vocabularyName != null ? new Settings() { VocabularyName = vocabularyName } : null }); return response.TranscriptionJob; } • For API details, see StartTranscriptionJob in AWS SDK for .NET API Reference. CLI AWS CLI Example 1: To transcribe an audio ﬁle The following start-transcription-job example transcribes your audio ﬁle. aws transcribe start-transcription-job \ --cli-input-json file://myfile.json Contents of myfile.json: { "TranscriptionJobName": "cli-simple-transcription-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" } } For more information, see Getting Started (AWS Command Line Interface) in the Amazon Transcribe Developer Guide. Example 2: To transcribe a multi-channel audio ﬁle Actions 461 Amazon Transcribe Developer Guide The following start-transcription-job example transcribes your multi-channel audio ﬁle. aws transcribe start-transcription-job \ --cli-input-json file://mysecondfile.json Contents of mysecondfile.json: { "TranscriptionJobName": "cli-channelid-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" }, "Settings":{ "ChannelIdentification":true } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-channelid-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your- media-file-name.file-extension" }, "StartTime": "2020-09-17T16:07:56.817000+00:00", "CreationTime": "2020-09-17T16:07:56.784000+00:00", "Settings": { "ChannelIdentification": true } } } For more information, see Transcribing Multi-Channel Audio in the Amazon Transcribe Developer Guide. Actions 462 Amazon Transcribe Developer Guide Example 3: To transcribe an audio ﬁle and identify the diﬀerent speakers The following start-transcription-job example transcribes your audio ﬁle and identiﬁes the speakers in the transcription output. aws transcribe start-transcription-job \ --cli-input-json file://mythirdfile.json Contents of mythirdfile.json: { "TranscriptionJobName": "cli-speakerid-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" }, "Settings":{ "ShowSpeakerLabels": true, "MaxSpeakerLabels": 2 } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-speakerid-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your- media-file-name.file-extension" }, "StartTime": "2020-09-17T16:22:59.696000+00:00", "CreationTime": "2020-09-17T16:22:59.676000+00:00", "Settings": { "ShowSpeakerLabels": true, "MaxSpeakerLabels": 2 } } } Actions 463 Amazon Transcribe Developer Guide For more information, see Identifying Speakers in the Amazon Transcribe Developer Guide. Example 4: To transcribe an audio ﬁle and mask any unwanted words in the transcription output The following start-transcription-job
transcribe-dg-094	transcribe-dg.pdf	94	identiﬁes the speakers in the transcription output. aws transcribe start-transcription-job \ --cli-input-json file://mythirdfile.json Contents of mythirdfile.json: { "TranscriptionJobName": "cli-speakerid-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" }, "Settings":{ "ShowSpeakerLabels": true, "MaxSpeakerLabels": 2 } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-speakerid-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your- media-file-name.file-extension" }, "StartTime": "2020-09-17T16:22:59.696000+00:00", "CreationTime": "2020-09-17T16:22:59.676000+00:00", "Settings": { "ShowSpeakerLabels": true, "MaxSpeakerLabels": 2 } } } Actions 463 Amazon Transcribe Developer Guide For more information, see Identifying Speakers in the Amazon Transcribe Developer Guide. Example 4: To transcribe an audio ﬁle and mask any unwanted words in the transcription output The following start-transcription-job example transcribes your audio ﬁle and uses a vocabulary ﬁlter you've previously created to mask any unwanted words. aws transcribe start-transcription-job \ --cli-input-json file://myfourthfile.json Contents of myfourthfile.json: { "TranscriptionJobName": "cli-filter-mask-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" }, "Settings":{ "VocabularyFilterName": "your-vocabulary-filter", "VocabularyFilterMethod": "mask" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-filter-mask-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://Amazon-S3-Prefix/your-media-file.file- extension" }, "StartTime": "2020-09-18T16:36:18.568000+00:00", "CreationTime": "2020-09-18T16:36:18.547000+00:00", "Settings": { "VocabularyFilterName": "your-vocabulary-filter", "VocabularyFilterMethod": "mask" Actions 464 Amazon Transcribe Developer Guide } } } For more information, see Filtering Transcriptions in the Amazon Transcribe Developer Guide. Example 5: To transcribe an audio ﬁle and remove any unwanted words in the transcription output The following start-transcription-job example transcribes your audio ﬁle and uses a vocabulary ﬁlter you've previously created to mask any unwanted words. aws transcribe start-transcription-job \ --cli-input-json file://myfifthfile.json Contents of myfifthfile.json: { "TranscriptionJobName": "cli-filter-remove-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" }, "Settings":{ "VocabularyFilterName": "your-vocabulary-filter", "VocabularyFilterMethod": "remove" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-filter-remove-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your- media-file-name.file-extension" }, "StartTime": "2020-09-18T16:36:18.568000+00:00", Actions 465 Amazon Transcribe Developer Guide "CreationTime": "2020-09-18T16:36:18.547000+00:00", "Settings": { "VocabularyFilterName": "your-vocabulary-filter", "VocabularyFilterMethod": "remove" } } } For more information, see Filtering Transcriptions in the Amazon Transcribe Developer Guide. Example 6: To transcribe an audio ﬁle with increased accuracy using a custom vocabulary The following start-transcription-job example transcribes your audio ﬁle and uses a vocabulary ﬁlter you've previously created to mask any unwanted words. aws transcribe start-transcription-job \ --cli-input-json file://mysixthfile.json Contents of mysixthfile.json: { "TranscriptionJobName": "cli-vocab-job", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" }, "Settings":{ "VocabularyName": "your-vocabulary" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-vocab-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "the-language-of-your-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your- media-file-name.file-extension" }, Actions 466 Amazon Transcribe Developer Guide "StartTime": "2020-09-18T16:36:18.568000+00:00", "CreationTime": "2020-09-18T16:36:18.547000+00:00", "Settings": { "VocabularyName": "your-vocabulary" } } } For more information, see Filtering Transcriptions in the Amazon Transcribe Developer Guide. Example 7: To identify the language of an audio ﬁle and transcribe it The following start-transcription-job example transcribes your audio ﬁle and uses a vocabulary ﬁlter you've previously created to mask any unwanted words. aws transcribe start-transcription-job \ --cli-input-json file://myseventhfile.json Contents of myseventhfile.json: { "TranscriptionJobName": "cli-identify-language-transcription-job", "IdentifyLanguage": true, "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your-media- file-name.file-extension" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-identify-language-transcription-job", "TranscriptionJobStatus": "IN_PROGRESS", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/Amazon-S3-prefix/your- media-file-name.file-extension" }, "StartTime": "2020-09-18T22:27:23.970000+00:00", "CreationTime": "2020-09-18T22:27:23.948000+00:00", "IdentifyLanguage": true } Actions 467 Amazon Transcribe } Developer Guide For more information, see Identifying the Language in the Amazon Transcribe Developer Guide. Example 8: To transcribe an audio ﬁle with personally identiﬁable information redacted The following start-transcription-job example transcribes your audio ﬁle and redacts any personally identiﬁable information in the transcription output. aws transcribe start-transcription-job \ --cli-input-json file://myeighthfile.json Contents of myeigthfile.json: { "TranscriptionJobName": "cli-redaction-job", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://Amazon-S3-Prefix/your-media-file.file-extension" }, "ContentRedaction": { "RedactionOutput":"redacted", "RedactionType":"PII" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-redaction-job", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://Amazon-S3-Prefix/your-media-file.file- extension" }, "StartTime": "2020-09-25T23:49:13.195000+00:00", "CreationTime": "2020-09-25T23:49:13.176000+00:00", "ContentRedaction": { "RedactionType": "PII", Actions 468 Amazon Transcribe Developer Guide "RedactionOutput": "redacted" } } } For more information, see Automatic Content Redaction in the Amazon Transcribe Developer Guide. Example 9: To generate a transcript with personally identiﬁable information (PII) redacted and an unredacted transcript The following start-transcription-job example generates two transcrptions of your audio ﬁle, one with the personally identiﬁable information redacted, and the other without any redactions. aws transcribe start-transcription-job \ --cli-input-json file://myninthfile.json Contents of myninthfile.json: { "TranscriptionJobName": "cli-redaction-job-with-unredacted-transcript", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://Amazon-S3-Prefix/your-media-file.file-extension" }, "ContentRedaction": { "RedactionOutput":"redacted_and_unredacted", "RedactionType":"PII" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-redaction-job-with-unredacted-transcript", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://Amazon-S3-Prefix/your-media-file.file- extension" Actions 469 Amazon Transcribe }, Developer Guide "StartTime": "2020-09-25T23:59:47.677000+00:00", "CreationTime": "2020-09-25T23:59:47.653000+00:00", "ContentRedaction": { "RedactionType": "PII", "RedactionOutput": "redacted_and_unredacted" } } } For more information, see Automatic Content Redaction in the Amazon Transcribe Developer Guide. Example 10: To use a custom language model you've previously created to transcribe an audio ﬁle. The following start-transcription-job example transcribes your audio ﬁle with a custom language model you've previously created. aws transcribe start-transcription-job \ --cli-input-json file://mytenthfile.json Contents of mytenthfile.json: { "TranscriptionJobName": "cli-clm-2-job-1", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.file-extension" }, "ModelSettings": { "LanguageModelName":"cli-clm-2" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-clm-2-job-1", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", Actions 470 Amazon Transcribe "Media": { Developer Guide "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.file- extension" }, "StartTime": "2020-09-28T17:56:01.835000+00:00",
transcribe-dg-095	transcribe-dg.pdf	95	"RedactionType": "PII", "RedactionOutput": "redacted_and_unredacted" } } } For more information, see Automatic Content Redaction in the Amazon Transcribe Developer Guide. Example 10: To use a custom language model you've previously created to transcribe an audio ﬁle. The following start-transcription-job example transcribes your audio ﬁle with a custom language model you've previously created. aws transcribe start-transcription-job \ --cli-input-json file://mytenthfile.json Contents of mytenthfile.json: { "TranscriptionJobName": "cli-clm-2-job-1", "LanguageCode": "language-code", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.file-extension" }, "ModelSettings": { "LanguageModelName":"cli-clm-2" } } Output: { "TranscriptionJob": { "TranscriptionJobName": "cli-clm-2-job-1", "TranscriptionJobStatus": "IN_PROGRESS", "LanguageCode": "language-code", Actions 470 Amazon Transcribe "Media": { Developer Guide "MediaFileUri": "s3://amzn-s3-demo-bucket/your-audio-file.file- extension" }, "StartTime": "2020-09-28T17:56:01.835000+00:00", "CreationTime": "2020-09-28T17:56:01.801000+00:00", "ModelSettings": { "LanguageModelName": "cli-clm-2" } } } For more information, see Improving Domain-Speciﬁc Transcription Accuracy with Custom Language Models in the Amazon Transcribe Developer Guide. • For API details, see StartTranscriptionJob in AWS CLI Command Reference. JavaScript SDK for JavaScript (v3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Start a transcription job. // Import the required AWS SDK clients and commands for Node.js import { StartTranscriptionJobCommand } from "@aws-sdk/client-transcribe"; import { transcribeClient } from "./libs/transcribeClient.js"; // Set the parameters export const params = { TranscriptionJobName: "JOB_NAME", LanguageCode: "LANGUAGE_CODE", // For example, 'en-US' MediaFormat: "SOURCE_FILE_FORMAT", // For example, 'wav' Media: { MediaFileUri: "SOURCE_LOCATION", // For example, "https://transcribe-demo.s3-REGION.amazonaws.com/ hello_world.wav" Actions 471 Amazon Transcribe }, Developer Guide OutputBucketName: "OUTPUT_BUCKET_NAME", }; export const run = async () => { try { const data = await transcribeClient.send( new StartTranscriptionJobCommand(params), ); console.log("Success - put", data); return data; // For unit tests. } catch (err) { console.log("Error", err); } }; run(); Create the client. import { TranscribeClient } from "@aws-sdk/client-transcribe"; // Set the AWS Region. const REGION = "REGION"; //e.g. "us-east-1" // Create an Amazon Transcribe service client object. const transcribeClient = new TranscribeClient({ region: REGION }); export { transcribeClient }; • For more information, see AWS SDK for JavaScript Developer Guide. • For API details, see StartTranscriptionJob in AWS SDK for JavaScript API Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Actions 472 Amazon Transcribe Developer Guide def start_job( job_name, media_uri, media_format, language_code, transcribe_client, vocabulary_name=None, ): """ Starts a transcription job. This function returns as soon as the job is started. To get the current status of the job, call get_transcription_job. The job is successfully completed when the job status is 'COMPLETED'. :param job_name: The name of the transcription job. This must be unique for your AWS account. :param media_uri: The URI where the audio file is stored. This is typically in an Amazon S3 bucket. :param media_format: The format of the audio file. For example, mp3 or wav. :param language_code: The language code of the audio file. For example, en-US or ja-JP :param transcribe_client: The Boto3 Transcribe client. :param vocabulary_name: The name of a custom vocabulary to use when transcribing the audio file. :return: Data about the job. """ try: job_args = { "TranscriptionJobName": job_name, "Media": {"MediaFileUri": media_uri}, "MediaFormat": media_format, "LanguageCode": language_code, } if vocabulary_name is not None: job_args["Settings"] = {"VocabularyName": vocabulary_name} response = transcribe_client.start_transcription_job(**job_args) job = response["TranscriptionJob"] logger.info("Started transcription job %s.", job_name) except ClientError: logger.exception("Couldn't start transcription job %s.", job_name) raise else: Actions 473 Amazon Transcribe return job Developer Guide • For API details, see StartTranscriptionJob in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use UpdateVocabulary with an AWS SDK or CLI The following code examples show how to use UpdateVocabulary. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: • Create and reﬁne a custom vocabulary .NET SDK for .NET Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. /// <summary> /// Update a custom vocabulary with new values. Update overwrites all existing information. /// </summary> /// <param name="languageCode">The language code of the vocabulary.</param> /// <param name="phrases">Phrases to use in the vocabulary.</param> /// <param name="vocabularyName">Name for the vocabulary.</param> /// <returns>The state of the custom vocabulary.</returns> Actions 474 Amazon Transcribe Developer Guide public async Task<VocabularyState> UpdateCustomVocabulary(LanguageCode languageCode, List<string> phrases, string vocabularyName) { var response = await _amazonTranscribeService.UpdateVocabularyAsync( new UpdateVocabularyRequest() { LanguageCode = languageCode, Phrases = phrases, VocabularyName = vocabularyName }); return response.VocabularyState; } • For API details, see UpdateVocabulary in AWS SDK for .NET API Reference. CLI AWS CLI To update a custom vocabulary with new terms. The following update-vocabulary example overwrites the terms used to create a custom vocabulary
transcribe-dg-096	transcribe-dg.pdf	96	language code of the vocabulary.</param> /// <param name="phrases">Phrases to use in the vocabulary.</param> /// <param name="vocabularyName">Name for the vocabulary.</param> /// <returns>The state of the custom vocabulary.</returns> Actions 474 Amazon Transcribe Developer Guide public async Task<VocabularyState> UpdateCustomVocabulary(LanguageCode languageCode, List<string> phrases, string vocabularyName) { var response = await _amazonTranscribeService.UpdateVocabularyAsync( new UpdateVocabularyRequest() { LanguageCode = languageCode, Phrases = phrases, VocabularyName = vocabularyName }); return response.VocabularyState; } • For API details, see UpdateVocabulary in AWS SDK for .NET API Reference. CLI AWS CLI To update a custom vocabulary with new terms. The following update-vocabulary example overwrites the terms used to create a custom vocabulary with the new ones that you provide. Prerequisite: to replace the terms in a custom vocabulary, you need a ﬁle with new terms. aws transcribe update-vocabulary \ --vocabulary-file-uri s3://amzn-s3-demo-bucket/Amazon-S3-Prefix/custom- vocabulary.txt \ --vocabulary-name custom-vocabulary \ --language-code language-code Output: { "VocabularyName": "custom-vocabulary", "LanguageCode": "language", "VocabularyState": "PENDING" } Actions 475 Amazon Transcribe Developer Guide For more information, see Custom Vocabularies in the Amazon Transcribe Developer Guide. • For API details, see UpdateVocabulary in AWS CLI Command Reference. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. def update_vocabulary( vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None ): """ Updates an existing custom vocabulary. The entire vocabulary is replaced with the contents of the update. :param vocabulary_name: The name of the vocabulary to update. :param language_code: The language code of the vocabulary. :param transcribe_client: The Boto3 Transcribe client. :param phrases: A list of comma-separated phrases to include in the vocabulary. :param table_uri: A table of phrases and pronunciation hints to include in the vocabulary. """ try: vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code} if phrases is not None: vocab_args["Phrases"] = phrases elif table_uri is not None: vocab_args["VocabularyFileUri"] = table_uri response = transcribe_client.update_vocabulary(**vocab_args) logger.info("Updated custom vocabulary %s.", response["VocabularyName"]) except ClientError: Actions 476 Amazon Transcribe Developer Guide logger.exception("Couldn't update custom vocabulary %s.", vocabulary_name) raise • For API details, see UpdateVocabulary in AWS SDK for Python (Boto3) API Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Scenarios for Amazon Transcribe using AWS SDKs The following code examples show you how to implement common scenarios in Amazon Transcribe with AWS SDKs. These scenarios show you how to accomplish speciﬁc tasks by calling multiple functions within Amazon Transcribe or combined with other AWS services. Each scenario includes a link to the complete source code, where you can ﬁnd instructions on how to set up and run the code. Scenarios target an intermediate level of experience to help you understand service actions in context. Examples • Build an Amazon Transcribe streaming app • Convert text to speech and back to text using an AWS SDK • Create and reﬁne an Amazon Transcribe custom vocabulary using an AWS SDK • Transcribe audio and get job data with Amazon Transcribe using an AWS SDK Build an Amazon Transcribe streaming app The following code example shows how to build an app that records, transcribes, and translates live audio in real-time, and emails the results. Scenarios 477 Amazon Transcribe JavaScript SDK for JavaScript (v3) Developer Guide Shows how to use Amazon Transcribe to build an app that records, transcribes, and translates live audio in real-time, and emails the results using Amazon Simple Email Service (Amazon SES). For complete source code and instructions on how to set up and run, see the full example on GitHub. Services used in this example • Amazon Comprehend • Amazon SES • Amazon Transcribe • Amazon Translate For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Convert text to speech and back to text using an AWS SDK The following code example shows how to: • Use Amazon Polly to synthesize a plain text (UTF-8) input ﬁle to an audio ﬁle. • Upload the audio ﬁle to an Amazon S3 bucket. • Use Amazon Transcribe to convert the audio ﬁle to text. • Display the text. Rust SDK for Rust Use Amazon Polly to synthesize a plain text (UTF-8) input ﬁle to an audio ﬁle, upload the audio ﬁle to an Amazon S3 bucket, use Amazon Transcribe to convert that audio ﬁle to text, and display the text. Convert text to speech and back to text 478 Amazon Transcribe Developer Guide For complete source code and instructions on how to set up and run, see the full example on GitHub. Services used in this example • Amazon Polly • Amazon S3 • Amazon Transcribe For a complete list of AWS SDK developer
