Migrating From Amazon Web Services (AWS) Transcribe to Deepgram

Learn how to migrate from Amazon Web Services (AWS) Transcribe to Deepgram. For developers who are using AWS Transcribe and and want to migrate to Deepgram.

Migrating From Amazon Web Services (AWS) Transcribe to Deepgram

Learn how to migrate from Amazon Web Services (AWS) Transcribe to Deepgram. For developers who are using AWS Transcribe and and want to migrate to Deepgram.

This guide covers migrating your transcription processes from Amazon Web Services (AWS) Transcribe to Deepgram, including key differences, code examples, and best practices.

Getting Started

Before you can use Deepgram, you’ll need to create a Deepgram account. Signup is free and includes $200 in free credit and access to all of Deepgram’s features!

Before you start, you’ll need to follow the steps in the Make Your First API Request guide to obtain a Deepgram API key, and configure your environment if you are choosing to use a Deepgram SDK.

Migration Process

During the migration process, you will need to perform the following tasks:

Before Migration	During Migration	After Migration
- Identify any upstream dependencies on your transcriptions - Find representative samples of your audio for testing - Get familiar with Deepgram’s API and understand differences from Amazon - Create an API key - Test your audio - Create a migration plan - Create a rollback plan	- Configure your response parsing to conform to Deepgram’s JSON response - Swap over traffic to Deepgram API - Monitor systems	- Testing - Tune downstream processes to Deepgram output

Differences

Once you’ve selected your model, Deepgram provides many features and capabilities to help you transcribe and classify your audio. However, some capabilities and concepts are implemented differently from Amazon Transcribe.

Features and Capabilities	Deepgram	Amazon Transcribe
Nova Models	Yes	No
Enhanced model	Yes	Yes
Base model	Yes	Yes
Use Case or Domain-specific models	8	2
Trained model	Yes	Yes
Batch processing (1 hour of audio)	20 seconds	5 minutes
Streaming processing lag	300-800 ms	1000-5000 ms, or 1-5 s
Maximum number of channels	No limit	2
Search	Yes	Yes (part of Post Call Analytics bundle)
Diarization (separate per speaker)	Up to 16	Up to 5
Noise reduction	Yes	Yes
Custom vocabulary	Yes - Keyword Boosting, trained model	Yes - custom vocabulary, custom language model
Redaction	Yes	Yes
Punctuation	Yes	Yes
Profanity filter	Yes	Yes
Numeral formatting	Yes	Yes
Audio inputs	Supports over 100 file types. Some of these include: - MP3 - MP4 - MP2 - AAC - WAV - FLAC - PCM - M4A - Ogg - Opus - WebM	Batch - AMR - FLAC - MP3 - MP4 - Ogg - WebM - WAVStreaming - FLAC - Opus-encoded audio in an Ogg container - PCM signed 16-bit little-endian audio (note that this does not include WAV)
SDK	Yes: Node.js - .NET - Python - Go	Yes: .NET - Go - Java - JavaScript - PHP - Python - Ruby
Web Sockets	Yes	Yes
Data logging	Yes	Yes

Detailed Description of Differences

General

Both Deepgram and Amazon provide you with the following values by default:

Words
Timing
Confidence

Additionally, Amazon provides the following values by default:

Type

Deepgram Default JSON Response

Interim Result

JSON

1 {
2    "channel":{
3       "alternatives":[
4          {
5             "confidence":0.99121094,
6             "transcript":"section one of az",
7             "words":[
8                {
9                   "confidence":0.9980469,
10                   "end":1.2935,
11                   "start":0.81589997,
12                   "word":"section"
13                },
14                {
15                   "confidence":0.9897461,
16                   "end":1.6517,
17                   "start":1.2935,
18                   "word":"one"
19                },
20                {
21                   "confidence":0.99121094,
22                   "end":1.8507,
23                   "start":1.6517,
24                   "word":"of"
25                },
26                {
27                   "confidence":0.5073242,
28                   "end":1.9999375,
29                   "start":1.8507,
30                   "word":"az"
31                }
32             ]
33          }
34       ]
35    },
36    "channel_index":[
37       0,
38       1
39    ],
40    "duration":1.9999375,
41    "is_final":false,
42    "metadata":{
43       "model_uuid":"4899aa60-f723-4517-9815-2042acc12a82",
44       "request_id":"fbbe3da5-4a23-4fd3-a4eb-95f2fe8672f5"
45    },
46    "speech_final":false,
47    "start":0.0
48 }