transcribe-dg-097	transcribe-dg.pdf	97	• Display the text. Rust SDK for Rust Use Amazon Polly to synthesize a plain text (UTF-8) input ﬁle to an audio ﬁle, upload the audio ﬁle to an Amazon S3 bucket, use Amazon Transcribe to convert that audio ﬁle to text, and display the text. Convert text to speech and back to text 478 Amazon Transcribe Developer Guide For complete source code and instructions on how to set up and run, see the full example on GitHub. Services used in this example • Amazon Polly • Amazon S3 • Amazon Transcribe For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Create and reﬁne an Amazon Transcribe custom vocabulary using an AWS SDK The following code example shows how to: • Upload an audio ﬁle to Amazon S3. • Run an Amazon Transcribe job to transcribe the ﬁle and get the results. • Create and reﬁne a custom vocabulary to improve transcription accuracy. • Run jobs with custom vocabularies and get the results. Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Transcribe an audio ﬁle that contains a reading of Jabberwocky by Lewis Carroll. Start by creating functions that wrap Amazon Transcribe actions. def start_job( job_name, Create and reﬁne a custom vocabulary 479 Amazon Transcribe Developer Guide media_uri, media_format, language_code, transcribe_client, vocabulary_name=None, ): """ Starts a transcription job. This function returns as soon as the job is started. To get the current status of the job, call get_transcription_job. The job is successfully completed when the job status is 'COMPLETED'. :param job_name: The name of the transcription job. This must be unique for your AWS account. :param media_uri: The URI where the audio file is stored. This is typically in an Amazon S3 bucket. :param media_format: The format of the audio file. For example, mp3 or wav. :param language_code: The language code of the audio file. For example, en-US or ja-JP :param transcribe_client: The Boto3 Transcribe client. :param vocabulary_name: The name of a custom vocabulary to use when transcribing the audio file. :return: Data about the job. """ try: job_args = { "TranscriptionJobName": job_name, "Media": {"MediaFileUri": media_uri}, "MediaFormat": media_format, "LanguageCode": language_code, } if vocabulary_name is not None: job_args["Settings"] = {"VocabularyName": vocabulary_name} response = transcribe_client.start_transcription_job(job_args) job = response["TranscriptionJob"] logger.info("Started transcription job %s.", job_name) except ClientError: logger.exception("Couldn't start transcription job %s.", job_name) raise else: return job Create and reﬁne a custom vocabulary 480 Amazon Transcribe Developer Guide def get_job(job_name, transcribe_client): """ Gets details about a transcription job. :param job_name: The name of the job to retrieve. :param transcribe_client: The Boto3 Transcribe client. :return: The retrieved transcription job. """ try: response = transcribe_client.get_transcription_job( TranscriptionJobName=job_name ) job = response["TranscriptionJob"] logger.info("Got job %s.", job["TranscriptionJobName"]) except ClientError: logger.exception("Couldn't get job %s.", job_name) raise else: return job def delete_job(job_name, transcribe_client): """ Deletes a transcription job. This also deletes the transcript associated with the job. :param job_name: The name of the job to delete. :param transcribe_client: The Boto3 Transcribe client. """ try: transcribe_client.delete_transcription_job(TranscriptionJobName=job_name) logger.info("Deleted job %s.", job_name) except ClientError: logger.exception("Couldn't delete job %s.", job_name) raise def create_vocabulary( vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None ): Create and reﬁne a custom vocabulary 481 Amazon Transcribe """ Developer Guide Creates a custom vocabulary that can be used to improve the accuracy of transcription jobs. This function returns as soon as the vocabulary processing is started. Call get_vocabulary to get the current status of the vocabulary. The vocabulary is ready to use when its status is 'READY'. :param vocabulary_name: The name of the custom vocabulary. :param language_code: The language code of the vocabulary. For example, en-US or nl-NL. :param transcribe_client: The Boto3 Transcribe client. :param phrases: A list of comma-separated phrases to include in the vocabulary. :param table_uri: A table of phrases and pronunciation hints to include in the vocabulary. :return: Information about the newly created vocabulary. """ try: vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code} if phrases is not None: vocab_args["Phrases"] = phrases elif table_uri is not None: vocab_args["VocabularyFileUri"] = table_uri response = transcribe_client.create_vocabulary(vocab_args) logger.info("Created custom vocabulary %s.", response["VocabularyName"]) except ClientError: logger.exception("Couldn't create custom vocabulary %s.", vocabulary_name) raise else: return response def get_vocabulary(vocabulary_name, transcribe_client): """ Gets information about a custom vocabulary. :param vocabulary_name: The name of the vocabulary to retrieve. :param transcribe_client: The Boto3 Transcribe client. :return: Information about the vocabulary. """ Create and reﬁne a custom vocabulary 482 Amazon Transcribe try: Developer Guide response = transcribe_client.get_vocabulary(VocabularyName=vocabulary_name) logger.info("Got vocabulary %s.", response["VocabularyName"]) except ClientError: logger.exception("Couldn't get vocabulary %s.", vocabulary_name) raise else: return response def update_vocabulary( vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None ): """ Updates an existing custom vocabulary. The entire vocabulary is replaced with the contents of the update. :param vocabulary_name:
transcribe-dg-098	transcribe-dg.pdf	98	except ClientError: logger.exception("Couldn't create custom vocabulary %s.", vocabulary_name) raise else: return response def get_vocabulary(vocabulary_name, transcribe_client): """ Gets information about a custom vocabulary. :param vocabulary_name: The name of the vocabulary to retrieve. :param transcribe_client: The Boto3 Transcribe client. :return: Information about the vocabulary. """ Create and reﬁne a custom vocabulary 482 Amazon Transcribe try: Developer Guide response = transcribe_client.get_vocabulary(VocabularyName=vocabulary_name) logger.info("Got vocabulary %s.", response["VocabularyName"]) except ClientError: logger.exception("Couldn't get vocabulary %s.", vocabulary_name) raise else: return response def update_vocabulary( vocabulary_name, language_code, transcribe_client, phrases=None, table_uri=None ): """ Updates an existing custom vocabulary. The entire vocabulary is replaced with the contents of the update. :param vocabulary_name: The name of the vocabulary to update. :param language_code: The language code of the vocabulary. :param transcribe_client: The Boto3 Transcribe client. :param phrases: A list of comma-separated phrases to include in the vocabulary. :param table_uri: A table of phrases and pronunciation hints to include in the vocabulary. """ try: vocab_args = {"VocabularyName": vocabulary_name, "LanguageCode": language_code} if phrases is not None: vocab_args["Phrases"] = phrases elif table_uri is not None: vocab_args["VocabularyFileUri"] = table_uri response = transcribe_client.update_vocabulary(*vocab_args) logger.info("Updated custom vocabulary %s.", response["VocabularyName"]) except ClientError: logger.exception("Couldn't update custom vocabulary %s.", vocabulary_name) raise Create and reﬁne a custom vocabulary 483 Amazon Transcribe Developer Guide def list_vocabularies(vocabulary_filter, transcribe_client): """ Lists the custom vocabularies created for this AWS account. :param vocabulary_filter: The returned vocabularies must contain this string in their names. :param transcribe_client: The Boto3 Transcribe client. :return: The list of retrieved vocabularies. """ try: response = transcribe_client.list_vocabularies(NameContains=vocabulary_filter) vocabs = response["Vocabularies"] next_token = response.get("NextToken") while next_token is not None: response = transcribe_client.list_vocabularies( NameContains=vocabulary_filter, NextToken=next_token ) vocabs += response["Vocabularies"] next_token = response.get("NextToken") logger.info( "Got %s vocabularies with filter %s.", len(vocabs), vocabulary_filter ) except ClientError: logger.exception( "Couldn't list vocabularies with filter %s.", vocabulary_filter ) raise else: return vocabs def delete_vocabulary(vocabulary_name, transcribe_client): """ Deletes a custom vocabulary. :param vocabulary_name: The name of the vocabulary to delete. :param transcribe_client: The Boto3 Transcribe client. """ try: transcribe_client.delete_vocabulary(VocabularyName=vocabulary_name) Create and reﬁne a custom vocabulary 484 Amazon Transcribe Developer Guide logger.info("Deleted vocabulary %s.", vocabulary_name) except ClientError: logger.exception("Couldn't delete vocabulary %s.", vocabulary_name) raise Call the wrapper functions to transcribe audio without a custom vocabulary and then with diﬀerent versions of a custom vocabulary to see improved results. def usage_demo(): """Shows how to use the Amazon Transcribe service.""" logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") s3_resource = boto3.resource("s3") transcribe_client = boto3.client("transcribe") print("-" 88) print("Welcome to the Amazon Transcribe demo!") print("-" * 88) bucket_name = f"jabber-bucket-{time.time_ns()}" print(f"Creating bucket {bucket_name}.") bucket = s3_resource.create_bucket( Bucket=bucket_name, CreateBucketConfiguration={ "LocationConstraint": transcribe_client.meta.region_name }, ) media_file_name = ".media/Jabberwocky.mp3" media_object_key = "Jabberwocky.mp3" print(f"Uploading media file {media_file_name}.") bucket.upload_file(media_file_name, media_object_key) media_uri = f"s3://{bucket.name}/{media_object_key}" job_name_simple = f"Jabber-{time.time_ns()}" print(f"Starting transcription job {job_name_simple}.") start_job( job_name_simple, f"s3://{bucket_name}/{media_object_key}", "mp3", "en-US", Create and reﬁne a custom vocabulary 485 Amazon Transcribe Developer Guide transcribe_client, ) transcribe_waiter = TranscribeCompleteWaiter(transcribe_client) transcribe_waiter.wait(job_name_simple) job_simple = get_job(job_name_simple, transcribe_client) transcript_simple = requests.get( job_simple["Transcript"]["TranscriptFileUri"] ).json() print(f"Transcript for job {transcript_simple['jobName']}:") print(transcript_simple["results"]["transcripts"][0]["transcript"]) print("-" * 88) print( "Creating a custom vocabulary that lists the nonsense words to try to " "improve the transcription." ) vocabulary_name = f"Jabber-vocabulary-{time.time_ns()}" create_vocabulary( vocabulary_name, "en-US", transcribe_client, phrases=[ "brillig", "slithy", "borogoves", "mome", "raths", "Jub-Jub", "frumious", "manxome", "Tumtum", "uffish", "whiffling", "tulgey", "thou", "frabjous", "callooh", "callay", "chortled", ], ) vocabulary_ready_waiter = VocabularyReadyWaiter(transcribe_client) vocabulary_ready_waiter.wait(vocabulary_name) Create and reﬁne a custom vocabulary 486 Amazon Transcribe Developer Guide job_name_vocabulary_list = f"Jabber-vocabulary-list-{time.time_ns()}" print(f"Starting transcription job {job_name_vocabulary_list}.") start_job( job_name_vocabulary_list, media_uri, "mp3", "en-US", transcribe_client, vocabulary_name, ) transcribe_waiter.wait(job_name_vocabulary_list) job_vocabulary_list = get_job(job_name_vocabulary_list, transcribe_client) transcript_vocabulary_list = requests.get( job_vocabulary_list["Transcript"]["TranscriptFileUri"] ).json() print(f"Transcript for job {transcript_vocabulary_list['jobName']}:") print(transcript_vocabulary_list["results"]["transcripts"][0]["transcript"]) print("-" * 88) print( "Updating the custom vocabulary with table data that provides additional " "pronunciation hints." ) table_vocab_file = "jabber-vocabulary-table.txt" bucket.upload_file(table_vocab_file, table_vocab_file) update_vocabulary( vocabulary_name, "en-US", transcribe_client, table_uri=f"s3://{bucket.name}/{table_vocab_file}", ) vocabulary_ready_waiter.wait(vocabulary_name) job_name_vocab_table = f"Jabber-vocab-table-{time.time_ns()}" print(f"Starting transcription job {job_name_vocab_table}.") start_job( job_name_vocab_table, media_uri, "mp3", "en-US", transcribe_client, vocabulary_name=vocabulary_name, ) Create and reﬁne a custom vocabulary 487 Amazon Transcribe Developer Guide transcribe_waiter.wait(job_name_vocab_table) job_vocab_table = get_job(job_name_vocab_table, transcribe_client) transcript_vocab_table = requests.get( job_vocab_table["Transcript"]["TranscriptFileUri"] ).json() print(f"Transcript for job {transcript_vocab_table['jobName']}:") print(transcript_vocab_table["results"]["transcripts"][0]["transcript"]) print("-" * 88) print("Getting data for jobs and vocabularies.") jabber_jobs = list_jobs("Jabber", transcribe_client) print(f"Found {len(jabber_jobs)} jobs:") for job_sum in jabber_jobs: job = get_job(job_sum["TranscriptionJobName"], transcribe_client) print( f"\t{job['TranscriptionJobName']}, {job['Media']['MediaFileUri']}, " f"{job['Settings'].get('VocabularyName')}" ) jabber_vocabs = list_vocabularies("Jabber", transcribe_client) print(f"Found {len(jabber_vocabs)} vocabularies:") for vocab_sum in jabber_vocabs: vocab = get_vocabulary(vocab_sum["VocabularyName"], transcribe_client) vocab_content = requests.get(vocab["DownloadUri"]).text print(f"\t{vocab['VocabularyName']} contents:") print(vocab_content) print("-" * 88) print("Deleting demo jobs.") for job_name in [job_name_simple, job_name_vocabulary_list, job_name_vocab_table]: delete_job(job_name, transcribe_client) print("Deleting demo vocabulary.") delete_vocabulary(vocabulary_name, transcribe_client) print("Deleting demo bucket.") bucket.objects.delete() bucket.delete() print("Thanks for watching!") • For API details, see the following topics in AWS SDK for Python (Boto3) API Reference. Create and reﬁne a custom vocabulary 488 Amazon Transcribe Developer Guide • CreateVocabulary • DeleteTranscriptionJob • DeleteVocabulary • GetTranscriptionJob • GetVocabulary • ListVocabularies • StartTranscriptionJob • UpdateVocabulary For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Transcribe audio and get job data with Amazon Transcribe using an AWS SDK The following
transcribe-dg-099	transcribe-dg.pdf	99	print("Deleting demo bucket.") bucket.objects.delete() bucket.delete() print("Thanks for watching!") • For API details, see the following topics in AWS SDK for Python (Boto3) API Reference. Create and reﬁne a custom vocabulary 488 Amazon Transcribe Developer Guide • CreateVocabulary • DeleteTranscriptionJob • DeleteVocabulary • GetTranscriptionJob • GetVocabulary • ListVocabularies • StartTranscriptionJob • UpdateVocabulary For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Transcribe audio and get job data with Amazon Transcribe using an AWS SDK The following code examples show how to: • Start a transcription job with Amazon Transcribe. • Wait for the job to complete. • Get the URI where the transcript is stored. For more information, see Getting started with Amazon Transcribe. Java SDK for Java 2.x Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. Transcribes a PCM ﬁle. Transcribe audio and get job data 489 Amazon Transcribe Developer Guide /** * To run this AWS code example, ensure that you have set up your development * environment, including your AWS credentials. * * For information, see this documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get- started.html / public class TranscribeStreamingDemoFile { private static final Region REGION = Region.US_EAST_1; private static TranscribeStreamingAsyncClient client; public static void main(String args[]) throws ExecutionException, InterruptedException { final String USAGE = "\n" + "Usage:\n" + " <file> \n\n" + "Where:\n" + " file - the location of a PCM file to transcribe. In this example, ensure the PCM file is 16 hertz (Hz). \n"; if (args.length != 1) { System.out.println(USAGE); System.exit(1); } String file = args[0]; client = TranscribeStreamingAsyncClient.builder() .region(REGION) .build(); CompletableFuture<Void> result = client.startStreamTranscription(getRequest(16_000), new AudioStreamPublisher(getStreamFromFile(file)), getResponseHandler()); result.get(); client.close(); } Transcribe audio and get job data 490 Amazon Transcribe Developer Guide private static InputStream getStreamFromFile(String file) { try { File inputFile = new File(file); InputStream audioStream = new FileInputStream(inputFile); return audioStream; } catch (FileNotFoundException e) { throw new RuntimeException(e); } } private static StartStreamTranscriptionRequest getRequest(Integer mediaSampleRateHertz) { return StartStreamTranscriptionRequest.builder() .languageCode(LanguageCode.EN_US) .mediaEncoding(MediaEncoding.PCM) .mediaSampleRateHertz(mediaSampleRateHertz) .build(); } private static StartStreamTranscriptionResponseHandler getResponseHandler() { return StartStreamTranscriptionResponseHandler.builder() .onResponse(r -> { System.out.println("Received Initial response"); }) .onError(e -> { System.out.println(e.getMessage()); StringWriter sw = new StringWriter(); e.printStackTrace(new PrintWriter(sw)); System.out.println("Error Occurred: " + sw.toString()); }) .onComplete(() -> { System.out.println("=== All records stream successfully ==="); }) .subscriber(event -> { List<Result> results = ((TranscriptEvent) event).transcript().results(); if (results.size() > 0) { if (! results.get(0).alternatives().get(0).transcript().isEmpty()) { System.out.println(results.get(0).alternatives().get(0).transcript()); } Transcribe audio and get job data 491 Amazon Transcribe Developer Guide } }) .build(); } private static class AudioStreamPublisher implements Publisher<AudioStream> { private final InputStream inputStream; private static Subscription currentSubscription; private AudioStreamPublisher(InputStream inputStream) { this.inputStream = inputStream; } @Override public void subscribe(Subscriber<? super AudioStream> s) { if (this.currentSubscription == null) { this.currentSubscription = new SubscriptionImpl(s, inputStream); } else { this.currentSubscription.cancel(); this.currentSubscription = new SubscriptionImpl(s, inputStream); } s.onSubscribe(currentSubscription); } } public static class SubscriptionImpl implements Subscription { private static final int CHUNK_SIZE_IN_BYTES = 1024 1; private final Subscriber<? super AudioStream> subscriber; private final InputStream inputStream; private ExecutorService executor = Executors.newFixedThreadPool(1); private AtomicLong demand = new AtomicLong(0); SubscriptionImpl(Subscriber<? super AudioStream> s, InputStream inputStream) { this.subscriber = s; this.inputStream = inputStream; } @Override public void request(long n) { if (n <= 0) { subscriber.onError(new IllegalArgumentException("Demand must be positive")); Transcribe audio and get job data 492 Amazon Transcribe } demand.getAndAdd(n); executor.submit(() -> { try { do { Developer Guide ByteBuffer audioBuffer = getNextEvent(); if (audioBuffer.remaining() > 0) { AudioEvent audioEvent = audioEventFromBuffer(audioBuffer); subscriber.onNext(audioEvent); } else { subscriber.onComplete(); break; } } while (demand.decrementAndGet() > 0); } catch (Exception e) { subscriber.onError(e); } }); } @Override public void cancel() { executor.shutdown(); } private ByteBuffer getNextEvent() { ByteBuffer audioBuffer = null; byte[] audioBytes = new byte[CHUNK_SIZE_IN_BYTES]; int len = 0; try { len = inputStream.read(audioBytes); if (len <= 0) { audioBuffer = ByteBuffer.allocate(0); } else { audioBuffer = ByteBuffer.wrap(audioBytes, 0, len); } } catch (IOException e) { throw new UncheckedIOException(e); } Transcribe audio and get job data 493 Amazon Transcribe Developer Guide return audioBuffer; } private AudioEvent audioEventFromBuffer(ByteBuffer bb) { return AudioEvent.builder() .audioChunk(SdkBytes.fromByteBuffer(bb)) .build(); } } } Transcribes streaming audio from your computer's microphone. public class TranscribeStreamingDemoApp { private static final Region REGION = Region.US_EAST_1; private static TranscribeStreamingAsyncClient client; public static void main(String[] args) throws URISyntaxException, ExecutionException, InterruptedException, LineUnavailableException { client = TranscribeStreamingAsyncClient.builder() .credentialsProvider(getCredentials()) .region(REGION) .build(); CompletableFuture<Void> result = client.startStreamTranscription(getRequest(16_000), new AudioStreamPublisher(getStreamFromMic()), getResponseHandler()); result.get(); client.close(); } private static InputStream getStreamFromMic() throws LineUnavailableException { // Signed PCM AudioFormat with 16kHz, 16 bit sample size, mono int sampleRate = 16000; AudioFormat format = new AudioFormat(sampleRate, 16, 1, true, false); Transcribe audio and get job data 494 Amazon Transcribe Developer Guide DataLine.Info info = new DataLine.Info(TargetDataLine.class, format); if (!AudioSystem.isLineSupported(info)) { System.out.println("Line not supported"); System.exit(0); } TargetDataLine line = (TargetDataLine) AudioSystem.getLine(info); line.open(format);
transcribe-dg-100	transcribe-dg.pdf	100	private static final Region REGION = Region.US_EAST_1; private static TranscribeStreamingAsyncClient client; public static void main(String[] args) throws URISyntaxException, ExecutionException, InterruptedException, LineUnavailableException { client = TranscribeStreamingAsyncClient.builder() .credentialsProvider(getCredentials()) .region(REGION) .build(); CompletableFuture<Void> result = client.startStreamTranscription(getRequest(16_000), new AudioStreamPublisher(getStreamFromMic()), getResponseHandler()); result.get(); client.close(); } private static InputStream getStreamFromMic() throws LineUnavailableException { // Signed PCM AudioFormat with 16kHz, 16 bit sample size, mono int sampleRate = 16000; AudioFormat format = new AudioFormat(sampleRate, 16, 1, true, false); Transcribe audio and get job data 494 Amazon Transcribe Developer Guide DataLine.Info info = new DataLine.Info(TargetDataLine.class, format); if (!AudioSystem.isLineSupported(info)) { System.out.println("Line not supported"); System.exit(0); } TargetDataLine line = (TargetDataLine) AudioSystem.getLine(info); line.open(format); line.start(); InputStream audioStream = new AudioInputStream(line); return audioStream; } private static AwsCredentialsProvider getCredentials() { return DefaultCredentialsProvider.create(); } private static StartStreamTranscriptionRequest getRequest(Integer mediaSampleRateHertz) { return StartStreamTranscriptionRequest.builder() .languageCode(LanguageCode.EN_US.toString()) .mediaEncoding(MediaEncoding.PCM) .mediaSampleRateHertz(mediaSampleRateHertz) .build(); } private static StartStreamTranscriptionResponseHandler getResponseHandler() { return StartStreamTranscriptionResponseHandler.builder() .onResponse(r -> { System.out.println("Received Initial response"); }) .onError(e -> { System.out.println(e.getMessage()); StringWriter sw = new StringWriter(); e.printStackTrace(new PrintWriter(sw)); System.out.println("Error Occurred: " + sw); }) .onComplete(() -> { System.out.println("=== All records stream successfully ==="); }) .subscriber(event -> { Transcribe audio and get job data 495 Amazon Transcribe Developer Guide List<Result> results = ((TranscriptEvent) event).transcript().results(); if (results.size() > 0) { if (! results.get(0).alternatives().get(0).transcript().isEmpty()) { System.out.println(results.get(0).alternatives().get(0).transcript()); } } }) .build(); } private static class AudioStreamPublisher implements Publisher<AudioStream> { private static Subscription currentSubscription; private final InputStream inputStream; private AudioStreamPublisher(InputStream inputStream) { this.inputStream = inputStream; } @Override public void subscribe(Subscriber<? super AudioStream> s) { if (currentSubscription == null) { currentSubscription = new SubscriptionImpl(s, inputStream); } else { currentSubscription.cancel(); currentSubscription = new SubscriptionImpl(s, inputStream); } s.onSubscribe(currentSubscription); } } public static class SubscriptionImpl implements Subscription { private static final int CHUNK_SIZE_IN_BYTES = 1024; private final Subscriber<? super AudioStream> subscriber; private final InputStream inputStream; private final ExecutorService executor = Executors.newFixedThreadPool(1); private final AtomicLong demand = new AtomicLong(0); SubscriptionImpl(Subscriber<? super AudioStream> s, InputStream inputStream) { Transcribe audio and get job data 496 Amazon Transcribe Developer Guide this.subscriber = s; this.inputStream = inputStream; } @Override public void request(long n) { if (n <= 0) { subscriber.onError(new IllegalArgumentException("Demand must be positive")); } demand.getAndAdd(n); executor.submit(() -> { try { do { ByteBuffer audioBuffer = getNextEvent(); if (audioBuffer.remaining() > 0) { AudioEvent audioEvent = audioEventFromBuffer(audioBuffer); subscriber.onNext(audioEvent); } else { subscriber.onComplete(); break; } } while (demand.decrementAndGet() > 0); } catch (Exception e) { subscriber.onError(e); } }); } @Override public void cancel() { executor.shutdown(); } private ByteBuffer getNextEvent() { ByteBuffer audioBuffer = null; byte[] audioBytes = new byte[CHUNK_SIZE_IN_BYTES]; int len = 0; try { len = inputStream.read(audioBytes); Transcribe audio and get job data 497 Amazon Transcribe Developer Guide if (len <= 0) { audioBuffer = ByteBuffer.allocate(0); } else { audioBuffer = ByteBuffer.wrap(audioBytes, 0, len); } } catch (IOException e) { throw new UncheckedIOException(e); } return audioBuffer; } private AudioEvent audioEventFromBuffer(ByteBuffer bb) { return AudioEvent.builder() .audioChunk(SdkBytes.fromByteBuffer(bb)) .build(); } } } • For API details, see the following topics in AWS SDK for Java 2.x API Reference. • GetTranscriptionJob • StartTranscriptionJob Python SDK for Python (Boto3) Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. import time import boto3 def transcribe_file(job_name, file_uri, transcribe_client): Transcribe audio and get job data 498 Amazon Transcribe Developer Guide transcribe_client.start_transcription_job( TranscriptionJobName=job_name, Media={"MediaFileUri": file_uri}, MediaFormat="wav", LanguageCode="en-US", ) max_tries = 60 while max_tries > 0: max_tries -= 1 job = transcribe_client.get_transcription_job(TranscriptionJobName=job_name) job_status = job["TranscriptionJob"]["TranscriptionJobStatus"] if job_status in ["COMPLETED", "FAILED"]: print(f"Job {job_name} is {job_status}.") if job_status == "COMPLETED": print( f"Download the transcript from\n" f"\t{job['TranscriptionJob']['Transcript'] ['TranscriptFileUri']}." ) break else: print(f"Waiting for {job_name}. Current status is {job_status}.") time.sleep(10) def main(): transcribe_client = boto3.client("transcribe") file_uri = "s3://test-transcribe/answer2.wav" transcribe_file("Example-job", file_uri, transcribe_client) if __name__ == "__main__": main() • For API details, see the following topics in AWS SDK for Python (Boto3) API Reference. • GetTranscriptionJob • StartTranscriptionJob Transcribe audio and get job data 499 Amazon Transcribe Developer Guide For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Transcribe audio and get job data 500 Amazon Transcribe Developer Guide Security in Amazon Transcribe Cloud security at AWS is the highest priority. As an AWS customer, you beneﬁt from a data center and network architecture built to meet the requirements of the most security-sensitive organizations. Security is a shared responsibility between AWS and you. The shared responsibility model describes this as security of the cloud and security in the cloud: • Security of the cloud: AWS is responsible for protecting the infrastructure that runs AWS services in the AWS Cloud. AWS also provides you with services you can use securely. Third-party auditors regularly test and verify the eﬀectiveness of our security as part of the AWS Compliance Programs. To learn about the compliance programs that apply to Amazon Transcribe, see AWS Services in Scope by Compliance
transcribe-dg-101	transcribe-dg.pdf	101	to meet the requirements of the most security-sensitive organizations. Security is a shared responsibility between AWS and you. The shared responsibility model describes this as security of the cloud and security in the cloud: • Security of the cloud: AWS is responsible for protecting the infrastructure that runs AWS services in the AWS Cloud. AWS also provides you with services you can use securely. Third-party auditors regularly test and verify the eﬀectiveness of our security as part of the AWS Compliance Programs. To learn about the compliance programs that apply to Amazon Transcribe, see AWS Services in Scope by Compliance Program. • Security in the cloud: Your responsibility is determined by the AWS service that you use. You are also responsible for other factors including the sensitivity of your data, your company’s requirements, and applicable laws and regulations. This documentation helps you understand how to apply the shared responsibility model when using Amazon Transcribe. The following topics show you how to conﬁgure Amazon Transcribe to meet your security and compliance objectives. You also learn how to use other AWS services to monitor and secure your Amazon Transcribe resources. Topics • Identity and Access Management for Amazon Transcribe • Data protection in Amazon Transcribe • Monitoring Amazon Transcribe • Compliance validation for Amazon Transcribe • Resilience in Amazon Transcribe • Infrastructure security in Amazon Transcribe • Vulnerability analysis and management in Amazon Transcribe • Security best practices for Amazon Transcribe 501 Amazon Transcribe Developer Guide Identity and Access Management for Amazon Transcribe AWS Identity and Access Management (IAM) is an AWS service that helps an administrator securely control access to AWS resources. IAM administrators control who can be authenticated (signed in) and authorized (have permissions) to use Amazon Transcribe resources. IAM is an AWS service that you can use with no additional charge. Topics • Audience • Authenticating with identities • Managing access using policies • How Amazon Transcribe works with IAM • Cross-service confused deputy prevention • Amazon Transcribe identity-based policy examples • Troubleshooting Amazon Transcribe identity and access Audience How you use AWS Identity and Access Management (IAM) diﬀers, depending on the work that you do in Amazon Transcribe. Service user – If you use the Amazon Transcribe service to do your job, then your administrator provides you with the credentials and permissions that you need. As you use more Amazon Transcribe features to do your work, you might need additional permissions. Understanding how access is managed can help you request the right permissions from your administrator. If you cannot access a feature in Amazon Transcribe, see Troubleshooting Amazon Transcribe identity and access. Service administrator – If you're in charge of Amazon Transcribe resources at your company, you probably have full access to Amazon Transcribe. It's your job to determine which Amazon Transcribe features and resources your service users should access. You must then submit requests to your IAM administrator to change the permissions of your service users. Review the information on this page to understand the basic concepts of IAM. To learn more about how your company can use IAM with Amazon Transcribe, see How Amazon Transcribe works with IAM. IAM administrator – If you're an IAM administrator, you might want to learn details about how you can write policies to manage access to Amazon Transcribe. To view example Amazon Transcribe Identity and Access Management 502 Amazon Transcribe Developer Guide identity-based policies that you can use in IAM, see Amazon Transcribe identity-based policy examples. Authenticating with identities Authentication is how you sign in to AWS using your identity credentials. You must be authenticated (signed in to AWS) as the AWS account root user, as an IAM user, or by assuming an IAM role. You can sign in to AWS as a federated identity by using credentials provided through an identity source. AWS IAM Identity Center (IAM Identity Center) users, your company's single sign-on authentication, and your Google or Facebook credentials are examples of federated identities. When you sign in as a federated identity, your administrator previously set up identity federation using IAM roles. When you access AWS by using federation, you are indirectly assuming a role. Depending on the type of user you are, you can sign in to the AWS Management Console or the AWS access portal. For more information about signing in to AWS, see How to sign in to your AWS account in the AWS Sign-In User Guide. If you access AWS programmatically, AWS provides a software development kit (SDK) and a command line interface (CLI) to cryptographically sign your requests by using your credentials. If you don't use AWS tools, you must sign requests yourself. For more information about using the recommended method to sign requests yourself, see AWS Signature Version 4 for API requests in the IAM User Guide. Regardless of the authentication
transcribe-dg-102	transcribe-dg.pdf	102	to the AWS Management Console or the AWS access portal. For more information about signing in to AWS, see How to sign in to your AWS account in the AWS Sign-In User Guide. If you access AWS programmatically, AWS provides a software development kit (SDK) and a command line interface (CLI) to cryptographically sign your requests by using your credentials. If you don't use AWS tools, you must sign requests yourself. For more information about using the recommended method to sign requests yourself, see AWS Signature Version 4 for API requests in the IAM User Guide. Regardless of the authentication method that you use, you might be required to provide additional security information. For example, AWS recommends that you use multi-factor authentication (MFA) to increase the security of your account. To learn more, see Multi-factor authentication in the AWS IAM Identity Center User Guide and AWS Multi-factor authentication in IAM in the IAM User Guide. AWS account root user When you create an AWS account, you begin with one sign-in identity that has complete access to all AWS services and resources in the account. This identity is called the AWS account root user and is accessed by signing in with the email address and password that you used to create the account. We strongly recommend that you don't use the root user for your everyday tasks. Safeguard your root user credentials and use them to perform the tasks that only the root user can perform. For the complete list of tasks that require you to sign in as the root user, see Tasks that require root user credentials in the IAM User Guide. Authenticating with identities 503 Amazon Transcribe Federated identity Developer Guide As a best practice, require human users, including users that require administrator access, to use federation with an identity provider to access AWS services by using temporary credentials. A federated identity is a user from your enterprise user directory, a web identity provider, the AWS Directory Service, the Identity Center directory, or any user that accesses AWS services by using credentials provided through an identity source. When federated identities access AWS accounts, they assume roles, and the roles provide temporary credentials. For centralized access management, we recommend that you use AWS IAM Identity Center. You can create users and groups in IAM Identity Center, or you can connect and synchronize to a set of users and groups in your own identity source for use across all your AWS accounts and applications. For information about IAM Identity Center, see What is IAM Identity Center? in the AWS IAM Identity Center User Guide. IAM users and groups An IAM user is an identity within your AWS account that has speciﬁc permissions for a single person or application. Where possible, we recommend relying on temporary credentials instead of creating IAM users who have long-term credentials such as passwords and access keys. However, if you have speciﬁc use cases that require long-term credentials with IAM users, we recommend that you rotate access keys. For more information, see Rotate access keys regularly for use cases that require long- term credentials in the IAM User Guide. An IAM group is an identity that speciﬁes a collection of IAM users. You can't sign in as a group. You can use groups to specify permissions for multiple users at a time. Groups make permissions easier to manage for large sets of users. For example, you could have a group named IAMAdmins and give that group permissions to administer IAM resources. Users are diﬀerent from roles. A user is uniquely associated with one person or application, but a role is intended to be assumable by anyone who needs it. Users have permanent long-term credentials, but roles provide temporary credentials. To learn more, see Use cases for IAM users in the IAM User Guide. IAM roles An IAM role is an identity within your AWS account that has speciﬁc permissions. It is similar to an IAM user, but is not associated with a speciﬁc person. To temporarily assume an IAM role in the AWS Management Console, you can switch from a user to an IAM role (console). You can assume a Authenticating with identities 504 Amazon Transcribe Developer Guide role by calling an AWS CLI or AWS API operation or by using a custom URL. For more information about methods for using roles, see Methods to assume a role in the IAM User Guide. IAM roles with temporary credentials are useful in the following situations: • Federated user access – To assign permissions to a federated identity, you create a role and deﬁne permissions for the role. When a federated identity authenticates, the identity is associated with the role and is granted the permissions that are deﬁned by the role. For information about roles for federation, see
transcribe-dg-103	transcribe-dg.pdf	103	Developer Guide role by calling an AWS CLI or AWS API operation or by using a custom URL. For more information about methods for using roles, see Methods to assume a role in the IAM User Guide. IAM roles with temporary credentials are useful in the following situations: • Federated user access – To assign permissions to a federated identity, you create a role and deﬁne permissions for the role. When a federated identity authenticates, the identity is associated with the role and is granted the permissions that are deﬁned by the role. For information about roles for federation, see Create a role for a third-party identity provider (federation) in the IAM User Guide. If you use IAM Identity Center, you conﬁgure a permission set. To control what your identities can access after they authenticate, IAM Identity Center correlates the permission set to a role in IAM. For information about permissions sets, see Permission sets in the AWS IAM Identity Center User Guide. • Temporary IAM user permissions – An IAM user or role can assume an IAM role to temporarily take on diﬀerent permissions for a speciﬁc task. • Cross-account access – You can use an IAM role to allow someone (a trusted principal) in a diﬀerent account to access resources in your account. Roles are the primary way to grant cross- account access. However, with some AWS services, you can attach a policy directly to a resource (instead of using a role as a proxy). To learn the diﬀerence between roles and resource-based policies for cross-account access, see Cross account resource access in IAM in the IAM User Guide. • Cross-service access – Some AWS services use features in other AWS services. For example, when you make a call in a service, it's common for that service to run applications in Amazon EC2 or store objects in Amazon S3. A service might do this using the calling principal's permissions, using a service role, or using a service-linked role. • Forward access sessions (FAS) – When you use an IAM user or role to perform actions in AWS, you are considered a principal. When you use some services, you might perform an action that then initiates another action in a diﬀerent service. FAS uses the permissions of the principal calling an AWS service, combined with the requesting AWS service to make requests to downstream services. FAS requests are only made when a service receives a request that requires interactions with other AWS services or resources to complete. In this case, you must have permissions to perform both actions. For policy details when making FAS requests, see Forward access sessions. • Service role – A service role is an IAM role that a service assumes to perform actions on your behalf. An IAM administrator can create, modify, and delete a service role from within IAM. For more information, see Create a role to delegate permissions to an AWS service in the IAM User Guide. Authenticating with identities 505 Amazon Transcribe Developer Guide • Service-linked role – A service-linked role is a type of service role that is linked to an AWS service. The service can assume the role to perform an action on your behalf. Service-linked roles appear in your AWS account and are owned by the service. An IAM administrator can view, but not edit the permissions for service-linked roles. • Applications running on Amazon EC2 – You can use an IAM role to manage temporary credentials for applications that are running on an EC2 instance and making AWS CLI or AWS API requests. This is preferable to storing access keys within the EC2 instance. To assign an AWS role to an EC2 instance and make it available to all of its applications, you create an instance proﬁle that is attached to the instance. An instance proﬁle contains the role and enables programs that are running on the EC2 instance to get temporary credentials. For more information, see Use an IAM role to grant permissions to applications running on Amazon EC2 instances in the IAM User Guide. Managing access using policies You control access in AWS by creating policies and attaching them to AWS identities or resources. A policy is an object in AWS that, when associated with an identity or resource, deﬁnes their permissions. AWS evaluates these policies when a principal (user, root user, or role session) makes a request. Permissions in the policies determine whether the request is allowed or denied. Most policies are stored in AWS as JSON documents. For more information about the structure and contents of JSON policy documents, see Overview of JSON policies in the IAM User Guide. Administrators can use AWS JSON policies to specify who has access to what. That is, which principal can perform actions on what resources, and under
transcribe-dg-104	transcribe-dg.pdf	104	is an object in AWS that, when associated with an identity or resource, deﬁnes their permissions. AWS evaluates these policies when a principal (user, root user, or role session) makes a request. Permissions in the policies determine whether the request is allowed or denied. Most policies are stored in AWS as JSON documents. For more information about the structure and contents of JSON policy documents, see Overview of JSON policies in the IAM User Guide. Administrators can use AWS JSON policies to specify who has access to what. That is, which principal can perform actions on what resources, and under what conditions. By default, users and roles have no permissions. To grant users permission to perform actions on the resources that they need, an IAM administrator can create IAM policies. The administrator can then add the IAM policies to roles, and users can assume the roles. IAM policies deﬁne permissions for an action regardless of the method that you use to perform the operation. For example, suppose that you have a policy that allows the iam:GetRole action. A user with that policy can get role information from the AWS Management Console, the AWS CLI, or the AWS API. Identity-based policies Identity-based policies are JSON permissions policy documents that you can attach to an identity, such as an IAM user, group of users, or role. These policies control what actions users and roles can Managing access using policies 506 Amazon Transcribe Developer Guide perform, on which resources, and under what conditions. To learn how to create an identity-based policy, see Deﬁne custom IAM permissions with customer managed policies in the IAM User Guide. Identity-based policies can be further categorized as inline policies or managed policies. Inline policies are embedded directly into a single user, group, or role. Managed policies are standalone policies that you can attach to multiple users, groups, and roles in your AWS account. Managed policies include AWS managed policies and customer managed policies. To learn how to choose between a managed policy or an inline policy, see Choose between managed policies and inline policies in the IAM User Guide. Resource-based policies Resource-based policies are JSON policy documents that you attach to a resource. Examples of resource-based policies are IAM role trust policies and Amazon S3 bucket policies. In services that support resource-based policies, service administrators can use them to control access to a speciﬁc resource. For the resource where the policy is attached, the policy deﬁnes what actions a speciﬁed principal can perform on that resource and under what conditions. You must specify a principal in a resource-based policy. Principals can include accounts, users, roles, federated users, or AWS services. Resource-based policies are inline policies that are located in that service. You can't use AWS managed policies from IAM in a resource-based policy. Access control lists (ACLs) Access control lists (ACLs) control which principals (account members, users, or roles) have permissions to access a resource. ACLs are similar to resource-based policies, although they do not use the JSON policy document format. Amazon S3, AWS WAF, and Amazon VPC are examples of services that support ACLs. To learn more about ACLs, see Access control list (ACL) overview in the Amazon Simple Storage Service Developer Guide. Other policy types AWS supports additional, less-common policy types. These policy types can set the maximum permissions granted to you by the more common policy types. • Permissions boundaries – A permissions boundary is an advanced feature in which you set the maximum permissions that an identity-based policy can grant to an IAM entity (IAM user Managing access using policies 507 Amazon Transcribe Developer Guide or role). You can set a permissions boundary for an entity. The resulting permissions are the intersection of an entity's identity-based policies and its permissions boundaries. Resource-based policies that specify the user or role in the Principal ﬁeld are not limited by the permissions boundary. An explicit deny in any of these policies overrides the allow. For more information about permissions boundaries, see Permissions boundaries for IAM entities in the IAM User Guide. • Service control policies (SCPs) – SCPs are JSON policies that specify the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. AWS Organizations is a service for grouping and centrally managing multiple AWS accounts that your business owns. If you enable all features in an organization, then you can apply service control policies (SCPs) to any or all of your accounts. The SCP limits permissions for entities in member accounts, including each AWS account root user. For more information about Organizations and SCPs, see Service control policies in the AWS Organizations User Guide. • Resource control policies (RCPs) – RCPs are JSON policies that you can use to set the maximum available permissions for resources in your accounts without updating the IAM