Final Result

JSON

1 {
2    "channel":{
3       "alternatives":[
4          {
5             "confidence":0.9941406,
6             "transcript":"section one of az fable a new translation",
7             "words":[
8                {
9                   "confidence":0.9975586,
10                   "end":1.2970455,
11                   "start":0.81813633,
12                   "word":"section"
13                },
14                {
15                   MORE WORDS...
16                },
17                {
18                   "confidence":0.99853516,
19                   "end":4.2314997,
20                   "start":3.7315,
21                   "word":"translation"
22                }
23             ]
24          }
25       ]
26    },
27    "channel_index":[
28       0,
29       1
30    ],
31    "duration":4.4,
32    "is_final":true,
33    "metadata":{
34       "model_uuid":"4899aa60-f723-4517-9815-2042acc12a82",
35       "request_id":"fbbe3da5-4a23-4fd3-a4eb-95f2fe8672f5"
36    },
37    "speech_final":false,
38    "start":0.0
39 }

Use Case or Domain-specific Models

Deepgram and Amazon provide speech recognition models that are pre-trained or tuned to identify the words and phrases unique to a specific use case or domain. Deepgram creates our speech recognition models through transfer learning from our highly-performant general models. It is important to test multiple models to see which one meets the accuracy, performance, and scalability needs for your use case.

For more details on Deepgram models see Model Overview.

Deepgram provides:

General
Phone calls
Meetings
Voicemail
Conversational AI
Finance
Video
Whisper Cloud
Custom Models

Amazon provides:

Standard
Medical
Call Analytics

Search

Deepgram provides acoustic-based search that identifies phrases by audio patterns rather than text matches. Unlike Amazon’s Call Analytics text-based search, this approach finds phrases even when transcription is imperfect, offering higher accuracy.

Deepgram’s Search feature uses a query that allows you to pass in a word or phrase to find and then returns results in the response JSON object. In the JSON response, we return information including the start time and end time of when that phrase was possibly uttered and a confidence rating for each match. So, in the response, you will see the query (the word or phrase you searched for), and then hits (an array of objects that give you the confidence, start, end, and snippet of each possible match to your search). You can include up to 25 search terms per request.

Sample code

You can search for multiple terms individually: search=epistemology&search=warwick

You can search for a phrase. URL-encode the phrase when submitting it: search=social%20epistemology

As an example, we used a WAV audio file that contains the first 20 seconds of a college lecture on epistemology. The term “epistemology” in this audio file is sufficiently technical that our model will not transcribe it accurately, but our phonetic search will still be able to find it.

Input

search=epistemology

Response

JSON

1 "search":[
2   {
3     "query":"epistemology",
4     "hits":[
5       {"confidence":0.9348958,"start":10.725,"end":11.485,"snippet":"is a"},
6       {"confidence":0.9348958,"start":13.965,"end":14.764999,"snippet":"epi"},
7       {"confidence":0.9204282,"start":4.0074897,"end":4.805,"snippet":"social epi"},
8       {"confidence":0.27662036,"start":8.792552,"end":10.365,"snippet":"us today is"},
9       {"confidence":0.1319444,"start":17.205,"end":18.115,"snippet":"nature of knowledge"},
10       {"confidence":0.0885417,"start":15.285,"end":16.085,"snippet":"branch philosophy"},
11       {"confidence":0.045138836,"start":5.1240044,"end":5.722137,"snippet":"university of"},
12       {"confidence":0.045138836,"start":5.6025105,"end":7.4367843,"snippet":"warwick and"},
13       {"confidence":0.0,"start":1.0168257,"end":1.9339627,"snippet":"hello this is steve"},
14       {"confidence":0.0,"start":7.277282,"end":8.27417,"snippet":"and the question"}
15     ]
16   }
17 ]

Amazon Transcribe customers need to upgrade to Post Call Analytics (PCA) to have access to search capabilities. PCA currently supports the following search features:

Search on call attributes such as time range, sentiment, or entities
Search transcriptions

Custom Vocabulary

Both Deepgram and Amazon provide users the ability to improve the accuracy of specific keywords or vocabulary. Deepgram has two methods to perform this: our Keyword Boosting feature and model training. Amazon also has two methods for improving accuracy through custom vocabulary and creating a custom language model using their tools.

Deepgram Keyword Boosting

Deepgram’s Keywords feature improves transcription accuracy by focusing on specific terms you provide with your audio request.

Key points to consider:

You can send up to 200 keywords per API request
When listing keywords, you do not need to provide any “sounds like” spelling hints.
Pro tip: If you want a keyword capitalized in your transcript, capitalize it in the keywords parameter.

Deepgram AI Model Training

For more than 200 unique keywords or domain-specific terms that Keyword Boosting doesn’t handle effectively, upgrade to a trained AI model. Deepgram can train a custom model using your audio samples and transcripts to learn your specific language, accents, and terminology.

Key points to consider:

Deepgram trains models based on human-transcribed transcripts and the associated audio to achieve maximum accuracy.
If you have only audio without transcribed text, we have an expert human transcription team that can transcribe the audio to create an excellent model training dataset.

Amazon Custom Vocabularies

Developers can use Amazon Transcribe 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.