transcribe-dg-105	transcribe-dg.pdf	105	Organizations is a service for grouping and centrally managing multiple AWS accounts that your business owns. If you enable all features in an organization, then you can apply service control policies (SCPs) to any or all of your accounts. The SCP limits permissions for entities in member accounts, including each AWS account root user. For more information about Organizations and SCPs, see Service control policies in the AWS Organizations User Guide. • Resource control policies (RCPs) – RCPs are JSON policies that you can use to set the maximum available permissions for resources in your accounts without updating the IAM policies attached to each resource that you own. The RCP limits permissions for resources in member accounts and can impact the eﬀective permissions for identities, including the AWS account root user, regardless of whether they belong to your organization. For more information about Organizations and RCPs, including a list of AWS services that support RCPs, see Resource control policies (RCPs) in the AWS Organizations User Guide. • Session policies – Session policies are advanced policies that you pass as a parameter when you programmatically create a temporary session for a role or federated user. The resulting session's permissions are the intersection of the user or role's identity-based policies and the session policies. Permissions can also come from a resource-based policy. An explicit deny in any of these policies overrides the allow. For more information, see Session policies in the IAM User Guide. Multiple policy types When multiple types of policies apply to a request, the resulting permissions are more complicated to understand. To learn how AWS determines whether to allow a request when multiple policy types are involved, see Policy evaluation logic in the IAM User Guide. How Amazon Transcribe works with IAM Before you use IAM to manage access to Amazon Transcribe, learn what IAM features are available to use with Amazon Transcribe. How Amazon Transcribe works with IAM 508 Amazon Transcribe Developer Guide IAM features you can use with Amazon Transcribe IAM feature Amazon Transcribe support Identity-based policies Resource-based policies Policy actions Policy resources Policy condition keys (service-speciﬁc) ACLs Yes No Yes Yes Yes No ABAC (tags in policies) Partial Temporary credentials Principal permissions Service roles Service-linked roles Yes Yes Yes No To get a high-level view of how Amazon Transcribe and other AWS services work with most IAM features, see AWS services that work with IAM in the IAM User Guide. Identity-based policies for Amazon Transcribe Supports identity-based policies: Yes Identity-based policies are JSON permissions policy documents that you can attach to an identity, such as an IAM user, group of users, or role. These policies control what actions users and roles can perform, on which resources, and under what conditions. To learn how to create an identity-based policy, see Deﬁne custom IAM permissions with customer managed policies in the IAM User Guide. With IAM identity-based policies, you can specify allowed or denied actions and resources as well as the conditions under which actions are allowed or denied. You can't specify the principal in an identity-based policy because it applies to the user or role to which it is attached. To learn about all How Amazon Transcribe works with IAM 509 Amazon Transcribe Developer Guide of the elements that you can use in a JSON policy, see IAM JSON policy elements reference in the IAM User Guide. Identity-based policy examples for Amazon Transcribe To view examples of Amazon Transcribe identity-based policies, see Amazon Transcribe identity- based policy examples. Resource-based policies within Amazon Transcribe Supports resource-based policies: No Resource-based policies are JSON policy documents that you attach to a resource. Examples of resource-based policies are IAM role trust policies and Amazon S3 bucket policies. In services that support resource-based policies, service administrators can use them to control access to a speciﬁc resource. For the resource where the policy is attached, the policy deﬁnes what actions a speciﬁed principal can perform on that resource and under what conditions. You must specify a principal in a resource-based policy. Principals can include accounts, users, roles, federated users, or AWS services. To enable cross-account access, you can specify an entire account or IAM entities in another account as the principal in a resource-based policy. Adding a cross-account principal to a resource- based policy is only half of establishing the trust relationship. When the principal and the resource are in diﬀerent AWS accounts, an IAM administrator in the trusted account must also grant the principal entity (user or role) permission to access the resource. They grant permission by attaching an identity-based policy to the entity. However, if a resource-based policy grants access to a principal in the same account, no additional identity-based policy is required. For more information, see Cross account resource access in IAM in the IAM User Guide. Policy actions for
transcribe-dg-106	transcribe-dg.pdf	106	resource-based policy. Adding a cross-account principal to a resource- based policy is only half of establishing the trust relationship. When the principal and the resource are in diﬀerent AWS accounts, an IAM administrator in the trusted account must also grant the principal entity (user or role) permission to access the resource. They grant permission by attaching an identity-based policy to the entity. However, if a resource-based policy grants access to a principal in the same account, no additional identity-based policy is required. For more information, see Cross account resource access in IAM in the IAM User Guide. Policy actions for Amazon Transcribe Supports policy actions: Yes Administrators can use AWS JSON policies to specify who has access to what. That is, which principal can perform actions on what resources, and under what conditions. The Action element of a JSON policy describes the actions that you can use to allow or deny access in a policy. Policy actions usually have the same name as the associated AWS API operation. There are some exceptions, such as permission-only actions that don't have a matching API How Amazon Transcribe works with IAM 510 Amazon Transcribe Developer Guide operation. There are also some operations that require multiple actions in a policy. These additional actions are called dependent actions. Include actions in a policy to grant permissions to perform the associated operation. To see a list of Amazon Transcribe actions, see Actions deﬁned by Amazon Transcribe in the Service Authorization Reference. Policy actions in Amazon Transcribe use the transcribe preﬁx before the action. To specify multiple actions in a single statement, separate them with commas. "Action": [ "transcribe:action1", "transcribe:action2" ] You can specify multiple actions using wildcards (). For example, to specify all actions that begin with the word List, include the following action: "Action": "transcribe:List" To view examples of Amazon Transcribe identity-based policies, see Amazon Transcribe identity- based policy examples. Policy resources for Amazon Transcribe Supports policy resources: Yes Administrators can use AWS JSON policies to specify who has access to what. That is, which principal can perform actions on what resources, and under what conditions. The Resource JSON policy element speciﬁes the object or objects to which the action applies. Statements must include either a Resource or a NotResource element. As a best practice, specify a resource using its Amazon Resource Name (ARN). You can do this for actions that support a speciﬁc resource type, known as resource-level permissions. For actions that don't support resource-level permissions, such as listing operations, use a wildcard () to indicate that the statement applies to all resources. How Amazon Transcribe works with IAM 511 Amazon Transcribe "Resource": "" Developer Guide To see a list of Amazon Transcribe resource types and their ARNs, see Resources deﬁned by Amazon Transcribe in the Service Authorization Reference. To learn with which actions you can specify the ARN of each resource, see Actions deﬁned by Amazon Transcribe. To view examples of Amazon Transcribe identity-based policies, see Amazon Transcribe identity- based policy examples. Policy condition keys for Amazon Transcribe Supports service-speciﬁc policy condition keys: Yes Administrators can use AWS JSON policies to specify who has access to what. That is, which principal can perform actions on what resources, and under what conditions. The Condition element (or Condition block) lets you specify conditions in which a statement is in eﬀect. The Condition element is optional. You can create conditional expressions that use condition operators, such as equals or less than, to match the condition in the policy with values in the request. If you specify multiple Condition elements in a statement, or multiple keys in a single Condition element, AWS evaluates them using a logical AND operation. If you specify multiple values for a single condition key, AWS evaluates the condition using a logical OR operation. All of the conditions must be met before the statement's permissions are granted. You can also use placeholder variables when you specify conditions. For example, you can grant an IAM user permission to access a resource only if it is tagged with their IAM user name. For more information, see IAM policy elements: variables and tags in the IAM User Guide. AWS supports global condition keys and service-speciﬁc condition keys. To see all AWS global condition keys, see AWS global condition context keys in the IAM User Guide. To see a list of Amazon Transcribe condition keys, see Condition keys for Amazon Transcribe in the Service Authorization Reference. To learn with which actions and resources you can use a condition key, see Actions deﬁned by Amazon Transcribe. To view examples of Amazon Transcribe identity-based policies, see Amazon Transcribe identity- based policy examples. How Amazon Transcribe works with IAM 512 Amazon Transcribe Developer Guide ACLs in Amazon Transcribe Supports ACLs: No Access control lists (ACLs) control which principals (account members,
transcribe-dg-107	transcribe-dg.pdf	107	keys. To see all AWS global condition keys, see AWS global condition context keys in the IAM User Guide. To see a list of Amazon Transcribe condition keys, see Condition keys for Amazon Transcribe in the Service Authorization Reference. To learn with which actions and resources you can use a condition key, see Actions deﬁned by Amazon Transcribe. To view examples of Amazon Transcribe identity-based policies, see Amazon Transcribe identity- based policy examples. How Amazon Transcribe works with IAM 512 Amazon Transcribe Developer Guide ACLs in Amazon Transcribe Supports ACLs: No Access control lists (ACLs) control which principals (account members, users, or roles) have permissions to access a resource. ACLs are similar to resource-based policies, although they do not use the JSON policy document format. ABAC with Amazon Transcribe Supports ABAC (tags in policies): Partial Attribute-based access control (ABAC) is an authorization strategy that deﬁnes permissions based on attributes. In AWS, these attributes are called tags. You can attach tags to IAM entities (users or roles) and to many AWS resources. Tagging entities and resources is the ﬁrst step of ABAC. Then you design ABAC policies to allow operations when the principal's tag matches the tag on the resource that they are trying to access. ABAC is helpful in environments that are growing rapidly and helps with situations where policy management becomes cumbersome. To control access based on tags, you provide tag information in the condition element of a policy using the aws:ResourceTag/key-name, aws:RequestTag/key-name, or aws:TagKeys condition keys. If a service supports all three condition keys for every resource type, then the value is Yes for the service. If a service supports all three condition keys for only some resource types, then the value is Partial. For more information about ABAC, see Deﬁne permissions with ABAC authorization in the IAM User Guide. To view a tutorial with steps for setting up ABAC, see Use attribute-based access control (ABAC) in the IAM User Guide. For more information about tagging Amazon Transcribe resources, see Tagging resources. For more detailed information on tag-based access control, see Controlling access to AWS resources using tags. Using temporary credentials with Amazon Transcribe Supports temporary credentials: Yes How Amazon Transcribe works with IAM 513 Amazon Transcribe Developer Guide Some AWS services don't work when you sign in using temporary credentials. For additional information, including which AWS services work with temporary credentials, see AWS services that work with IAM in the IAM User Guide. You are using temporary credentials if you sign in to the AWS Management Console using any method except a user name and password. For example, when you access AWS using your company's single sign-on (SSO) link, that process automatically creates temporary credentials. You also automatically create temporary credentials when you sign in to the console as a user and then switch roles. For more information about switching roles, see Switch from a user to an IAM role (console) in the IAM User Guide. You can manually create temporary credentials using the AWS CLI or AWS API. You can then use those temporary credentials to access AWS. AWS recommends that you dynamically generate temporary credentials instead of using long-term access keys. For more information, see Temporary security credentials in IAM. Cross-service principal permissions for Amazon Transcribe Supports forward access sessions (FAS): Yes When you use an IAM user or role to perform actions in AWS, you are considered a principal. When you use some services, you might perform an action that then initiates another action in a diﬀerent service. FAS uses the permissions of the principal calling an AWS service, combined with the requesting AWS service to make requests to downstream services. FAS requests are only made when a service receives a request that requires interactions with other AWS services or resources to complete. In this case, you must have permissions to perform both actions. For policy details when making FAS requests, see Forward access sessions. Service roles for Amazon Transcribe Supports service roles: Yes A service role is an IAM role that a service assumes to perform actions on your behalf. An IAM administrator can create, modify, and delete a service role from within IAM. For more information, see Create a role to delegate permissions to an AWS service in the IAM User Guide. Warning Changing the permissions for a service role might break Amazon Transcribe functionality. Edit service roles only when Amazon Transcribe provides guidance to do so. How Amazon Transcribe works with IAM 514 Amazon Transcribe Developer Guide Service-linked roles for Amazon Transcribe Supports service-linked roles: No A service-linked role is a type of service role that is linked to an AWS service. The service can assume the role to perform an action on your behalf. Service-linked roles appear in your AWS account and are owned by the service. An IAM administrator
transcribe-dg-108	transcribe-dg.pdf	108	permissions to an AWS service in the IAM User Guide. Warning Changing the permissions for a service role might break Amazon Transcribe functionality. Edit service roles only when Amazon Transcribe provides guidance to do so. How Amazon Transcribe works with IAM 514 Amazon Transcribe Developer Guide Service-linked roles for Amazon Transcribe Supports service-linked roles: No A service-linked role is a type of service role that is linked to an AWS service. The service can assume the role to perform an action on your behalf. Service-linked roles appear in your AWS account and are owned by the service. An IAM administrator can view, but not edit the permissions for service-linked roles. Amazon Transcribe doesn't support service-linked roles. For details about creating or managing service-linked roles for other services, see AWS services that work with IAM. Find a service in the table that includes a Yes in the Service-linked role column. Choose the Yes link to view the service-linked role documentation for that service. Cross-service confused deputy prevention A confused deputy is an entity (a service or an account) that is coerced by a diﬀerent entity to perform an action. This type of impersonation can happen cross-account and cross-service. To prevent confused deputies, AWS provides tools that help you protect your data for all services using service principals that have been given access to resources in your AWS account. This section focuses on cross-service confused deputy prevention speciﬁc to Amazon Transcribe; however, you can learn more about this topic in the confused deputy problem section of the IAM User Guide. To limit the permissions IAM gives to Amazon Transcribe to access your resources, we recommend using the global condition context keys aws:SourceArn and aws:SourceAccount in your resource policies. If you use both of these global condition context keys, and the aws:SourceArn value contains the AWS account ID, the aws:SourceAccount value and the AWS account in aws:SourceArn must use the same AWS account ID when used in the same policy statement. If you want only one resource to be associated with the cross-service access, use aws:SourceArn. If you want to associate any resource in that AWS account with cross-service access, use aws:SourceAccount. Note The most eﬀective way to protect against the confused deputy problem is to use the aws:SourceArn global condition context key with the full ARN of the resource. Confused deputy prevention 515 Amazon Transcribe Developer Guide If you don’t know the full ARN, or if you're specifying multiple resources, use the aws:SourceArn global context condition key with wildcards () for the unknown portions of the ARN. For example, arn:aws:transcribe::123456789012:. For an example of an assume role policy that shows how you can prevent a confused deputy issue, see Confused deputy prevention policy. Amazon Transcribe identity-based policy examples By default, users and roles don't have permission to create or modify Amazon Transcribe resources. They also can't perform tasks by using the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS API. To grant users permission to perform actions on the resources that they need, an IAM administrator can create IAM policies. The administrator can then add the IAM policies to roles, and users can assume the roles. To learn how to create an IAM identity-based policy by using these example JSON policy documents, see Create IAM policies (console) in the IAM User Guide. For details about actions and resource types deﬁned by Amazon Transcribe, including the format of the ARNs for each of the resource types, see Actions, resources, and condition keys for Amazon Transcribe in the Service Authorization Reference. Topics • Policy best practices • Using the AWS Management Console • Permissions required for IAM roles • Permissions required for Amazon S3 encryption keys • Allow users to view their own permissions • AWS KMS encryption context policy • Confused deputy prevention policy • Viewing transcription jobs based on tags Identity-based policy examples 516 Amazon Transcribe Policy best practices Developer Guide Identity-based policies determine whether someone can create, access, or delete Amazon Transcribe resources in your account. These actions can incur costs for your AWS account. When you create or edit identity-based policies, follow these guidelines and recommendations: • Get started with AWS managed policies and move toward least-privilege permissions – To get started granting permissions to your users and workloads, use the AWS managed policies that grant permissions for many common use cases. They are available in your AWS account. We recommend that you reduce permissions further by deﬁning AWS customer managed policies that are speciﬁc to your use cases. For more information, see AWS managed policies or AWS managed policies for job functions in the IAM User Guide. • Apply least-privilege permissions – When you set permissions with IAM policies, grant only the permissions required to perform a task. You do this by deﬁning the actions that
transcribe-dg-109	transcribe-dg.pdf	109	permissions – To get started granting permissions to your users and workloads, use the AWS managed policies that grant permissions for many common use cases. They are available in your AWS account. We recommend that you reduce permissions further by deﬁning AWS customer managed policies that are speciﬁc to your use cases. For more information, see AWS managed policies or AWS managed policies for job functions in the IAM User Guide. • Apply least-privilege permissions – When you set permissions with IAM policies, grant only the permissions required to perform a task. You do this by deﬁning the actions that can be taken on speciﬁc resources under speciﬁc conditions, also known as least-privilege permissions. For more information about using IAM to apply permissions, see Policies and permissions in IAM in the IAM User Guide. • Use conditions in IAM policies to further restrict access – You can add a condition to your policies to limit access to actions and resources. For example, you can write a policy condition to specify that all requests must be sent using SSL. You can also use conditions to grant access to service actions if they are used through a speciﬁc AWS service, such as AWS CloudFormation. For more information, see IAM JSON policy elements: Condition in the IAM User Guide. • Use IAM Access Analyzer to validate your IAM policies to ensure secure and functional permissions – IAM Access Analyzer validates new and existing policies so that the policies adhere to the IAM policy language (JSON) and IAM best practices. IAM Access Analyzer provides more than 100 policy checks and actionable recommendations to help you author secure and functional policies. For more information, see Validate policies with IAM Access Analyzer in the IAM User Guide. • Require multi-factor authentication (MFA) – If you have a scenario that requires IAM users or a root user in your AWS account, turn on MFA for additional security. To require MFA when API operations are called, add MFA conditions to your policies. For more information, see Secure API access with MFA in the IAM User Guide. For more information about best practices in IAM, see Security best practices in IAM in the IAM User Guide. Identity-based policy examples 517 Amazon Transcribe Developer Guide Using the AWS Management Console To access the Amazon Transcribe console, you must have a minimum set of permissions. These permissions must allow you to list and view details about the Amazon Transcribe resources in your AWS account. If you create an identity-based policy that is more restrictive than the minimum required permissions, the console won't function as intended for entities (users or roles) with that policy. You don't need to allow minimum console permissions for users that are making calls only to the AWS CLI or the AWS API. Instead, allow access to only the actions that match the API operation that they're trying to perform. To ensure that an entity (users and roles) can use the AWS Management Console, attach one of the following AWS-managed policies to them. • AmazonTranscribeFullAccess: Grants full access to create, read, update, delete, and run all Amazon Transcribe resources. It also allows access to Amazon S3 buckets with transcribe in the bucket name. • AmazonTranscribeReadOnlyAccess: Grants read-only access to Amazon Transcribe resources so that you can get and list transcription jobs and custom vocabularies. Note You can review the managed permission policies by signing in to the IAM AWS Management Console and searching by policy name. A search for "transcribe" returns both policies listed above (AmazonTranscribeReadOnly and AmazonTranscribeFullAccess). You can also create your own custom IAM policies to allow permissions for Amazon Transcribe API actions. You can attach these custom policies to the entities that require those permissions. Permissions required for IAM roles If you create an IAM role to call Amazon Transcribe, it must have permission to access the Amazon S3 bucket. If applicable, the KMS key must also be used to encrypt the contents of the bucket. Refer to the following sections for example policies. Identity-based policy examples 518 Amazon Transcribe Trust policies Developer Guide The IAM entity you use to make your transcription request must have a trust policy that enables Amazon Transcribe to assume that role. Use the following Amazon Transcribe trust policy. Note that if you're making a real-time Call Analytics request with post-call analytics enabled, you must use 'Trust policy for real-time Call Analytics'. Trust policy for Amazon Transcribe { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": [ "transcribe.amazonaws.com" ] }, "Action": [ "sts:AssumeRole" ], "Condition": { "StringEquals": { "aws:SourceAccount": "111122223333" }, "StringLike": { "aws:SourceArn": "arn:aws:transcribe:us-west-2:111122223333:*" } } } ] } Trust policy for real-time Call Analytics { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": [ Identity-based policy examples 519 Amazon Transcribe Developer Guide "transcribe.streaming.amazonaws.com" ]
transcribe-dg-110	transcribe-dg.pdf	110	that role. Use the following Amazon Transcribe trust policy. Note that if you're making a real-time Call Analytics request with post-call analytics enabled, you must use 'Trust policy for real-time Call Analytics'. Trust policy for Amazon Transcribe { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": [ "transcribe.amazonaws.com" ] }, "Action": [ "sts:AssumeRole" ], "Condition": { "StringEquals": { "aws:SourceAccount": "111122223333" }, "StringLike": { "aws:SourceArn": "arn:aws:transcribe:us-west-2:111122223333:" } } } ] } Trust policy for real-time Call Analytics { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": [ Identity-based policy examples 519 Amazon Transcribe Developer Guide "transcribe.streaming.amazonaws.com" ] }, "Action": [ "sts:AssumeRole" ], "Condition": { "StringEquals": { "aws:SourceAccount": "111122223333" }, "StringLike": { "aws:SourceArn": "arn:aws:transcribe:us-west-2:111122223333:" } } } ] } Amazon S3 input bucket policy The following policy gives an IAM role permission to access ﬁles from the speciﬁed input bucket. { "Version": "2012-10-17", "Statement": { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:ListBucket" ], "Resource": [ "arn:aws:s3:::DOC-EXAMPLE-INPUT-BUCKET", "arn:aws:s3:::DOC-EXAMPLE-INPUT-BUCKET/" ] } } Amazon S3 output bucket policy The following policy gives an IAM role permission to write ﬁles to the speciﬁed output bucket. { "Version": "2012-10-17", Identity-based policy examples 520 Amazon Transcribe Developer Guide "Statement": { "Effect": "Allow", "Action": [ "s3:PutObject" ], "Resource": [ "arn:aws:s3:::DOC-EXAMPLE-OUTPUT-BUCKET/" ] } } Permissions required for Amazon S3 encryption keys If you're using a KMS key to encrypt an Amazon S3 bucket, include the following in the KMS key policy. This gives Amazon Transcribe access to the contents of the bucket. For more information about allowing access to KMS keys, see Allowing external AWS accounts to access an KMS key in the AWS KMS Developer Guide. { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "AWS": "arn:aws:iam::111122223333:role/ExampleRole" }, "Action": [ "kms:Decrypt" ], "Resource": "arn:aws:kms:us-west-2:111122223333:key/KMS-Example-KeyId" } ] } Allow users to view their own permissions This example shows how you might create a policy that allows IAM users to view the inline and managed policies that are attached to their user identity. This policy includes permissions to complete this action on the console or programmatically using the AWS CLI or AWS API. { "Version": "2012-10-17", Identity-based policy examples 521 Developer Guide Amazon Transcribe "Statement": [ { "Sid": "ViewOwnUserInfo", "Effect": "Allow", "Action": [ "iam:GetUserPolicy", "iam:ListGroupsForUser", "iam:ListAttachedUserPolicies", "iam:ListUserPolicies", "iam:GetUser" ], "Resource": ["arn:aws:iam:::user/${aws:username}"] }, { "Sid": "NavigateInConsole", "Effect": "Allow", "Action": [ "iam:GetGroupPolicy", "iam:GetPolicyVersion", "iam:GetPolicy", "iam:ListAttachedGroupPolicies", "iam:ListGroupPolicies", "iam:ListPolicyVersions", "iam:ListPolicies", "iam:ListUsers" ], "Resource": "" } ] } AWS KMS encryption context policy The following policy grants the IAM role “ExampleRole” permission to use the AWS KMS Decrypt and Encrypt operations for this particular KMS key. This policy works only for requests with at least one encryption context pair, in this case "color:indigoBlue”. For more information on AWS KMS encryption context, see AWS KMS encryption context. { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", Identity-based policy examples 522 Amazon Transcribe "Principal": { "AWS": "arn:aws:iam::111122223333:role/ExampleRole" }, Developer Guide "Action": [ "kms:Decrypt", "kms:DescribeKey", "kms:Encrypt", "kms:GenerateDataKey", "kms:ReEncrypt" ], "Resource": "", "Condition": { "StringEquals": { "kms:EncryptionContext:color":"indigoBlue" } } } ] } Confused deputy prevention policy Here's an example of an assume role policy that shows how you can use aws:SourceArn and aws:SourceAccount with Amazon Transcribe to prevent a confused deputy issue. For more information on confused deputy prevention, see Cross-service confused deputy prevention. { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "transcribe.amazonaws.com" }, "Action": [ "sts:AssumeRole", ], "Condition": { "StringEquals": { "aws:SourceAccount": "111122223333" }, "StringLike": { "aws:SourceArn": "arn:aws:transcribe:us-west-2:111122223333:" Identity-based policy examples 523 Amazon Transcribe } } } ] } Developer Guide Viewing transcription jobs based on tags You can use conditions in your identity-based policy to control access to Amazon Transcribe resources based on tags. This example shows how you might create a policy that allows viewing a transcription job. However, permission is granted only if the transcription job tag Owner has the value of that user's user name. This policy also grants the permissions necessary to complete this action using the AWS Management Console. You can attach this policy to the IAM entities in your account. If a role named test-role attempts to view a transcription job, the transcription job must be tagged Owner=test-role or owner=test-role (condition key names are not case-sensitive), otherwise they are denied access. For more information, see IAM JSON policy elements: Condition in the IAM User Guide. For more information on tagging in Amazon Transcribe, see Tagging resources. { "Version": "2012-10-17", "Statement": [ { "Sid": "ListTranscriptionJobsInConsole", "Effect": "Allow", "Action": "transcribe:ListTranscriptionJobs", "Resource": "" }, { "Sid": "ViewTranscriptionJobsIfOwner", "Effect": "Allow", "Action": "transcribe:GetTranscriptionJobs", "Resource": "arn:aws:transcribe:::transcription-job/", "Condition": { "StringEquals": {"aws:ResourceTag/Owner": "${aws:username}"} } } ] } Identity-based policy examples 524 Amazon Transcribe Developer Guide Troubleshooting Amazon Transcribe identity and access Use the following information to diagnose and ﬁx common issues that you might encounter when working with Amazon Transcribe and AWS Identity and Access Management (IAM). Topics • I am not authorized to
transcribe-dg-111	transcribe-dg.pdf	111	JSON policy elements: Condition in the IAM User Guide. For more information on tagging in Amazon Transcribe, see Tagging resources. { "Version": "2012-10-17", "Statement": [ { "Sid": "ListTranscriptionJobsInConsole", "Effect": "Allow", "Action": "transcribe:ListTranscriptionJobs", "Resource": "" }, { "Sid": "ViewTranscriptionJobsIfOwner", "Effect": "Allow", "Action": "transcribe:GetTranscriptionJobs", "Resource": "arn:aws:transcribe:::transcription-job/", "Condition": { "StringEquals": {"aws:ResourceTag/Owner": "${aws:username}"} } } ] } Identity-based policy examples 524 Amazon Transcribe Developer Guide Troubleshooting Amazon Transcribe identity and access Use the following information to diagnose and ﬁx common issues that you might encounter when working with Amazon Transcribe and AWS Identity and Access Management (IAM). Topics • I am not authorized to perform an action in Amazon Transcribe • I am not authorized to perform iam:PassRole • I want to allow people outside of my AWS account to access my Amazon Transcribe resources I am not authorized to perform an action in Amazon Transcribe If you receive an error that you're not authorized to perform an action, your policies must be updated to allow you to perform the action. The following example error occurs when the mateojackson IAM user tries to use the console to view details about a ﬁctional my-example-widget resource but doesn't have the ﬁctional transcribe:GetWidget permissions. User: arn:aws:iam::123456789012:user/mateojackson is not authorized to perform: transcribe:GetWidget on resource: my-example-widget In this case, the policy for the mateojackson user must be updated to allow access to the my- example-widget resource by using the transcribe:GetWidget action. If you need help, contact your AWS administrator. Your administrator is the person who provided you with your sign-in credentials. I am not authorized to perform iam:PassRole If you receive an error that you're not authorized to perform the iam:PassRole action, your policies must be updated to allow you to pass a role to Amazon Transcribe. Some AWS services allow you to pass an existing role to that service instead of creating a new service role or service-linked role. To do this, you must have permissions to pass the role to the service. The following example error occurs when an IAM user named marymajor tries to use the console to perform an action in Amazon Transcribe. However, the action requires the service to have Troubleshooting 525 Amazon Transcribe Developer Guide permissions that are granted by a service role. Mary does not have permissions to pass the role to the service. User: arn:aws:iam::123456789012:user/marymajor is not authorized to perform: iam:PassRole In this case, Mary's policies must be updated to allow her to perform the iam:PassRole action. If you need help, contact your AWS administrator. Your administrator is the person who provided you with your sign-in credentials. I want to allow people outside of my AWS account to access my Amazon Transcribe resources You can create a role that users in other accounts or people outside of your organization can use to access your resources. You can specify who is trusted to assume the role. For services that support resource-based policies or access control lists (ACLs), you can use those policies to grant people access to your resources. To learn more, consult the following: • To learn whether Amazon Transcribe supports these features, see How Amazon Transcribe works with IAM. • To learn how to provide access to your resources across AWS accounts that you own, see Providing access to an IAM user in another AWS account that you own in the IAM User Guide. • To learn how to provide access to your resources to third-party AWS accounts, see Providing access to AWS accounts owned by third parties in the IAM User Guide. • To learn how to provide access through identity federation, see Providing access to externally authenticated users (identity federation) in the IAM User Guide. • To learn the diﬀerence between using roles and resource-based policies for cross-account access, see Cross account resource access in IAM in the IAM User Guide. Data protection in Amazon Transcribe The AWS shared responsibility model applies to data protection in Amazon Transcribe. As described in this model, AWS is responsible for protecting the global infrastructure that runs all of the AWS Cloud. You are responsible for maintaining control over your content that is hosted on this Data protection 526 Amazon Transcribe Developer Guide infrastructure. You are also responsible for the security conﬁguration and management tasks for the AWS services that you use. For more information about data privacy, see the Data Privacy FAQ. For information about data protection in Europe, see the AWS Shared Responsibility Model and GDPR blog post on the AWS Security Blog. For data protection purposes, we recommend that you protect AWS account credentials and set up individual users with AWS IAM Identity Center or AWS Identity and Access Management (IAM). That way, each user is given only the permissions necessary to fulﬁll their job duties. We also recommend that you secure your data in the following ways:
transcribe-dg-112	transcribe-dg.pdf	112	conﬁguration and management tasks for the AWS services that you use. For more information about data privacy, see the Data Privacy FAQ. For information about data protection in Europe, see the AWS Shared Responsibility Model and GDPR blog post on the AWS Security Blog. For data protection purposes, we recommend that you protect AWS account credentials and set up individual users with AWS IAM Identity Center or AWS Identity and Access Management (IAM). That way, each user is given only the permissions necessary to fulﬁll their job duties. We also recommend that you secure your data in the following ways: • Use multi-factor authentication (MFA) with each account. • Use SSL/TLS to communicate with AWS resources. We require TLS 1.2 and recommend TLS 1.3. • Set up API and user activity logging with AWS CloudTrail. For information about using CloudTrail trails to capture AWS activities, see Working with CloudTrail trails in the AWS CloudTrail User Guide. • Use AWS encryption solutions, along with all default security controls within AWS services. • Use advanced managed security services such as Amazon Macie, which assists in discovering and securing sensitive data that is stored in Amazon S3. • If you require FIPS 140-3 validated cryptographic modules when accessing AWS through a command line interface or an API, use a FIPS endpoint. For more information about the available FIPS endpoints, see Federal Information Processing Standard (FIPS) 140-3. We strongly recommend that you never put conﬁdential or sensitive information, such as your customers' email addresses, into tags or free-form text ﬁelds such as a Name ﬁeld. This includes when you work with Amazon Transcribe or other AWS services using the console, API, AWS CLI, or AWS SDKs. Any data that you enter into tags or free-form text ﬁelds used for names may be used for billing or diagnostic logs. If you provide a URL to an external server, we strongly recommend that you do not include credentials information in the URL to validate your request to that server. Inter-network traﬃc privacy An Amazon Virtual Private Cloud (Amazon VPC) endpoint for Amazon Transcribe is a logical entity within a VPC that allows connectivity only to Amazon Transcribe. Amazon VPC routes requests to Amazon Transcribe and routes responses back to the VPC. For more information, see AWS PrivateLink concepts. For information about using Amazon VPC endpoints with Amazon Transcribe see Amazon Transcribe and interface VPC endpoints (AWS PrivateLink). Inter-network traﬃc privacy 527 Amazon Transcribe Data encryption Developer Guide Data encryption refers to protecting data while in transit and at rest. You can protect your data by using Amazon S3-managed keys or KMS keys at rest, alongside standard Transport Layer Security (TLS) while in transit. Encryption at rest Amazon Transcribe uses the default Amazon S3 key (SSE-S3) for server-side encryption of transcripts placed in your Amazon S3 bucket. When you use the StartTranscriptionJob operation, you can specify your own KMS key to encrypt the output from a transcription job. Amazon Transcribe uses an Amazon EBS volume encrypted with the default key. Encryption in transit Amazon Transcribe uses TLS 1.2 with AWS certiﬁcates to encrypt data in transit. This includes streaming transcriptions. Key management Amazon Transcribe works with KMS keys to provide enhanced encryption for your data. With Amazon S3, you can encrypt your input media when creating a transcription job. Integration with AWS KMS allows encryption of the output from a StartTranscriptionJob request. If you don't specify a KMS key, the output of the transcription job is encrypted with the default Amazon S3 key (SSE-S3). For more information on AWS KMS, see the AWS Key Management Service Developer Guide. Key management using the AWS Management Console To encrypt the output of your transcription job, you can choose between using a KMS key for the AWS account that is making the request, or a KMS key from another AWS account. If you don't specify a KMS key, the output of the transcription job is encrypted with the default Amazon S3 key (SSE-S3). To enable output encryption: 1. Under Output data choose Encryption. Data encryption 528 Amazon Transcribe Developer Guide 2. Choose whether the KMS key is from the AWS account you're currently using or from a diﬀerent AWS account. If you want to use a key from the current AWS account, choose the key from KMS key ID. If you're using a key from a diﬀerent AWS account, you must enter the key's ARN. To use a key from a diﬀerent AWS account, the caller must have kms:Encrypt permissions for the KMS key. Refer to Creating a key policy for more information. Key management using the API To use output encryption with the API, you must specify your KMS key using the OutputEncryptionKMSKeyId parameter of the StartCallAnalyticsJob, StartMedicalTranscriptionJob, or StartTranscriptionJob operation. If using a key located in the current