Key points to consider:

You can have up to 100 vocabularies per AWS account.
You can create custom vocabularies in table or list format:
- Table format: You must first upload your custom vocabulary file into an Amazon S3 bucket, then include the Amazon S3 URI of your vocabulary file in your request.
- List format: If using the AWS Management Console, you must save your custom vocabulary file locally, then include the path to your custom vocabulary file in your request; if using the command line (AWS CLI or AWS SDK), you must include your list as comma-separated words within your request.

Amazon Custom Language Models

Amazon provides the option to use your own custom language model. These custom language models learn the context associated with a given word to more accurately transcribe that word. For customers with access to the data and with training and tuning programming expertise, this could be a viable option.

Key considerations:

You must have separate sets of data on hand—one for training and one for tuning.
- 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.
Your data must meet Amazon language, encoding, special character, and file size requirements, or your model training will fail.
- Data must be 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.
- Data must be in plain text format with UTF-8 encoding.
- Data must not contain any special characters or formatting, such as HTML tags.
- Data must be a maximum combined total of 2 GB in size for training data and 200 MB for tuning data.

What to Expect in the JSON Response

Both Deepgram and Amazon will provide you with the following values:

transcript
start_time (duration)
end_time (duration)
word (string)
confidence (float)

In addition, by default, Amazon will provide:

Type (pronunciation or punctuation)

Pro Tip

Avoid transferring over a list of keywords that has been used with a previous vendor.

Every system is different. Whereas Keyword Boosting may have been necessary with another vendor, Deepgram’s system may already perform well without the aid of keywords. Additional boosting may negatively affect results. Start without any keywords, add them as you see fit, and cautiously increase boosts using decimal values until you find the best fit for your data. To learn more about Keyword Boosting, see our Keyword Boosting feature documentation.

Batch Transcription

Both Deepgram and Amazon provide pre-recorded, or batch, transcriptions. In addition to the limit differences for audio length and processing scale (listed below), Deepgram does not require you to store your audio in an Amazon S3 bucket. You can transcribe audio files from your preferred storage location—your laptop, VPC, or third-party cloud.

Amazon maintains the following limits for batch processing:

Maximum audio length of 4 hours
Maximum audio file size of 500 MB (call analytics)-2 GB
Number of concurrent batch transcriptions of 100-250, depending on model

Deepgram maintains the following limits for batch processing:

No limits to audio length
Maximum audio file size of 2 GB per file
Can scale to thousands of concurrent batch transcriptions
Maximum processing time of 10 minutes per file (20 minutes for Whisper)

What to Expect in the JSON Response

Both Deepgram and Amazon will provide you with the following values:

transcript
start_time (duration)
end_time (duration)
word (string)
confidence (float)

Sample Code

Deepgram supports both transcription of files on your local machine and transcription of files stored remotely.

Deepgram

Python

1 # For more Python SDK migration guides, visit:
2 # https://github.com/deepgram/deepgram-python-sdk/tree/main/docs
3 
4 import os
5 from deepgram import DeepgramClient
6 import asyncio, json
7 
8 DEEPGRAM_API_KEY = os.getenv('DEEPGRAM_API_KEY', 'YOUR_API_KEY')
9 PATH_TO_FILE = 'some/file.wav'
10 
11 async def main():
12     # Initializes the Deepgram SDK
13     deepgram = DeepgramClient(api_key=DEEPGRAM_API_KEY)
14     # Open the audio file
15     with open(PATH_TO_FILE, 'rb') as audio:
16         # ...or replace mimetype as appropriate
17         payload = {'buffer': audio}
18         response = await deepgram.listen.prerecorded.v('1').transcribe_file(
19             payload,
20             model='nova-2',
21             punctuate=True
22         )
23         print(json.dumps(response, indent=4))
24 
25 asyncio.run(main())

To see more, visit Deepgram’s Python SDK GitHub repo.

Amazon

Python

1 import time
2 import boto3
3 
4 def transcribe_file(job_name, file_uri, transcribe_client):
5     transcribe_client.start_transcription_job(
6         TranscriptionJobName = job_name,
7         Media = {
8             'MediaFileUri': file_uri
9         },
10         MediaFormat = 'flac',
11         LanguageCode = 'en-US'
12     )
13 
14     max_tries = 60
15     while max_tries > 0:
16         max_tries -= 1
17         job = transcribe_client.get_transcription_job(TranscriptionJobName = job_name)
18         job_status = job['TranscriptionJob']['TranscriptionJobStatus']
19         if job_status in ['COMPLETED', 'FAILED']:
20             print(f"Job {job_name} is {job_status}.")
21             if job_status == 'COMPLETED':
22                 print(
23                     f"Download the transcript from\n"
24                     f"\t{job['TranscriptionJob']['Transcript']['TranscriptFileUri']}.")
25             break
26         else:
27             print(f"Waiting for {job_name}. Current status is {job_status}.")
28         time.sleep(10)
29 
30 def main():
31     transcribe_client = boto3.client('transcribe', region_name = 'us-west-2')
32     file_uri = 's3://DOC-EXAMPLE-BUCKET/my-input-files/my-media-file.flac'
33     transcribe_file('Example-job', file_uri, transcribe_client)
34 
35 if __name__ == '__main__':
36     main()

To see more, visit Amazon Transcribe’s Developer Guide.