transcribe-dg-113	transcribe-dg.pdf	113	If you want to use a key from the current AWS account, choose the key from KMS key ID. If you're using a key from a diﬀerent AWS account, you must enter the key's ARN. To use a key from a diﬀerent AWS account, the caller must have kms:Encrypt permissions for the KMS key. Refer to Creating a key policy for more information. Key management using the API To use output encryption with the API, you must specify your KMS key using the OutputEncryptionKMSKeyId parameter of the StartCallAnalyticsJob, StartMedicalTranscriptionJob, or StartTranscriptionJob operation. If using a key located in the current AWS account, you can specify your KMS key in one of four ways: 1. Use the KMS key ID itself. For example, 1234abcd-12ab-34cd-56ef-1234567890ab. 2. Use an alias for the KMS key ID. For example, alias/ExampleAlias. 3. Use the Amazon Resource Name (ARN) for the KMS key ID. For example, arn:aws:kms:region:account-ID:key/1234abcd-12ab-34cd-56ef-1234567890ab. 4. Use the ARN for the KMS key alias. For example, arn:aws:kms:region:account-ID:alias/ ExampleAlias. If using a key located in a diﬀerent AWS account than the current AWS account, you can specify your KMS key in one of two ways: 1. Use the ARN for the KMS key ID. For example, arn:aws:kms:region:account- ID:key/1234abcd-12ab-34cd-56ef-1234567890ab. 2. Use the ARN for the KMS key alias. For example, arn:aws:kms:region:account-ID:alias/ ExampleAlias. Data encryption 529 Amazon Transcribe Developer Guide Note that the entity making the request must have permission to use the speciﬁed KMS key. AWS KMS encryption context AWS KMS encryption context is a map of plain text, non-secret key:value pairs. This map represents additional authenticated data, known as encryption context pairs, which provide an added layer of security for your data. Amazon Transcribe requires a symmetric encryption key to encrypt transcription output into a customer-speciﬁed Amazon S3 bucket. To learn more, see Asymmetric keys in AWS KMS. When creating your encryption context pairs, do not include sensitive information. Encryption context is not secret—it's visible in plain text within your CloudTrail logs (so you can use it to identify and categorize your cryptographic operations). Your encryption context pair can include special characters, such as underscores (_), dashes (-), slashes (/, \) and colons (:). Tip It can be useful to relate the values in your encryption context pair to the data being encrypted. Although not required, we recommend you use non-sensitive metadata related to your encrypted content, such as ﬁle names, header values, or unencrypted database ﬁelds. To use output encryption with the API, set the KMSEncryptionContext parameter in the StartTranscriptionJob operation. In order to provide encryption context for the output encryption operation, the OutputEncryptionKMSKeyId parameter must reference a symmetric KMS key ID. You can use AWS KMS condition keys with IAM policies to control access to a symmetric encryption KMS key based on the encryption context that was used in the request for a cryptographic operation. For an example encryption context policy, see AWS KMS encryption context policy. Using encryption context is optional, but recommended. For more information, see Encryption context. Data encryption 530 Amazon Transcribe Developer Guide Opting out of using your data for service improvement By default, Amazon Transcribe stores and uses voice inputs that it has processed to develop the service and continuously improve your experience. You can opt out of having your content used to develop and improve Amazon Transcribe by using an AWS Organizations opt-out policy. For information about how to opt out, see AI services opt-out policies. Monitoring Amazon Transcribe Monitoring is an important part of maintaining the reliability, availability, and performance of Amazon Transcribe and your other AWS solutions. AWS provides the following monitoring tools to watch Amazon Transcribe, report when something is wrong, and take automatic actions when appropriate: • Amazon CloudWatch monitors your AWS resources and the applications that you run on AWS in real time. You can collect and track metrics, create customized dashboards, and set alarms that notify you or take actions when a speciﬁed metric reaches a threshold that you specify. For example, you can have CloudWatch track CPU usage or other metrics on your Amazon EC2 instances and automatically launch new instances when needed. • Amazon CloudWatch Logs can monitor, store, and access your log ﬁles from Amazon EC2 instances, CloudTrail, and other sources. CloudWatch Logs can monitor information in the log ﬁles and notify you when certain thresholds are met. You can also archive your log data in highly durable storage. • AWS CloudTrail captures API calls and related events made by or on behalf of your AWS account and delivers the log ﬁles to an Amazon S3 bucket that you specify. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred. For more information, see the Amazon CloudWatch User Guide. Amazon EventBridge is a serverless service
transcribe-dg-114	transcribe-dg.pdf	114	other sources. CloudWatch Logs can monitor information in the log ﬁles and notify you when certain thresholds are met. You can also archive your log data in highly durable storage. • AWS CloudTrail captures API calls and related events made by or on behalf of your AWS account and delivers the log ﬁles to an Amazon S3 bucket that you specify. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred. For more information, see the Amazon CloudWatch User Guide. Amazon EventBridge is a serverless service that uses events to connect application components together, making it easier for you to build scalable event-driven applications. EventBridge delivers a stream of real-time data from your own applications, Software as a Service (SaaS) applications, and AWS services and routes that data to targets such as Lambda. You can monitor events that happen in services, and build event-driven architectures. For more information, see the Amazon EventBridge User Guide. Opting out of using your data for service improvement 531 Amazon Transcribe Topics • Monitoring Amazon Transcribe with Amazon CloudWatch • Monitoring Amazon Transcribe with AWS CloudTrail • Using Amazon EventBridge with Amazon Transcribe Developer Guide Monitoring Amazon Transcribe with Amazon CloudWatch You can monitor Amazon Transcribe using CloudWatch, which collects raw data and processes it into readable, near real-time metrics. These statistics are kept for 15 months, so that you can access historical information and gain a better perspective on how your web application or service is performing. You can also set alarms that watch for certain thresholds, and send notiﬁcations or take actions when those thresholds are met. For more information, see the CloudWatch User Guide. Using Amazon CloudWatch metrics and dimensions with Amazon Transcribe Amazon Transcribe supports CloudWatch metrics and dimensions, which are data that can help you monitor performance. Supported metrics categories include traﬃc, errors, data transfer, and latency associated with your transcription jobs. Supported metrics are located through CloudWatch in the AWS/Transcribe namespace. Note CloudWatch monitoring metrics are free of charge and don't count against CloudWatch service quotas. For more information on CloudWatch metrics, see Using Amazon CloudWatch metrics. Monitoring Amazon Transcribe with AWS CloudTrail Amazon Transcribe is integrated with AWS CloudTrail, a service that provides a record of actions taken in Amazon Transcribe by an AWS Identity and Access Management (IAM) user or role, or by an AWS service. CloudTrail captures all API calls for Amazon Transcribe. That includes calls from the AWS Management Console and code calls to the Amazon Transcribe APIs, as events. By creating a trail, you can enable continuous delivery of CloudTrail events, including events for Amazon Transcribe, to an Amazon S3 bucket. If you don't create a trail, you can still view the most recent events in the CloudTrail AWS Management Console in Event history. Using the information Monitoring with CloudWatch 532 Amazon Transcribe Developer Guide collected by CloudTrail, you can see each request that is made to Amazon Transcribe, the IP address from which the request is made, who made the request, when it is made, and additional details. To learn more about CloudTrail, refer to the AWS CloudTrail User Guide. Amazon Transcribe and CloudTrail CloudTrail is enabled on your AWS account when you create the account. When activity occurs in Amazon Transcribe, that activity is recorded in a CloudTrail event along with other AWS service events in the CloudTrail Event history. You can view, search, and download recent events in your AWS account. For more information, see Viewing Events with CloudTrail Event History. To get an ongoing record of events in your AWS account, including events for Amazon Transcribe, create a trail. A trail is a conﬁguration that enables CloudTrail to deliver events as log ﬁles to a speciﬁed Amazon S3 bucket. CloudTrail log ﬁles contain one or more log entries. An event represents a single request from any source. It includes information about the requested action, the date and time of the action, request parameters, and so on. CloudTrail log ﬁles aren't an ordered stack trace of the public API calls, so they don't appear in any speciﬁc order. By default, when you create a trail in the AWS Management Console, the trail applies to all AWS Regions. The trail logs events from all AWS Regions in the AWS partition and delivers the log ﬁles to the Amazon S3 bucket that you specify. Additionally, you can conﬁgure other AWS services to further analyze and act upon the event data collected in CloudTrail logs. For more information, see: • Overview for Creating a Trail • CloudTrail Supported Services and Integrations • Conﬁguring Amazon SNS Notiﬁcations for CloudTrail • Receiving CloudTrail Log Files from Multiple Regions and Receiving CloudTrail Log Files from Multiple Accounts CloudTrail logs all Amazon Transcribe actions,
transcribe-dg-115	transcribe-dg.pdf	115	AWS Management Console, the trail applies to all AWS Regions. The trail logs events from all AWS Regions in the AWS partition and delivers the log ﬁles to the Amazon S3 bucket that you specify. Additionally, you can conﬁgure other AWS services to further analyze and act upon the event data collected in CloudTrail logs. For more information, see: • Overview for Creating a Trail • CloudTrail Supported Services and Integrations • Conﬁguring Amazon SNS Notiﬁcations for CloudTrail • Receiving CloudTrail Log Files from Multiple Regions and Receiving CloudTrail Log Files from Multiple Accounts CloudTrail logs all Amazon Transcribe actions, which are documented in the API Reference. For example, the CreateVocabulary, GetTranscriptionJob, and StartTranscriptionJob operations generate entries in the CloudTrail log ﬁles. When CloudTrail logs Amazon Transcribe API operations, the CloudTrail log entry uses empty strings for sensitive information in the request and response parameters, such as Amazon S3 URI values. Every event or log entry contains information about who generated the request. This information helps you determine the following: Monitoring Amazon Transcribe with CloudTrail 533 Amazon Transcribe Developer Guide • Whether the request is made with root or IAM user credentials • Whether the request is made with temporary security credentials for an IAM role or federated user • Whether the request is made by another AWS service For more information, see the CloudTrail userIdentity Element. You can also aggregate Amazon Transcribe log ﬁles from multiple AWS Regions and multiple AWS accounts into a single Amazon S3 bucket. For more information, see Receiving CloudTrail Log Files from Multiple Regions and Receiving CloudTrail Log Files from Multiple Accounts. Example: Amazon Transcribe log ﬁle entries A trail is a conﬁguration that enables delivery of events as log ﬁles to a speciﬁed Amazon S3 bucket. CloudTrail log ﬁles contain one or more log entries. An event represents a single request from any source. It includes such information about the requested action as the date and time of the action, and request parameters. CloudTrail log ﬁles aren't an ordered stack trace of the public API calls, so they don't appear in any speciﬁc order. Calls to the StartTranscriptionJob and GetTranscriptionJob API operations create the following entry. Note When CloudTrail logs Amazon Transcribe API operations, the CloudTrail log entry uses empty strings for sensitive information in the request and response parameters, such as Amazon S3 URI values. { "Records": [ { "eventVersion": "1.05", "userIdentity": { "type": "IAMUser", "principalId": "111122223333", "arn": "arn:aws:iam:us-west-2:111122223333:user/my-user-name", "accountId": "111122223333", "accessKeyId": "AKIAIOSFODNN7EXAMPLE", "userName": "my-user-name" Monitoring Amazon Transcribe with CloudTrail 534 Developer Guide Amazon Transcribe }, "eventTime": "2022-03-07T15:03:45Z", "eventSource": "transcribe.amazonaws.com", "eventName": "StartTranscriptionJob", "awsRegion": "us-west-2", "sourceIPAddress": "127.0.0.1", "userAgent": "[]", "requestParameters": { "mediaFormat": "flac", "languageCode": "en-US", "transcriptionJobName": "my-first-transcription-job", "media": { "mediaFileUri": "" } }, "responseElements": { "transcriptionJob": { "transcriptionJobStatus": "IN_PROGRESS", "mediaFormat": "flac", "creationTime": "2022-03-07T15:03:44.229000-08:00", "transcriptionJobName": "my-first-transcription-job", "languageCode": "en-US", "media": { "mediaFileUri": "" } } }, "requestID": "47B8E8D397DCE7A6", "eventID": "cdc4b7ed-e171-4cef-975a-ad829d4123e8", "eventType": "AwsApiCall", "recipientAccountId": "111122223333" }, { "eventVersion": "1.05", "userIdentity": { "type": "IAMUser", "principalId": "111122223333", "arn": "arn:aws:iam:us-west-2:111122223333:user/my-user-name", "accountId": "111122223333", "accessKeyId": "AKIAIOSFODNN7EXAMPLE", "userName": "my-user-name" }, "eventTime": "2022-03-07T15:07:11Z", "eventSource": "transcribe.amazonaws.com", Monitoring Amazon Transcribe with CloudTrail 535 Amazon Transcribe Developer Guide "eventName": "GetTranscriptionJob", "awsRegion": "us-west-2", "sourceIPAddress": "127.0.0.1", "userAgent": "[]", "requestParameters": { "transcriptionJobName": "my-first-transcription-job" }, "responseElements": { "transcriptionJob": { "settings": { }, "transcriptionJobStatus": "COMPLETED", "mediaFormat": "flac", "creationTime": "2022-03-07T15:03:44.229000-08:00", "transcriptionJobName": "my-first-transcription-job", "languageCode": "en-US", "media": { "mediaFileUri": "" }, "transcript": { "transcriptFileUri": "" } } }, "requestID": "BD8798EACDD16751", "eventID": "607b9532-1423-41c7-b048-ec2641693c47", "eventType": "AwsApiCall", "recipientAccountId": "111122223333" } ] } Using Amazon EventBridge with Amazon Transcribe With Amazon EventBridge, you can respond to state changes in your Amazon Transcribe jobs by initiating events in other AWS services. When a transcription job changes state, EventBridge automatically sends an event to an event stream. You create rules that deﬁne the events that you want to monitor in the event stream and the action that EventBridge should take when those events occur. For example, routing the event to another service (or target), which can then take an action. You could, for example, conﬁgure a rule to route an event to an AWS Lambda function Using Amazon EventBridge with Amazon Transcribe 536 Amazon Transcribe Developer Guide when a transcription job has completed successfully. To deﬁne EventBridge rules, refer to the following sections. You can receive notiﬁcations for events through multiple channels, including email, Amazon Q Developer in chat applications chat notiﬁcations, or AWS Console Mobile Application push notiﬁcations. You can also see notiﬁcations in the Console Notiﬁcations Center. If you want to set up notiﬁcations, you can use AWS User Notiﬁcations. AWS User Notiﬁcations supports aggregation, which can reduce the number of notiﬁcations you receive during speciﬁc events. Deﬁning EventBridge rules To deﬁne EventBridge rules, use the AWS Management Console. When you deﬁne a rule, use Amazon Transcribe as the service name. For an example of how to create an EventBridge rule, see Amazon EventBridge rules. Before using EventBridge,
transcribe-dg-116	transcribe-dg.pdf	116	multiple channels, including email, Amazon Q Developer in chat applications chat notiﬁcations, or AWS Console Mobile Application push notiﬁcations. You can also see notiﬁcations in the Console Notiﬁcations Center. If you want to set up notiﬁcations, you can use AWS User Notiﬁcations. AWS User Notiﬁcations supports aggregation, which can reduce the number of notiﬁcations you receive during speciﬁc events. Deﬁning EventBridge rules To deﬁne EventBridge rules, use the AWS Management Console. When you deﬁne a rule, use Amazon Transcribe as the service name. For an example of how to create an EventBridge rule, see Amazon EventBridge rules. Before using EventBridge, note the following deﬁnitions: • Event–An event indicates a change in the state of one of your transcription jobs. For example, when the TranscriptionJobStatus of a job changes from IN_PROGRESS to COMPLETED. • Target–A target is another AWS service that processes an event. For example, AWS Lambda or Amazon Simple Notiﬁcation Service (Amazon SNS). A target receives events in JSON format. • Rule–A rule matches incoming events that you want EventBridge to watch for and routes them to a target or targets for processing. If a rule routes an event to multiple targets, all of the targets process the event in parallel. A rule can customize the JSON sent to the target. Amazon EventBridge events are emitted on a best-eﬀort basis. For more information about creating and managing events in EventBridge, see Amazon EventBridge events in the Amazon EventBridge User Guide. The following is an example of an EventBridge rule for Amazon Transcribe that's initiated when a transcription job's status changes to COMPLETED or FAILED. { "source": [ "aws.transcribe" ], "detail-type": [ "Transcribe Job State Change" Using Amazon EventBridge with Amazon Transcribe 537 Developer Guide Amazon Transcribe ], "detail": { "TranscriptionJobStatus": [ "COMPLETED", "FAILED" ] } } The rule contains the following ﬁelds: • source–The source of the event. For Amazon Transcribe, this is always aws.transcribe. • detail-type–An identiﬁer for the details of the event. For Amazon Transcribe, this is always Transcribe Job State Change. • detail–The new job status of the transcription job. In this example, the rule initiates an event when the job status changes to COMPLETED or FAILED. Amazon Transcribe events Amazon EventBridge logs several Amazon Transcribe events: • Transcription job events • Language identiﬁcation events • Call Analytics events • Call Analytics post-call events • Vocabulary events These events all contain the following shared ﬁelds: • version: The version of the event data. This value is always 0. • id: A unique identiﬁer generated by EventBridge for the event. • detail-type: An identiﬁer for the details of the event. For example, Transcribe Job State Change. • source: The source of the event. For Amazon Transcribe this is always aws.transcribe. • account: The AWS account ID of the account that generated the API call. Using Amazon EventBridge with Amazon Transcribe 538 Amazon Transcribe Developer Guide • time: The date and time the event is delivered. • region: The AWS Region in which the request is made. • resources: The resources used by the API call. For Amazon Transcribe, this ﬁeld is always empty. • detail: Additional details about the event. • FailureReason: This ﬁeld is present if the state or status changes to FAILED, and describes the reason for the FAILED state or status. • Each event type has additional unique ﬁelds that are displayed under detail. These unique ﬁelds are deﬁned in the following sections after each event example. Transcription job events When a job's state changes from IN_PROGRESS to COMPLETED or FAILED, Amazon Transcribe generates an event. To identify the job that changed state and initiate the event in your target, use the event's TranscriptionJobName ﬁeld. An Amazon Transcribe event contains the following information. A FailureReason ﬁeld is added under detail if your transcription job status is FAILED. Note that this event applies only to the StartTranscriptionJob API operation. { "version": "0", "id": "event ID", "detail-type":"Transcribe Job State Change", "source": "aws.transcribe", "account": "111122223333", "time": "timestamp", "region": "us-west-2", "resources": [], "detail": { "TranscriptionJobName": "my-first-transcription-job", "TranscriptionJobStatus": "COMPLETED" (or "FAILED") } } • TranscriptionJobName: The unique name you chose for your transcription job. • TranscriptionJobStatus : The status of the transcription job. This can be COMPLETED or FAILED. Using Amazon EventBridge with Amazon Transcribe 539 Amazon Transcribe Language identiﬁcation events Developer Guide When you enable automatic language identiﬁcation, Amazon Transcribe generates an event when the language identiﬁcation state is COMPLETED or FAILED. To identify the job that changed state and initiate the event in your target, use the event's JobName ﬁeld. An Amazon Transcribe event contains the following information. A FailureReason ﬁeld is added under detail if your language identiﬁcation status is FAILED. Note that this event applies only to the StartTranscriptionJob API operation when the LanguageIdSettings parameter is included. { "version": "0", "id": "event ID", "detail-type": "Language