Live Streaming

Both APIs provide streaming transcription. Deepgram offers lower latency, higher accuracy, and easier implementation.

When using Deepgram to transcribe live streaming audio, two of the features you can use to further understand your audio include Endpointing and Interim Results. Both of these features monitor incoming live streaming audio and can indicate the end of a type of processing, but they are used in very different ways:

Deepgram live streaming looks for any deviation in the natural flow of speech and returns a finalized response at these places. To learn more about this feature, see Endpointing.
Deepgram live streaming can also return a series of interim transcripts followed by a final transcript. To learn more, see Interim Results.
If your downstream natural language processing (NLP) requires complete utterances, review our Best Practices for Utterance Segmentation.

Endpointing	Interim Results
Identifies deviations in the natural flow of speech and returns a finalized response at these places	Provides preliminary results during live streaming, then sends a definitive transcript of the portion of audio that has been processed so far
Turned on by default	Turned off by default
Returns `speech_final: true` parameter when Deepgram identifies a deviation in the natural flow of speech and the response contains the finalized transcript	Returns `is_final: true` parameter when Deepgram has finished processing audio for a time range and the response contains the finalized transcript for that time range
Can be controlled by adjusting the length of silence required for an endpoint to be detected	Dictated by Deepgram’s internal algorithm

Amazon Transcribe

Amazon Transcribe can perform real-time streaming through the AWS SDKs, HTTP/2, WebSockets, and the AWS Management Console. Amazon supports FLAC, OPUS-encoded audio in an Ogg container, and PCM (16-bit little-endian, audio format, no WAV). If using HTTP/2 or WebSockets, Amazon requires a format called event stream encoding. Amazon streaming is supported in only 12 languages, and Amazon notes that its real-time streaming speed “may have accuracy limitations in some cases.”

Amazon real-time streaming automatically provides partial results, passing along a results field called IsPartial. If IsPartial is set to true, then the transcripted segment is not complete. If IsPartial is set to false, then the transcripted segment is final. Deepgram has a similar interim results field called is_final. With Deepgram, if is_final is set to false, then the results may be improved. If is_final is set to true, then the transcript is final.

If you require lower latency, Amazon requires you to enable Partial Results Stabilization, which can continually change the text until the segment is final. This feature adds a field called Stable to the results. If Stable is set to true, then the text will not change. If Stable is set to false, then the text may change as the segment is transcribed. Turning on Partial Results Stabilization also affects accuracy, depending on the level of stability required. Higher stability transcribes faster but with lower accuracy.

What to Expect in the JSON Response

By default, both Deepgram and Amazon will provide you with the following values:

transcript
start_time (duration)
end_time (duration)
word (string)
confidence (float)

Additionally, Amazon and Deepgram provide the following:

Deepgram	Amazon
- punctuate - interim_results - language - is_final - speech_final	- Type - Stable - VocabularyFilterMatch - IsPartial - ResultID

Configure Environment

We provide sample scripts in Python and Node.js and assume you have already configured either a Python or Node development environment. System requirements will vary depending on the programming language you use:

Pre-recorded	Live Streaming
Node.js - node >= 14.14.37Python - python >= 3.7	Node.js - node >= 14.14.37 - cross-fetch >= 3.1.5Python - python >= 3.7 - aiohttp >= 3.8.1

Get Transcripts and Customize Response

To get transcripts and customize your response, you can use our SDKs and code samples for:

Migration Best Practices

Each API service has different limitations to ensure performance. Known differences between Deepgram and Amazon Transcribe are detailed below, along with information about how our customers address these differences during the migration process.

AWS Capability	Deepgram Recommendations
Job queueing	Job queueing is not required for Deepgram. Due to our architecture built for scalability, we can run as many streams as required for your application. Please speak with us about your requirements.
Language identification	Not currently available.
Custom vocabulary filters	Deepgram’s Find and Replace feature matches this Amazon feature; we can find words that need to be replaced with other words.
Interruption	Not currently analyzed
Loudness	Not currently analyzed
Non-talk time	We provide timestamps for the talk time, so non-talk time can easily be calculated.
Talk speed	Not currently analyzed

This guide covers migrating your transcription processes from Amazon Web Services (AWS) Transcribe to Deepgram, including key differences, code examples, and best practices.

Getting Started

Before you can use Deepgram, you’ll need to create a Deepgram account. Signup is free and includes $200 in free credit and access to all of Deepgram’s features!

Before you start, you’ll need to follow the steps in the Make Your First API Request guide to obtain a Deepgram API key, and configure your environment if you are choosing to use a Deepgram SDK.

Migration Process

During the migration process, you will need to perform the following tasks:

Before Migration	During Migration	After Migration
- Identify any upstream dependencies on your transcriptions - Find representative samples of your audio for testing - Get familiar with Deepgram’s API and understand differences from Amazon - Create an API key - Test your audio - Create a migration plan - Create a rollback plan	- Configure your response parsing to conform to Deepgram’s JSON response - Swap over traffic to Deepgram API - Monitor systems	- Testing - Tune downstream processes to Deepgram output

Differences

Features and Capabilities	Deepgram	Amazon Transcribe
Nova Models	Yes	No
Enhanced model	Yes	Yes
Base model	Yes	Yes
Use Case or Domain-specific models	8	2
Trained model	Yes	Yes
Batch processing (1 hour of audio)	20 seconds	5 minutes
Streaming processing lag	300-800 ms	1000-5000 ms, or 1-5 s
Maximum number of channels	No limit	2
Search	Yes	Yes (part of Post Call Analytics bundle)
Diarization (separate per speaker)	Up to 16	Up to 5
Noise reduction	Yes	Yes
Custom vocabulary	Yes - Keyword Boosting, trained model	Yes - custom vocabulary, custom language model
Redaction	Yes	Yes
Punctuation	Yes	Yes
Profanity filter	Yes	Yes
Numeral formatting	Yes	Yes
Audio inputs	Supports over 100 file types. Some of these include: - MP3 - MP4 - MP2 - AAC - WAV - FLAC - PCM - M4A - Ogg - Opus - WebM	Batch - AMR - FLAC - MP3 - MP4 - Ogg - WebM - WAVStreaming - FLAC - Opus-encoded audio in an Ogg container - PCM signed 16-bit little-endian audio (note that this does not include WAV)
SDK	Yes: Node.js - .NET - Python - Go	Yes: .NET - Go - Java - JavaScript - PHP - Python - Ruby
Web Sockets	Yes	Yes
Data logging	Yes	Yes

Detailed Description of Differences

General

Both Deepgram and Amazon provide you with the following values by default:

Words
Timing
Confidence

Additionally, Amazon provides the following values by default:

Type

Deepgram Default JSON Response

Interim Result

JSON

1 {
2    "channel":{
3       "alternatives":[
4          {
5             "confidence":0.99121094,
6             "transcript":"section one of az",
7             "words":[
8                {
9                   "confidence":0.9980469,
10                   "end":1.2935,
11                   "start":0.81589997,
12                   "word":"section"
13                },
14                {
15                   "confidence":0.9897461,
16                   "end":1.6517,
17                   "start":1.2935,
18                   "word":"one"
19                },
20                {
21                   "confidence":0.99121094,
22                   "end":1.8507,
23                   "start":1.6517,
24                   "word":"of"
25                },
26                {
27                   "confidence":0.5073242,
28                   "end":1.9999375,
29                   "start":1.8507,
30                   "word":"az"
31                }
32             ]
33          }
34       ]
35    },
36    "channel_index":[
37       0,
38       1
39    ],
40    "duration":1.9999375,
41    "is_final":false,
42    "metadata":{
43       "model_uuid":"4899aa60-f723-4517-9815-2042acc12a82",
44       "request_id":"fbbe3da5-4a23-4fd3-a4eb-95f2fe8672f5"
45    },
46    "speech_final":false,
47    "start":0.0
48 }

Final Result

JSON

1 {
2    "channel":{
3       "alternatives":[
4          {
5             "confidence":0.9941406,
6             "transcript":"section one of az fable a new translation",
7             "words":[
8                {
9                   "confidence":0.9975586,
10                   "end":1.2970455,
11                   "start":0.81813633,
12                   "word":"section"
13                },
14                {
15                   MORE WORDS...
16                },
17                {
18                   "confidence":0.99853516,
19                   "end":4.2314997,
20                   "start":3.7315,
21                   "word":"translation"
22                }
23             ]
24          }
25       ]
26    },
27    "channel_index":[
28       0,
29       1
30    ],
31    "duration":4.4,
32    "is_final":true,
33    "metadata":{
34       "model_uuid":"4899aa60-f723-4517-9815-2042acc12a82",
35       "request_id":"fbbe3da5-4a23-4fd3-a4eb-95f2fe8672f5"
36    },
37    "speech_final":false,
38    "start":0.0
39 }

Use Case or Domain-specific Models

For more details on Deepgram models see Model Overview.