transcribe-dg-117	transcribe-dg.pdf	117	Using Amazon EventBridge with Amazon Transcribe 539 Amazon Transcribe Language identiﬁcation events Developer Guide When you enable automatic language identiﬁcation, Amazon Transcribe generates an event when the language identiﬁcation state is COMPLETED or FAILED. To identify the job that changed state and initiate the event in your target, use the event's JobName ﬁeld. An Amazon Transcribe event contains the following information. A FailureReason ﬁeld is added under detail if your language identiﬁcation status is FAILED. Note that this event applies only to the StartTranscriptionJob API operation when the LanguageIdSettings parameter is included. { "version": "0", "id": "event ID", "detail-type": "Language Identification State Change", "source": "aws.transcribe", "account": "111122223333", "time": "timestamp", "region": "us-west-2", "resources": [], "detail": { "JobType": "TranscriptionJob", "JobName": "my-first-lang-id-job", "LanguageIdentificationStatus": "COMPLETED" (or "FAILED") } } • JobType: For transcription jobs, this value must be TranscriptionJob. • JobName: The unique name of your transcription job. • LanguageIdentificationStatus: The status of language identiﬁcation in a transcription job. This can be COMPLETED or FAILED. Call Analytics events When a Call Analytics job state changes from IN_PROGRESS to COMPLETED or FAILED, Amazon Transcribe generates an event. To identify the Call Analytics job that changed state and initiates the event in your target, use the event's JobName ﬁeld. An Amazon Transcribe event contains the following information. A FailureReason ﬁeld is added under detail if your Call Analytics job status is FAILED. Using Amazon EventBridge with Amazon Transcribe 540 Amazon Transcribe Developer Guide Note that this event applies only to the StartCallAnalyticsJob API operation. { "version": "0", "id": "event ID", "detail-type": "Call Analytics Job State Change", "source": "aws.transcribe", "account": "111122223333", "time": "timestamp", "region": "us-west-2", "resources": [], "detail": { "JobName": "my-first-analytics-job", "JobStatus": "COMPLETED" (or "FAILED"), "AnalyticsJobDetails": { // only when you enable optional features such as Generative Call Summarization "Skipped": [] } } } • JobName: The unique name of your Call Analytics transcription job. • JobStatus: The status of your Call Analytics transcription job. This can be either COMPLETED or FAILED. • AnalyticsJobDetails: The details of your Call Analytics transcription job, including information about skipped analytics features. Call Analytics post-call events When a post-call analytics transcription changes state from IN_PROGRESS to COMPLETED or FAILED, Amazon Transcribe generates an event. To identify the Call Analytics post-call job that changed state and initiate the event in your target, use the event's StreamingSessionId ﬁeld. Note that this event applies only to the StartCallAnalyticsStreamTranscription API operation when the PostCallAnalyticsSettings parameter is included. A COMPLETED event contains the following information: { "version": "0", "id": "event ID", Using Amazon EventBridge with Amazon Transcribe 541 Amazon Transcribe Developer Guide "detail-type": "Call Analytics Post Call Job State Change", "source": "aws.transcribe", "account": "111122223333", "time": "timestamp", "region": "us-west-2", "resources": [], "detail": { "StreamingSessionId": "session-id", "PostCallStatus": "COMPLETED", "Transcript": { "RedactedTranscriptFileUri": "s3://amzn-s3-demo-bucket/my-output-files/my- redacted-file.JSON", "TranscriptFileUri": "s3://amzn-s3-demo-bucket/my-output-files/my- file.JSON" }, "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-output-files/my-redacted- file.WAV", "RedactedMediaFileUri": "s3://amzn-s3-demo-bucket/my-output-files/my- redacted-file.WAV" } } } A FAILED event contains the following information: { "version": "0", "id": "event ID", "detail-type": "Call Analytics Post Call Job State Change", "source": "aws.transcribe", "account": "111122223333", "time": "timestamp", "region": "us-west-2", "resources": [], "detail": { "StreamingSessionId": "session-id", "PostCallStatus": "FAILED" } } • StreamingSessionId: The identiﬁcation number assigned to your real-time Call Analytics transcription request. Using Amazon EventBridge with Amazon Transcribe 542 Amazon Transcribe Developer Guide • PostCallStatus: The status of your post-call Call Analytics transcription. This can be either COMPLETED or FAILED. • Transcript: The URI of your redacted and unredacted transcripts. • Media: The URI of your redacted and unredacted audio ﬁles. AWS HealthScribe post stream analytics events When a state changes for a AWS HealthScribe post-stream analytics operation, such as a ClinicalNoteGenerationResult changing from IN_PROGRESS to COMPLETED, AWS HealthScribe generates an event with the following information: { "version":"0", "id":"event ID", "detail-type":"MedicalScribe Post Stream Analytics Update", "source":"aws.transcribe", "account":"111122223333", "time":"timestamp", "region":"us-east-1", "resources":[], "detail":{ "SessionId": <SessionID>, "UpdateType": "ClinicalNoteGenerationResult", "ClinicalNoteGenerationResult": { "ClinicalNoteOutputLocation": s3://amzn-s3-demo-bucket/clinical-note-output- files/clinical-notes.JSON, "TranscriptOutputLocation": s3://amzn-s3-demo-bucket/my-output-files/my- file.JSON, "Status": <IN_PROGRESS \| COMPLETED \| FAILED>, "FailureReason": <failure_reason> } } } • UpdateType: The type of post-stream analytics operation that generated the event. The content of the result object varies depending on the UpdateType. • SessionId: The identiﬁcation number for your AWS HealthScribe stream. Use this ID to identify originating streaming session and then ﬁnd the post-stream analytics that generated the event. Using Amazon EventBridge with Amazon Transcribe 543 Amazon Transcribe Developer Guide • Status: The status of the post-stream analytics operation. This can be IN_PROGRESS, COMPLETED, or FAILED. • ClinicalNoteOutputLocation: The URI of the output Amazon S3 bucket for the ClinicalNoteGenerationResult. • TranscriptOutputLocation: The URI of your transcript. Vocabulary events When a custom vocabulary's state changes from PENDING to READY or FAILED, Amazon Transcribe generates an event. To identify the custom vocabulary that changed state and initiate the event in your target, use the event's VocabularyName ﬁeld. An Amazon Transcribe event contains the following information. A FailureReason ﬁeld is added under detail if your custom vocabulary
transcribe-dg-118	transcribe-dg.pdf	118	Transcribe 543 Amazon Transcribe Developer Guide • Status: The status of the post-stream analytics operation. This can be IN_PROGRESS, COMPLETED, or FAILED. • ClinicalNoteOutputLocation: The URI of the output Amazon S3 bucket for the ClinicalNoteGenerationResult. • TranscriptOutputLocation: The URI of your transcript. Vocabulary events When a custom vocabulary's state changes from PENDING to READY or FAILED, Amazon Transcribe generates an event. To identify the custom vocabulary that changed state and initiate the event in your target, use the event's VocabularyName ﬁeld. An Amazon Transcribe event contains the following information. A FailureReason ﬁeld is added under detail if your custom vocabulary state is FAILED. Note This event applies only to the CreateVocabulary API operation. { "version": "0", "id": "event ID", "detail-type": "Vocabulary State Change", "source": "aws.transcribe", "account": "111122223333", "time": "timestamp", "region": "us-west-2", "resources": [], "detail": { "VocabularyName": "unique-vocabulary-name", "VocabularyState": "READY" (or "FAILED") } } • VocabularyName: The unique name of your custom vocabulary. • VocabularyState: The processing state of your custom vocabulary. This can be READY or FAILED. Using Amazon EventBridge with Amazon Transcribe 544 Amazon Transcribe Developer Guide Compliance validation for Amazon Transcribe To learn whether an AWS service is within the scope of speciﬁc compliance programs, see AWS services in Scope by Compliance Program and choose the compliance program that you are interested in. For general information, see AWS Compliance Programs. You can download third-party audit reports using AWS Artifact. For more information, see Downloading Reports in AWS Artifact. Your compliance responsibility when using AWS services is determined by the sensitivity of your data, your company's compliance objectives, and applicable laws and regulations. AWS provides the following resources to help with compliance: • Security Compliance & Governance – These solution implementation guides discuss architectural considerations and provide steps for deploying security and compliance features. • HIPAA Eligible Services Reference – Lists HIPAA eligible services. Not all AWS services are HIPAA eligible. • AWS Compliance Resources – This collection of workbooks and guides might apply to your industry and location. • AWS Customer Compliance Guides – Understand the shared responsibility model through the lens of compliance. The guides summarize the best practices for securing AWS services and map the guidance to security controls across multiple frameworks (including National Institute of Standards and Technology (NIST), Payment Card Industry Security Standards Council (PCI), and International Organization for Standardization (ISO)). • Evaluating Resources with Rules in the AWS Conﬁg Developer Guide – The AWS Conﬁg service assesses how well your resource conﬁgurations comply with internal practices, industry guidelines, and regulations. • AWS Security Hub – This AWS service provides a comprehensive view of your security state within AWS. Security Hub uses security controls to evaluate your AWS resources and to check your compliance against security industry standards and best practices. For a list of supported services and controls, see Security Hub controls reference. • Amazon GuardDuty – This AWS service detects potential threats to your AWS accounts, workloads, containers, and data by monitoring your environment for suspicious and malicious activities. GuardDuty can help you address various compliance requirements, like PCI DSS, by meeting intrusion detection requirements mandated by certain compliance frameworks. Compliance validation 545 Amazon Transcribe Developer Guide • AWS Audit Manager – This AWS service helps you continuously audit your AWS usage to simplify how you manage risk and compliance with regulations and industry standards. Resilience in Amazon Transcribe The AWS global infrastructure is built around AWS Regions and Availability Zones. AWS Regions provide multiple physically separated and isolated Availability Zones, which are connected with low-latency, high-throughput, and highly redundant networking. With Availability Zones, you can design and operate applications and databases that automatically fail over between zones without interruption. Availability Zones are more highly available, fault tolerant, and scalable than traditional single or multiple data center infrastructures. For more information about AWS Regions and Availability Zones, see AWS Global Infrastructure. Infrastructure security in Amazon Transcribe As a managed service, Amazon Transcribe is protected by AWS global network security. For information about AWS security services and how AWS protects infrastructure, see AWS Cloud Security. To design your AWS environment using the best practices for infrastructure security, see Infrastructure Protection in Security Pillar AWS Well‐Architected Framework. You use AWS published API calls to access Amazon Transcribe through the network. Clients must support the following: • Transport Layer Security (TLS). We require TLS 1.2 and recommend TLS 1.3. • Cipher suites with perfect forward secrecy (PFS) such as DHE (Ephemeral Diﬃe-Hellman) or ECDHE (Elliptic Curve Ephemeral Diﬃe-Hellman). Most modern systems such as Java 7 and later support these modes. Additionally, requests must be signed by using an access key ID and a secret access key that is associated with an IAM principal. Or you can use the AWS Security Token Service (AWS STS) to generate temporary security credentials to sign requests. Vulnerability analysis and
transcribe-dg-119	transcribe-dg.pdf	119	access Amazon Transcribe through the network. Clients must support the following: • Transport Layer Security (TLS). We require TLS 1.2 and recommend TLS 1.3. • Cipher suites with perfect forward secrecy (PFS) such as DHE (Ephemeral Diﬃe-Hellman) or ECDHE (Elliptic Curve Ephemeral Diﬃe-Hellman). Most modern systems such as Java 7 and later support these modes. Additionally, requests must be signed by using an access key ID and a secret access key that is associated with an IAM principal. Or you can use the AWS Security Token Service (AWS STS) to generate temporary security credentials to sign requests. Vulnerability analysis and management in Amazon Transcribe Conﬁguration and IT controls are a shared responsibility between AWS and you, our customer. For more information, see the AWS shared responsibility model. Resilience 546 Amazon Transcribe Developer Guide Amazon Transcribe and interface VPC endpoints (AWS PrivateLink) You can establish a private connection between your VPC and Amazon Transcribe by creating an interface VPC endpoint. Interface endpoints are powered by AWS PrivateLink, a technology that you can use to privately access Amazon Transcribe APIs without an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. Instances in your VPC don't need public IP addresses to communicate with Amazon Transcribe APIs. Traﬃc between your VPC and Amazon Transcribe does not leave the Amazon network. Each interface endpoint is represented by one or more Elastic Network Interfaces in your subnets. For more information, see Interface VPC endpoints (AWS PrivateLink) in the Amazon VPC User Guide. Considerations for Amazon Transcribe VPC endpoints Before you set up an interface VPC endpoint for Amazon Transcribe, make sure that you review Interface endpoint properties and limitations in the Amazon VPC User Guide. Amazon Transcribe supports making calls to all of its API actions from your VPC. Creating an interface VPC endpoint for Amazon Transcribe You can create a VPC endpoint for the Amazon Transcribe service using the Amazon VPC AWS Management Console or AWS CLI. For more information, see Creating an interface endpoint in the Amazon VPC User Guide. For batch transcriptions in Amazon Transcribe, create a VPC endpoint using the following service name: • com.amazonaws.us-west-2.transcribe For streaming transcriptions in Amazon Transcribe, create a VPC endpoint using the following service name: • com.amazonaws.us-west-2.transcribestreaming If you enable private DNS for the endpoint, you can make API requests to Amazon Transcribe using its default DNS name for the AWS Region, for example, transcribestreaming.us- east-2.amazonaws.com. VPC endpoints (AWS PrivateLink) 547 Amazon Transcribe Developer Guide For more information, see Accessing a service through an interface endpoint in the Amazon VPC User Guide. Creating a VPC endpoint policy for Amazon Transcribe You can attach an endpoint policy to your VPC endpoint that controls access to the streaming service or batch transcription service of Amazon Transcribe. The policy speciﬁes the following information: • The principal that can perform actions. • The actions that can be performed. • The resources on which actions can be performed. For more information, see Controlling access to services with VPC endpoints in the Amazon VPC User Guide. Example: VPC endpoint policy for Amazon Transcribe batch transcription actions The following is an example of an endpoint policy for a batch transcription in Amazon Transcribe. When attached to an endpoint, this policy grants access to the listed Amazon Transcribe actions for all principals on all resources. { "Statement":[ { "Principal":"", "Effect":"Allow", "Action":[ "transcribe:StartTranscriptionJob", "transcribe:ListTranscriptionJobs" ], "Resource":"" } ] } Example: VPC endpoint policy for Amazon Transcribe streaming transcription actions The following is an example of an endpoint policy for a streaming transcription in Amazon Transcribe. When attached to an endpoint, this policy grants access to the listed Amazon Transcribe actions for all principals on all resources. VPC endpoints (AWS PrivateLink) 548 Amazon Transcribe { "Statement":[ { "Principal":"", "Effect":"Allow", "Action":[ "transcribe:StartStreamTranscription", "transcribe:StartStreamTranscriptionWebsocket" Developer Guide ], "Resource":"" } ] } Shared subnets You cannot create, describe, modify, or delete VPC endpoints in subnets that are shared with you. However, you can use the VPC endpoints in subnets that are shared with you. For information about VPC sharing, see Share your VPC with other accounts in the Amazon Virtual Private Cloud guide. Security best practices for Amazon Transcribe The following best practices are general guidelines and don’t represent a complete security solution. Because these best practices might not be appropriate or suﬃcient for your environment, use them as helpful considerations rather than prescriptions. • Use data encryption, such as AWS KMS encryption context AWS KMS encryption context is a map of plain text, non-secret key:value pairs. This map represents additional authenticated data, known as encryption context pairs, which provide an added layer of security for your data. For more information, refer to AWS KMS encryption context. • Use temporary credentials whenever possible Where possible, use temporary credentials instead of long-term credentials, such as access keys. For
transcribe-dg-120	transcribe-dg.pdf	120	don’t represent a complete security solution. Because these best practices might not be appropriate or suﬃcient for your environment, use them as helpful considerations rather than prescriptions. • Use data encryption, such as AWS KMS encryption context AWS KMS encryption context is a map of plain text, non-secret key:value pairs. This map represents additional authenticated data, known as encryption context pairs, which provide an added layer of security for your data. For more information, refer to AWS KMS encryption context. • Use temporary credentials whenever possible Where possible, use temporary credentials instead of long-term credentials, such as access keys. For scenarios in which you need IAM users with programmatic access and long-term credentials, we recommend that you rotate access keys. Regularly rotating long-term credentials helps you Shared subnets 549 Amazon Transcribe Developer Guide familiarize yourself with the process. This is useful in case you are ever in a situation where you must rotate credentials, such as when an employee leaves your company. We recommend that you use IAM access last used information to rotate and remove access keys safely. For more information, see Rotating access keys and Security best practices in IAM. • Use IAM roles for applications and AWS services that require Amazon Transcribe access Use an IAM role to manage temporary credentials for applications or services that need to access Amazon Transcribe. When you use a role, you don't have to distribute long-term credentials, such as passwords or access keys, to an Amazon EC2 instance or AWS service. IAM roles can supply temporary permissions that applications can use when they make requests to AWS resources. For more information, refer to IAM roles and Common scenarios for roles: Users, applications, and services. • Use tag-based access control You can use tags to control access within your AWS accounts. In Amazon Transcribe. tags can be added to: transcription jobs, custom vocabularies, custom vocabulary ﬁlters, and custom language models. For more information, refer to Tag-based access control. • Use AWS monitoring tools Monitoring is an important part of maintaining the reliability, security, availability, and performance of Amazon Transcribe and your AWS solutions. You can monitor Amazon Transcribe using CloudTrail. For more information, refer to Monitoring Amazon Transcribe with AWS CloudTrail. • Enable AWS Conﬁg AWS Conﬁg can assess, audit, and evaluate the conﬁgurations of your AWS resources. Using AWS Conﬁg, you can review changes in conﬁgurations and relationships between AWS resources. You can also investigate detailed resource conﬁguration histories and determine your overall compliance against the conﬁgurations speciﬁed in your internal guidelines. This can help you simplify compliance auditing, security analysis, change management, and operational troubleshooting. For more information, refer to What Is AWS Conﬁg? Security best practices 550 Amazon Transcribe Developer Guide Amazon Transcribe Medical Amazon Transcribe Medical is an automatic speech recognition (ASR) service designed for medical professionals who want to transcribe medical-related speech, such as physician-dictated notes, drug safety monitoring, telemedicine appointments, or physician-patient conversations. Amazon Transcribe Medical is available through either real-time streaming (via microphone) or transcription of an uploaded ﬁle (batch). Important Amazon Transcribe Medical is not a substitute for professional medical advice, diagnosis, or treatment. Identify the right conﬁdence threshold for your use case, and use high conﬁdence thresholds in situations that require high accuracy. For certain use cases, results should be reviewed and veriﬁed by appropriately trained human reviewers. Amazon Transcribe Medical transcriptions should only be used in patient care scenarios after review for accuracy and sound medical judgment by trained medical professionals. Amazon Transcribe Medical operates under a shared responsibility model, whereby AWS is responsible for protecting the infrastructure that runs Amazon Transcribe Medical and you are responsible for managing your data. For more information, see Shared Responsibility Model. Amazon Transcribe Medical is available in US English (en-US). For best results, use a lossless audio format, such as FLAC or WAV, with PCM 16-bit encoding. Amazon Transcribe Medical supports sample rates of 16,000 Hz or higher. For analysis of your transcripts, you can use other AWS services, such as Amazon Comprehend Medical. Supported specialties Specialty Cardiology Neurology Oncology Sub-specialty Audio input none none none streaming only streaming only streaming only 551 Amazon Transcribe Specialty Primary Care Primary Care Primary Care Developer Guide Sub-specialty Audio input Family Medicine batch, streaming Internal Medicine batch, streaming Obstetrics and Gynecology (OB-GYN) batch, streaming Primary Care Pediatrics batch, streaming Radiology Urology none none streaming only streaming only Region availability and quotas Call Analytics is supported in the following AWS Regions: Region Transcription type af-south-1 (Cape Town) ap-east-1 (Hong Kong) ap-northeast-1 (Tokyo) ap-northeast-2 (Seoul) ap-south-1 (Mumbai) ap-southeast-1 (Singapore) batch batch batch, streaming batch, streaming batch batch ap-southeast-2 (Sydney) batch, streaming ca-central-1 (Canada, Central) batch, streaming eu-central-1 (Frankfurt) batch, streaming eu-north-1 (Stockholm) batch Region availability and quotas 552 Developer Guide Amazon Transcribe Region eu-west-1 (Ireland) eu-west-2 (London) eu-west-3 (Paris) me-south-1