Deepgram provides:

General
Phone calls
Meetings
Voicemail
Conversational AI
Finance
Video
Whisper Cloud
Custom Models

Amazon provides:

Standard
Medical
Call Analytics

Search

Sample code

You can search for multiple terms individually: search=epistemology&search=warwick

You can search for a phrase. URL-encode the phrase when submitting it: search=social%20epistemology

Input

search=epistemology

Response

JSON

1 "search":[
2   {
3     "query":"epistemology",
4     "hits":[
5       {"confidence":0.9348958,"start":10.725,"end":11.485,"snippet":"is a"},
6       {"confidence":0.9348958,"start":13.965,"end":14.764999,"snippet":"epi"},
7       {"confidence":0.9204282,"start":4.0074897,"end":4.805,"snippet":"social epi"},
8       {"confidence":0.27662036,"start":8.792552,"end":10.365,"snippet":"us today is"},
9       {"confidence":0.1319444,"start":17.205,"end":18.115,"snippet":"nature of knowledge"},
10       {"confidence":0.0885417,"start":15.285,"end":16.085,"snippet":"branch philosophy"},
11       {"confidence":0.045138836,"start":5.1240044,"end":5.722137,"snippet":"university of"},
12       {"confidence":0.045138836,"start":5.6025105,"end":7.4367843,"snippet":"warwick and"},
13       {"confidence":0.0,"start":1.0168257,"end":1.9339627,"snippet":"hello this is steve"},
14       {"confidence":0.0,"start":7.277282,"end":8.27417,"snippet":"and the question"}
15     ]
16   }
17 ]

Amazon Transcribe customers need to upgrade to Post Call Analytics (PCA) to have access to search capabilities. PCA currently supports the following search features:

Search on call attributes such as time range, sentiment, or entities
Search transcriptions

Custom Vocabulary

Deepgram Keyword Boosting

Deepgram’s Keywords feature improves transcription accuracy by focusing on specific terms you provide with your audio request.

Key points to consider:

You can send up to 200 keywords per API request
When listing keywords, you do not need to provide any “sounds like” spelling hints.
Pro tip: If you want a keyword capitalized in your transcript, capitalize it in the keywords parameter.

Deepgram AI Model Training

Key points to consider:

Deepgram trains models based on human-transcribed transcripts and the associated audio to achieve maximum accuracy.
If you have only audio without transcribed text, we have an expert human transcription team that can transcribe the audio to create an excellent model training dataset.

Amazon Custom Vocabularies

Key points to consider:

You can have up to 100 vocabularies per AWS account.
You can create custom vocabularies in table or list format:
- Table format: You must first upload your custom vocabulary file into an Amazon S3 bucket, then include the Amazon S3 URI of your vocabulary file in your request.
- List format: If using the AWS Management Console, you must save your custom vocabulary file locally, then include the path to your custom vocabulary file in your request; if using the command line (AWS CLI or AWS SDK), you must include your list as comma-separated words within your request.

Amazon Custom Language Models

Key considerations:

You must have separate sets of data on hand—one for training and one for tuning.
- 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.
Your data must meet Amazon language, encoding, special character, and file size requirements, or your model training will fail.
- Data must be 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.
- Data must be in plain text format with UTF-8 encoding.
- Data must not contain any special characters or formatting, such as HTML tags.
- Data must be a maximum combined total of 2 GB in size for training data and 200 MB for tuning data.

What to Expect in the JSON Response

Both Deepgram and Amazon will provide you with the following values:

transcript
start_time (duration)
end_time (duration)
word (string)
confidence (float)

In addition, by default, Amazon will provide:

Type (pronunciation or punctuation)

Pro Tip

Avoid transferring over a list of keywords that has been used with a previous vendor.

Batch Transcription

Amazon maintains the following limits for batch processing:

Maximum audio length of 4 hours
Maximum audio file size of 500 MB (call analytics)-2 GB
Number of concurrent batch transcriptions of 100-250, depending on model

Deepgram maintains the following limits for batch processing:

No limits to audio length
Maximum audio file size of 2 GB per file
Can scale to thousands of concurrent batch transcriptions
Maximum processing time of 10 minutes per file (20 minutes for Whisper)

What to Expect in the JSON Response

Both Deepgram and Amazon will provide you with the following values:

transcript
start_time (duration)
end_time (duration)
word (string)
confidence (float)

Sample Code

Deepgram supports both transcription of files on your local machine and transcription of files stored remotely.

Deepgram

Python

1 # For more Python SDK migration guides, visit:
2 # https://github.com/deepgram/deepgram-python-sdk/tree/main/docs
3 
4 import os
5 from deepgram import DeepgramClient
6 import asyncio, json
7 
8 DEEPGRAM_API_KEY = os.getenv('DEEPGRAM_API_KEY', 'YOUR_API_KEY')
9 PATH_TO_FILE = 'some/file.wav'
10 
11 async def main():
12     # Initializes the Deepgram SDK
13     deepgram = DeepgramClient(api_key=DEEPGRAM_API_KEY)
14     # Open the audio file
15     with open(PATH_TO_FILE, 'rb') as audio:
16         # ...or replace mimetype as appropriate
17         payload = {'buffer': audio}
18         response = await deepgram.listen.prerecorded.v('1').transcribe_file(
19             payload,
20             model='nova-2',
21             punctuate=True
22         )
23         print(json.dumps(response, indent=4))
24 
25 asyncio.run(main())

To see more, visit Deepgram’s Python SDK GitHub repo.

Amazon

Python

1 import time
2 import boto3
3 
4 def transcribe_file(job_name, file_uri, transcribe_client):
5     transcribe_client.start_transcription_job(
6         TranscriptionJobName = job_name,
7         Media = {
8             'MediaFileUri': file_uri
9         },
10         MediaFormat = 'flac',
11         LanguageCode = 'en-US'
12     )
13 
14     max_tries = 60
15     while max_tries > 0:
16         max_tries -= 1
17         job = transcribe_client.get_transcription_job(TranscriptionJobName = job_name)
18         job_status = job['TranscriptionJob']['TranscriptionJobStatus']
19         if job_status in ['COMPLETED', 'FAILED']:
20             print(f"Job {job_name} is {job_status}.")
21             if job_status == 'COMPLETED':
22                 print(
23                     f"Download the transcript from\n"
24                     f"\t{job['TranscriptionJob']['Transcript']['TranscriptFileUri']}.")
25             break
26         else:
27             print(f"Waiting for {job_name}. Current status is {job_status}.")
28         time.sleep(10)
29 
30 def main():
31     transcribe_client = boto3.client('transcribe', region_name = 'us-west-2')
32     file_uri = 's3://DOC-EXAMPLE-BUCKET/my-input-files/my-media-file.flac'
33     transcribe_file('Example-job', file_uri, transcribe_client)
34 
35 if __name__ == '__main__':
36     main()

To see more, visit Amazon Transcribe’s Developer Guide.

Live Streaming

Both APIs provide streaming transcription. Deepgram offers lower latency, higher accuracy, and easier implementation.

Deepgram live streaming looks for any deviation in the natural flow of speech and returns a finalized response at these places. To learn more about this feature, see Endpointing.
Deepgram live streaming can also return a series of interim transcripts followed by a final transcript. To learn more, see Interim Results.
If your downstream natural language processing (NLP) requires complete utterances, review our Best Practices for Utterance Segmentation.

Endpointing	Interim Results
Identifies deviations in the natural flow of speech and returns a finalized response at these places	Provides preliminary results during live streaming, then sends a definitive transcript of the portion of audio that has been processed so far
Turned on by default	Turned off by default
Returns `speech_final: true` parameter when Deepgram identifies a deviation in the natural flow of speech and the response contains the finalized transcript	Returns `is_final: true` parameter when Deepgram has finished processing audio for a time range and the response contains the finalized transcript for that time range
Can be controlled by adjusting the length of silence required for an endpoint to be detected	Dictated by Deepgram’s internal algorithm

Amazon Transcribe

What to Expect in the JSON Response

By default, both Deepgram and Amazon will provide you with the following values:

transcript
start_time (duration)
end_time (duration)
word (string)
confidence (float)

Additionally, Amazon and Deepgram provide the following:

Deepgram	Amazon
- punctuate - interim_results - language - is_final - speech_final	- Type - Stable - VocabularyFilterMatch - IsPartial - ResultID

Configure Environment

Pre-recorded	Live Streaming
Node.js - node >= 14.14.37Python - python >= 3.7	Node.js - node >= 14.14.37 - cross-fetch >= 3.1.5Python - python >= 3.7 - aiohttp >= 3.8.1

Get Transcripts and Customize Response

To get transcripts and customize your response, you can use our SDKs and code samples for:

Migration Best Practices

AWS Capability	Deepgram Recommendations
Job queueing	Job queueing is not required for Deepgram. Due to our architecture built for scalability, we can run as many streams as required for your application. Please speak with us about your requirements.
Language identification	Not currently available.
Custom vocabulary filters	Deepgram’s Find and Replace feature matches this Amazon feature; we can find words that need to be replaced with other words.
Interruption	Not currently analyzed
Loudness	Not currently analyzed
Non-talk time	We provide timestamps for the talk time, so non-talk time can easily be calculated.
Talk speed	Not currently analyzed

1	{
2	"channel":{
3	"alternatives":[
4	{
5	"confidence":0.99121094,
6	"transcript":"section one of az",
7	"words":[
8	{
9	"confidence":0.9980469,
10	"end":1.2935,
11	"start":0.81589997,
12	"word":"section"
13	},
14	{
15	"confidence":0.9897461,
16	"end":1.6517,
17	"start":1.2935,
18	"word":"one"
19	},
20	{
21	"confidence":0.99121094,
22	"end":1.8507,
23	"start":1.6517,
24	"word":"of"
25	},
26	{
27	"confidence":0.5073242,
28	"end":1.9999375,
29	"start":1.8507,
30	"word":"az"
31	}
32	]
33	}
34	]
35	},
36	"channel_index":[
37	0,
38	1
39	],
40	"duration":1.9999375,
41	"is_final":false,
42	"metadata":{
43	"model_uuid":"4899aa60-f723-4517-9815-2042acc12a82",
44	"request_id":"fbbe3da5-4a23-4fd3-a4eb-95f2fe8672f5"
45	},
46	"speech_final":false,
47	"start":0.0
48	}