transcribe-dg-121	transcribe-dg.pdf	121	Audio input Family Medicine batch, streaming Internal Medicine batch, streaming Obstetrics and Gynecology (OB-GYN) batch, streaming Primary Care Pediatrics batch, streaming Radiology Urology none none streaming only streaming only Region availability and quotas Call Analytics is supported in the following AWS Regions: Region Transcription type af-south-1 (Cape Town) ap-east-1 (Hong Kong) ap-northeast-1 (Tokyo) ap-northeast-2 (Seoul) ap-south-1 (Mumbai) ap-southeast-1 (Singapore) batch batch batch, streaming batch, streaming batch batch ap-southeast-2 (Sydney) batch, streaming ca-central-1 (Canada, Central) batch, streaming eu-central-1 (Frankfurt) batch, streaming eu-north-1 (Stockholm) batch Region availability and quotas 552 Developer Guide Amazon Transcribe Region eu-west-1 (Ireland) eu-west-2 (London) eu-west-3 (Paris) me-south-1 (Bahrain) sa-east-1 (São Paulo) us-east-1 (N. Virginia) us-east-2 (Ohio) Transcription type batch, streaming batch, streaming batch batch batch, streaming batch, streaming batch, streaming us-gov-east-1 (GovCloud, US-East) batch, streaming us-gov-west-1 (GovCloud, US-West) batch, streaming us-west-1 (San Francisco) batch us-west-2 (Oregon) batch, streaming Note that Region support diﬀers for Amazon Transcribe, Amazon Transcribe Medical, and Call Analytics.. To get the endpoints for each supported Region, see Service endpoints in the AWS General Reference. For a list of quotas that pertain to your transcriptions, refer to the Service quotas in the AWS General Reference. Some quotas can be changed upon request. If the Adjustable column contains 'Yes', you can request an increase. To do so, select the provided link. Medical specialties and terms When creating a medical transcription job, you specify the language, the medical specialty, and the audio type of the source ﬁle. You input US English (en-US) as the language and PRIMARYCARE as the medical specialty. Entering primary care as the value enables you to generate transcriptions from source audio in the following medical specialties: Medical specialties 553 Amazon Transcribe • Family Medicine • Internal Medicine • Obstetrics and Gynecology (OB-GYN) • Pediatrics Developer Guide You have the choice between dictation and conversation for your audio type. Choose dictation for audio ﬁles where the physician is giving a report about a patient visit or procedure. Choose conversation for audio ﬁles that involve a conversation between a physician and a patient or a conversation between physicians. To store the output of your transcription job, select an Amazon S3 bucket that you've already created. For more information on Amazon S3 buckets see Getting Started with Amazon Simple Storage Service. The following are the minimum number of request parameters to enter in the sample JSON. { "MedicalTranscriptionJobName": "my-first-transcription-job", "LanguageCode": "en-US", "Media": { "MediaFileUri": "s3://path to your audio file" }, "OutputBucketName": “your output bucket name", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } Amazon Transcribe Medical enables you to generate alternative transcriptions. For more information, see Generating alternative transcriptions. You can also enable speaker partitioning or identify channels in your audio. For more information, see Enabling speaker partitioning and Transcribing multi-channel audio. Transcribing medical terms and measurements Amazon Transcribe Medical can transcribe medical terms and measurements. Amazon Transcribe Medical outputs abbreviations for spoken terms. For example, "blood pressure" is transcribed as BP. You can ﬁnd a list of conventions that Amazon Transcribe Medical uses for medical terms and Transcribing medical terms and measurements 554 Amazon Transcribe Developer Guide measurements in the table on this page. The Spoken Term column refers to the term spoken in the source audio. The Output column refers to the abbreviation you see in your transcription results. You can see how the terms spoken in source audio correspond to the transcription output here. Term spoken in source audio Abbreviation used in output Example output Centigrade Celsius Fahrenheit grams meters feet kilos kilograms c c cubic centimeter milliliter blood pressure b p X over Y C C F g m ft kg kg cc cc mL BP BP X/Y The patient's temperature is 37.4 C. The patient's temperature is 37.4 C. The patient's temperature is 101 F. A mass of 100 g was extracted from the patient. The patient is 1.8 m tall. The patient is 6 ft tall. The patient weighs 80 kg. The patient weighs 80 kg. Patient received 100 cc of saline solution. Patient received 100 cc of saline solution. Patient excreted 100 mL urine. Patient BP was elevated. Patient BP was elevated. Patient BP was 120/80. Transcribing medical terms and measurements 555 Amazon Transcribe Developer Guide Term spoken in source audio Abbreviation used in output Example output beats per min beats per minute O 2 CO2 BPM BPM O2 CO2 post operation POSTOP post op cat scan Pulse 80 Respiration 17 in and out L ﬁve POSTOP CT Scan P 80 R 17 I/O L5 Patient had atrial ﬁbrillation with heart rate of 160 BPM. Patient had atrial ﬁbrillation with heart rate of 160 BPM. Patient O2 saturation was 98%. Patient required respiratory support for elevated CO2. Patient came for POSTOP evaluation. Patient came for POSTOP evaluation. Patient indication of cerebral hemorrhage required use of CT Scan. Patient vitals were P
transcribe-dg-122	transcribe-dg.pdf	122	Abbreviation used in output Example output beats per min beats per minute O 2 CO2 BPM BPM O2 CO2 post operation POSTOP post op cat scan Pulse 80 Respiration 17 in and out L ﬁve POSTOP CT Scan P 80 R 17 I/O L5 Patient had atrial ﬁbrillation with heart rate of 160 BPM. Patient had atrial ﬁbrillation with heart rate of 160 BPM. Patient O2 saturation was 98%. Patient required respiratory support for elevated CO2. Patient came for POSTOP evaluation. Patient came for POSTOP evaluation. Patient indication of cerebral hemorrhage required use of CT Scan. Patient vitals were P 80, R 17,... Patient vitals were P 80, R 17,... Patient was I/O sinus rhythm Lumbar puncture was performed between L4 and L5 Transcribing medical terms and measurements 556 Amazon Transcribe Transcribing numbers Developer Guide Amazon Transcribe Medical transcribes digits as numbers instead of words. For example, the spoken number "one thousand two hundred forty-two" is transcribed as 1242. Numbers are transcribed according to the following rules. Rule Description Convert cardinal numbers greater than 10 to numbers. • "Fifty ﬁve" > 55 • "a hundred" > 100 • "One thousand and thirty one" > 1031 • "One hundred twenty-three million four hundred ﬁfty six thousand seven hundred eight nine" > 123,456,789 Convert cardinal numbers followed by "million" or "billion" to numbers followed by a word when "million" or "billion" is not followed by a number. • "one hundred million" > 100 million • "one billion" > 1 billion • "two point three million" > 2.3 million Convert ordinal numbers greater than 10 to numbers. • "Forty third" > 43rd • "twenty sixth avenue" > 26th avenue Convert fractions to their numeric format. • "a quarter" > 1/4 Convert numbers less than 10 to digits if there are more than one in a row. • "three sixteenths" > 3/16 • "a half" > 1/2 • "a hundredth" > 1/100 • "three four ﬁve" > 345 • "My phone number is four two ﬁve ﬁve ﬁve ﬁve one two one two" > 4255551212 Decimals are indicated by "dot" or "point." • "three hundred and three dot ﬁve" > 303.5 • "three point twenty three" > 3.23 • "zero point four" > 0.4 Transcribing numbers 557 Amazon Transcribe Rule Developer Guide Description • "point three" > 0.3 Convert the word "percent" after a number to a percent sign. • "twenty three percent" > 23% • "twenty three point four ﬁve percent" > 23.45% Convert the words "dollar," "US dollar," "Australian dollar, "AUD," or "USD" after a number to a dollar symbol before the number. • "one dollar and ﬁfteen cents" > $1.15 • "twenty three USD" > $23 • "twenty three Australian dollars" > $23 Convert the words "pounds," or "milligrams" to "lbs" or "mg". • "twenty three pounds" > 23 lbs • "forty-ﬁve milligrams" > 45 mg Convert the words "rupees," "Indian rupees," or "INR" after a number to rupee sign (₹) before • "twenty three rupees" > ₹23 • "ﬁfty rupees thirty paise" > ₹50.30 the number. Convert times to numbers. • "seven a m eastern standard time" > 7 a.m. eastern standard time • "twelve thirty p m" > 12:30 p.m. Combine years expressed as two digits into four. • "nineteen sixty two" > 1962 • "the year is twenty twelve" > the year is Only valid for the 20th, 21st, and 22nd centuries. 2012 • "twenty nineteen" > 2019 • "twenty one thirty" > 2130 Convert dates to numbers. • "May ﬁfth twenty twelve" > May 5th 2012 • "May ﬁve twenty twelve" > May 5 2012 • "ﬁve May twenty twelve" > 5 May 2012 Separate spans of numbers by the word "to." • "twenty three to thirty seven" > 23 to 37 Transcribing numbers 558 Amazon Transcribe Developer Guide Transcribing a medical conversation You can use Amazon Transcribe Medical to transcribe a medical conversation between a clinician and a patient using either a batch transcription job or a real-time stream. Batch transcription jobs enable you to transcribe audio ﬁles. To ensure that Amazon Transcribe Medical produces transcription results with the highest possible accuracy, you must specify the medical specialty of the clinician in your transcription job or stream. You can transcribe a clinician-patient visit in the following medical specialities: • Cardiology – available in streaming transcription only • Neurology – available in streaming transcription only • Oncology – available in streaming transcription only • Primary Care – includes the following types of medical practice: • Family medicine • Internal medicine • Obstetrics and Gynecology (OB-GYN) • Pediatrics • Urology – available in streaming transcription only You can improve transcription accuracy by using medical custom vocabularies. For information on how medical custom vocabularies work, see Improving transcription accuracy with medical custom vocabularies. By default, Amazon Transcribe Medical returns
transcribe-dg-123	transcribe-dg.pdf	123	stream. You can transcribe a clinician-patient visit in the following medical specialities: • Cardiology – available in streaming transcription only • Neurology – available in streaming transcription only • Oncology – available in streaming transcription only • Primary Care – includes the following types of medical practice: • Family medicine • Internal medicine • Obstetrics and Gynecology (OB-GYN) • Pediatrics • Urology – available in streaming transcription only You can improve transcription accuracy by using medical custom vocabularies. For information on how medical custom vocabularies work, see Improving transcription accuracy with medical custom vocabularies. By default, Amazon Transcribe Medical returns the transcription with the highest conﬁdence level. If you'd like to conﬁgure it to return alternative transcriptions, see Generating alternative transcriptions. For information about how numbers and medical measurements appear in the transcription output, see Transcribing numbers and Transcribing medical terms and measurements. Topics • Transcribing an audio ﬁle of a medical conversation • Transcribing a medical conversation in a real-time stream • Enabling speaker partitioning • Transcribing multi-channel audio Transcribing a medical conversation 559 Amazon Transcribe Developer Guide Transcribing an audio ﬁle of a medical conversation Use a batch transcription job to transcribe audio ﬁles of medical conversations. You can use this to transcribe a clinician-patient dialogue. You can start a batch transcription job in either the StartMedicalTranscriptionJob API or the AWS Management Console. When you start a medical transcription job with the StartMedicalTranscriptionJob API, you specify PRIMARYCARE as the value of the Specialty parameter. AWS Management Console To transcribe a clinician-patient dialogue (AWS Management Console) To use the AWS Management Console to transcribe a clinician-patient dialogue, create a transcription job and choose Conversation for Audio input type. 1. 2. Sign in to the AWS Management Console. In the navigation pane, under Amazon Transcribe Medical, choose Transcription jobs. 3. Choose Create job. 4. On the Specify job details page, under Job settings , specify the following. a. Name – the name of the transcription job. b. Audio input type – Conversation 5. For the remaining ﬁelds, specify the Amazon S3 location of your audio ﬁle and where you want to store the output of your transcription job. 6. Choose Next. 7. Choose Create. API To transcribe a medical conversation using a batch transcription job (API) • For the StartMedicalTranscriptionJob API, specify the following. a. b. For MedicalTranscriptionJobName, specify a name unique in your AWS account. For LanguageCode, specify the language code that corresponds to the language spoken in your audio ﬁle and the language of your vocabulary ﬁlter. Transcribing an audio ﬁle 560 Amazon Transcribe Developer Guide c. d. e. f. For the MediaFileUri parameter of the Media object, specify the name of the audio ﬁle that you want to transcribe. For Specialty, specify the medical specialty of the clinician speaking in the audio ﬁle as PRIMARYCARE. For Type, specify CONVERSATION. For OutputBucketName, specify the Amazon S3 bucket to store the transcription results. The following is an example request that uses the AWS SDK for Python (Boto3) to transcribe a medical conversation of a clinician in the PRIMARYCARE specialty and a patient. from __future__ import print_function import time import boto3 transcribe = boto3.client('transcribe', 'us-west-2') job_name = "my-first-med-transcription-job" job_uri = "s3://amzn-s3-demo-bucket/my-input-files/my-audio-file.flac" transcribe.start_medical_transcription_job( MedicalTranscriptionJobName = job_name, Media = { 'MediaFileUri': job_uri }, OutputBucketName = 'amzn-s3-demo-bucket', OutputKey = 'output-files/', LanguageCode = 'en-US', Specialty = 'PRIMARYCARE', Type = 'CONVERSATION' ) while True: status = transcribe.get_medical_transcription_job(MedicalTranscriptionJobName = job_name) if status['MedicalTranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break print("Not ready yet...") time.sleep(5) print(status) Transcribing an audio ﬁle 561 Amazon Transcribe Developer Guide The following example code shows the transcription results of a clinician-patient conversation. { "jobName": "conversation-medical-transcription-job", "accountId": "111122223333", "results": { "transcripts": [ { "transcript": "... come for a follow up visit today..." } ], "items": [ { ... "start_time": "4.85", "end_time": "5.12", "alternatives": [ { "confidence": "1.0", "content": "come" } ], "type": "pronunciation" }, { "start_time": "5.12", "end_time": "5.29", "alternatives": [ { "confidence": "1.0", "content": "for" } ], "type": "pronunciation" }, { "start_time": "5.29", "end_time": "5.33", "alternatives": [ { "confidence": "0.9955", "content": "a" } Transcribing an audio ﬁle 562 Developer Guide Amazon Transcribe ], "type": "pronunciation" }, { "start_time": "5.33", "end_time": "5.66", "alternatives": [ { "confidence": "0.9754", "content": "follow" } ], "type": "pronunciation" }, { "start_time": "5.66", "end_time": "5.75", "alternatives": [ { "confidence": "0.9754", "content": "up" } ], "type": "pronunciation" }, { "start_time": "5.75", "end_time": "6.02", "alternatives": [ { "confidence": "1.0", "content": "visit" } ] ... }, "status": "COMPLETED" } Transcribing an audio ﬁle 563 Amazon Transcribe AWS CLI Developer Guide To transcribe a medical conversation using a batch transcription job (AWS CLI) • Run the following code. aws transcribe start-medical-transcription-job \ --region us-west-2 \ --cli-input-json file://example-start-command.json The following code shows the contents of example-start-command.json. { "MedicalTranscriptionJobName": "my-first-med-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-audio- file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } Transcribing a
transcribe-dg-124	transcribe-dg.pdf	124	"end_time": "5.75", "alternatives": [ { "confidence": "0.9754", "content": "up" } ], "type": "pronunciation" }, { "start_time": "5.75", "end_time": "6.02", "alternatives": [ { "confidence": "1.0", "content": "visit" } ] ... }, "status": "COMPLETED" } Transcribing an audio ﬁle 563 Amazon Transcribe AWS CLI Developer Guide To transcribe a medical conversation using a batch transcription job (AWS CLI) • Run the following code. aws transcribe start-medical-transcription-job \ --region us-west-2 \ --cli-input-json file://example-start-command.json The following code shows the contents of example-start-command.json. { "MedicalTranscriptionJobName": "my-first-med-transcription-job", "Media": { "MediaFileUri": "s3://amzn-s3-demo-bucket/my-input-files/my-audio- file.flac" }, "OutputBucketName": "amzn-s3-demo-bucket", "OutputKey": "my-output-files/", "LanguageCode": "en-US", "Specialty": "PRIMARYCARE", "Type": "CONVERSATION" } Transcribing a medical conversation in a real-time stream You can transcribe an audio stream of a medical conversation using either the HTTP/2 or WebSocket protocols. For information on how to start a stream using the WebSocket protocol, see Setting up a WebSocket stream. To start an HTTP/2 stream, use the StartMedicalStreamTranscription API. You can transcribe streaming audio in the following medical specialties: • Cardiology • Neurology • Oncology Transcribing a real-time stream 564 Amazon Transcribe • Primary Care • Urology Developer Guide Each medical specialty includes many types of procedures and appointments. Clinicians therefore dictate many diﬀerent types of notes. Use the following examples as guidance to help you specify the value of the specialty URI parameter of the WebSocket request, or the Specialty parameter of the StartMedicalStreamTranscription API: • For electrophysiology or echocardiography consultations, choose CARDIOLOGY. • For medical oncology, surgical oncology, or radiation oncology consultations, choose ONCOLOGY. • For a physician providing a consultation to a patient who had a stroke, either a transient ischemic attack or a cerebrovascular attack, choose NEUROLOGY. • For a consultation around urinary incontinence, choose UROLOGY. • For yearly checkup or urgent care visits, choose PRIMARYCARE. • For inpatient hospitalist visits, choose PRIMARYCARE. • For consultations regarding fertility, tubal ligation, IUD insertion, or abortion, choose PRIMARYCARE. AWS Management Console To transcribe a streaming medical conversation (AWS Management Console) To use the AWS Management Console to transcribe a clinician-patient dialogue in real-time stream, choose the option to transcribe a medical conversation, start the stream, and begin speaking into the microphone. 1. 2. Sign in to the AWS Management Console. In the navigation pane, under Amazon Transcribe Medical, choose Real-time transcription. 3. Choose Conversation. 4. For Medical specialty, choose the clinician's specialty. 5. Choose Start streaming. 6. Speak into the microphone. Transcribing a real-time stream 565 Amazon Transcribe Developer Guide Transcribing a medical conversation in an HTTP/2 stream The following is the syntax for the parameters of an HTTP/2 request. To transcribe an HTTP/2 stream of a medical conversation, use the StartMedicalStreamTranscription API and specify the following: • LanguageCode – The language code. The valid value is en-US • MediaEncoding – The encoding used for the input audio. Valid values are pcm, ogg-opus, and flac. • Specialty – The specialty of the medical professional. • Type – CONVERSATION To improve transcription accuracy of speciﬁc terms in a real-time stream, use a custom vocabulary. To enable a custom vocabulary, set the value of VocabularyName parameter to the name of the custom vocabulary that you want to use. For more information, see Improving transcription accuracy with medical custom vocabularies. To label the speech from diﬀerent speakers, set the ShowSpeakerLabel parameter to true. For more information, see Enabling speaker partitioning. For more information on setting up an HTTP/2 stream to transcribe a medical conversation, see Setting up an HTTP/2 stream. Transcribing a medical conversation in a WebSocket stream You can use a WebSocket request to transcribe a medical conversation. When you make a WebSocket request, you create a presigned URI. This URI contains the information needed to set up the audio stream between your application and Amazon Transcribe Medical. For more information on creating WebSocket requests, see Setting up a WebSocket stream. Use the following template to create your presigned URI. GET wss://transcribestreaming.us-west-2.amazonaws.com:8443/medical-stream- transcription-websocket ?language-code=languageCode &X-Amz-Algorithm=AWS4-HMAC-SHA256 &X-Amz-Credential=AKIAIOSFODNN7EXAMPLE%2F20220208%2Fus- west-2%2Ftranscribe%2Faws4_request &X-Amz-Date=20220208T235959Z Transcribing a real-time stream 566 Developer Guide Amazon Transcribe &X-Amz-Expires=300 &X-Amz-Security-Token=security-token &X-Amz-Signature=Signature Version 4 signature &X-Amz-SignedHeaders=host &media-encoding=flac &sample-rate=16000 &session-id=sessionId &specialty=medicalSpecialty &type=CONVERSATION &vocabulary-name=vocabularyName &show-speaker-label=boolean To improve transcription accuracy of speciﬁc terms in a real-time stream, use a custom vocabulary. To enable a custom vocabulary, set the value of vocabulary-name to the name of the custom vocabulary that you want to use. For more information, see Improving transcription accuracy with medical custom vocabularies. To label the speech from diﬀerent speakers, set the show-speaker-label parameter in to true. For more information, see Enabling speaker partitioning. For more information on creating pre-signed URIs, see Setting up a WebSocket stream. Enabling speaker partitioning To enable speaker partitioning in Amazon Transcribe Medical, use speaker diarization. This enables you to see what the patient said and what the clinician said in the transcription output. When you enable speaker diarization, Amazon Transcribe Medical labels each speaker utterance with a

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