1	{
2	"channel":{
3	"alternatives":[
4	{
5	"confidence":0.9941406,
6	"transcript":"section one of az fable a new translation",
7	"words":[
8	{
9	"confidence":0.9975586,
10	"end":1.2970455,
11	"start":0.81813633,
12	"word":"section"
13	},
14	{
15	MORE WORDS...
16	},
17	{
18	"confidence":0.99853516,
19	"end":4.2314997,
20	"start":3.7315,
21	"word":"translation"
22	}
23	]
24	}
25	]
26	},
27	"channel_index":[
28	0,
29	1
30	],
31	"duration":4.4,
32	"is_final":true,
33	"metadata":{
34	"model_uuid":"4899aa60-f723-4517-9815-2042acc12a82",
35	"request_id":"fbbe3da5-4a23-4fd3-a4eb-95f2fe8672f5"
36	},
37	"speech_final":false,
38	"start":0.0
39	}

1	"search":[
2	{
3	"query":"epistemology",
4	"hits":[
5	{"confidence":0.9348958,"start":10.725,"end":11.485,"snippet":"is a"},
6	{"confidence":0.9348958,"start":13.965,"end":14.764999,"snippet":"epi"},
7	{"confidence":0.9204282,"start":4.0074897,"end":4.805,"snippet":"social epi"},
8	{"confidence":0.27662036,"start":8.792552,"end":10.365,"snippet":"us today is"},
9	{"confidence":0.1319444,"start":17.205,"end":18.115,"snippet":"nature of knowledge"},
10	{"confidence":0.0885417,"start":15.285,"end":16.085,"snippet":"branch philosophy"},
11	{"confidence":0.045138836,"start":5.1240044,"end":5.722137,"snippet":"university of"},
12	{"confidence":0.045138836,"start":5.6025105,"end":7.4367843,"snippet":"warwick and"},
13	{"confidence":0.0,"start":1.0168257,"end":1.9339627,"snippet":"hello this is steve"},
14	{"confidence":0.0,"start":7.277282,"end":8.27417,"snippet":"and the question"}
15	]
16	}
17	]

1	# For more Python SDK migration guides, visit:
2	# https://github.com/deepgram/deepgram-python-sdk/tree/main/docs
3
4	import os
5	from deepgram import DeepgramClient
6	import asyncio, json
7
8	DEEPGRAM_API_KEY = os.getenv('DEEPGRAM_API_KEY', 'YOUR_API_KEY')
9	PATH_TO_FILE = 'some/file.wav'
10
11	async def main():
12	# Initializes the Deepgram SDK
13	deepgram = DeepgramClient(api_key=DEEPGRAM_API_KEY)
14	# Open the audio file
15	with open(PATH_TO_FILE, 'rb') as audio:
16	# ...or replace mimetype as appropriate
17	payload = {'buffer': audio}
18	response = await deepgram.listen.prerecorded.v('1').transcribe_file(
19	payload,
20	model='nova-2',
21	punctuate=True
22	)
23	print(json.dumps(response, indent=4))
24
25	asyncio.run(main())

1	import time
2	import boto3
3
4	def transcribe_file(job_name, file_uri, transcribe_client):
5	transcribe_client.start_transcription_job(
6	TranscriptionJobName = job_name,
7	Media = {
8	'MediaFileUri': file_uri
9	},
10	MediaFormat = 'flac',
11	LanguageCode = 'en-US'
12	)
13
14	max_tries = 60
15	while max_tries > 0:
16	max_tries -= 1
17	job = transcribe_client.get_transcription_job(TranscriptionJobName = job_name)
18	job_status = job['TranscriptionJob']['TranscriptionJobStatus']
19	if job_status in ['COMPLETED', 'FAILED']:
20	print(f"Job {job_name} is {job_status}.")
21	if job_status == 'COMPLETED':
22	print(
23	f"Download the transcript from\n"
24	f"\t{job['TranscriptionJob']['Transcript']['TranscriptFileUri']}.")
25	break
26	else:
27	print(f"Waiting for {job_name}. Current status is {job_status}.")
28	time.sleep(10)
29
30	def main():
31	transcribe_client = boto3.client('transcribe', region_name = 'us-west-2')
32	file_uri = 's3://DOC-EXAMPLE-BUCKET/my-input-files/my-media-file.flac'
33	transcribe_file('Example-job', file_uri, transcribe_client)
34
35	if __name__ == '__main__':
36	main()