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How to implement speaker separation with Async Audio or Video Files

Symbl's Async API allows you to process stored recordings of audio or video from files or URLs or even textual content from a conversation. In this guide, we will walk you through how to implement Speaker Separation with audio or video files. Speaker Separation, in short, is the ability to detect and separate unique speakers in a single stream of audio & video without the need for separate speaker events.

Contents#

Enabling the Diarization#

To enable Speaker Separation in the Async Audio or Video API is as simple as adding the enableSpeakerDiarization=true and diarizationSpeakerCount=<NUMBER_OF_UNIQUE_SPEAKERS> query parameters of the request URL.

This snippet shows a cURL command for consuming the Async Video URL-based API which takes in the URL for a publicly available URL of a Video File:

curl --location --request POST "https://api.symbl.ai/v1/process/video/
url?enableSpeakerDiarization=true&diarizationSpeakerCount=$NUMBER_OF_UNIQUE_SPEAKERS
&webhookUrl=$WEBHOOK_URL"
--header 'Content-Type: application/json'
--header "Authorization: Bearer $AUTH_TOKEN"
--data-raw '{
"url": "https://storage.googleapis.com/demo-conversations/interview-prep.mp4"
}'
info

The above example uses the Async Video URL API, but Speaker Separation can be achieved with other Async Audio/Video APIs in the same way.

  • AUTH_TOKEN needs to be replaced with the Bearer token generated during our authentication process.

  • WEBHOOK_URL can be replaced with a WebHook URL for receiving the status for the Job created after calling the API.

  • For accuracy, NUMBER_OF_UNIQUE_SPEAKERS should match the number of unique speakers in the Audio/Video data.

The above URL has two query parameters:

Parameter NameTypeDescription
enableSpeakerDiarizationBooleanWill enable the speaker separation for the audio or video data under consideration.
diarizationSpeakerCountIntegerSets the number of unique speakers in the audio or video data under consideration.

Identifying Unique Speakers#

Invoking the members call in the Conversation API will return the uniquely identified speakers for this conversation when Speaker Diarization is enabled.

Code Example#

View the API Reference for information on how to get member information.

👉 GET Member Information

JSON Response Example#

{
"members": [
{
"id": "9d6d34d9-5019-4694-9c9a-8ba7bfc8cfab",
"name": "Speaker 1"
},
{
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
}
]
}

The name assigned to a uniquely identified speaker/member from a separated audio/video will follow the format Speaker <number> where <number> is arbitrary and does not necessarily reflect in what order someone spoke.

The id can be used to identify a speaker/member for that specific conversation and can be used to update the details for the specific member demonstrated below in the Updating Detected Members section.

Getting the Speaker Separated Results#

Invoking the messages call in the Conversation API would return the speaker-separated results.

Code Example#

View the API Reference for information on how to get speech-to-text messages from the conversation

👉 GET Messages

JSON Response Example#

{
"messages": [
{
"id": "4591723946704896",
"text": "You're hired two words, everybody loves to hear.",
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
},
"startTime": "2020-08-04T07:18:17.573Z",
"endTime": "2020-08-04T07:18:21.573Z",
"conversationId": "5105430690791424"
},
{
"id": "6328236401229824",
"text": "But before we hear these words comes the interview today's video is part one in a series.",
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
},
"startTime": "2020-08-04T07:18:21.973Z",
"endTime": "2020-08-04T07:18:30.473Z",
"conversationId": "5105430690791424"
},
]
}

The above snippet shows the speaker in the from object with a unique ID. These are the uniquely identified members of this conversation.

info

Reminder: The speaker number in the above snippet is arbitrary and the number doesn’t necessarily reflect the order in which someone spoke.

Gain insights#

Similarly, invoking the insights call in the Conversation API would also reflect the identified speakers in the detected insights.

Code Example#

View the API Reference for information on how to get insights from the conversation.

👉 GET Insights

JSON Response Example#

{
"insights": [
{
"id": "5501181057040384",
"text": "We need to go over three more common interview questions.",
"type": "action_item",
"score": 1,
"messageIds": [
"5710067261243392"
],
"entities": [],
"phrases": [
{
"type": "action_phrase",
"text": "go over three more common interview questions"
}
],
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
},
"definitive": true,
"assignee": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
}
},
{
"id": "5519156904460288",
"text": "How did you hear about this position?",
"type": "question",
"score": 0.999988666660899,
"messageIds": [
"4616389407014912"
],
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
}
},
...
]
}

Updating the Detected Members#

The detected members (unique speakers) would have names like Speaker 1 as the automatic speaker recognition wouldn’t have any context to who this speaker is (name or other details of the speaker). Therefore, it is important to update the details of the detected speakers after the Job is marked as complete.

GET members#

The members call in the Conversation API returns the uniquely identified speakers as shown in the Identifying Unique Speakers section above when the Speaker Separation is enabled.

Let’s consider the same set of members that can be retrieved by calling the GET members call in the Conversation API.

👉 GET Member Information

JSON Response Example#

{
"members": [
{
"id": "9d6d34d9-5019-4694-9c9a-8ba7bfc8cfab",
"name": "Speaker 1"
},
{
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
}
]
}

PUT members#

We can now use the PUT members call to update the details of a specific member as shown below. This call would update the Speaker 2 as shown in the above section with the values in the cURL’s request-body:

👉 PUT Members Information

$ curl --location --request PUT "https://api.symbl.ai/v1/conversations/$CONVERSATION_ID/members/2f69f1c8-bf0a-48ef-b47f-95ae5a4de325"
--header 'Content-Type: application/json'
--header "Authorization: Bearer $AUTH_TOKEN"
--data-raw '{
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"email": "john@example.com",
"name": "John Doe"
}'

  • The CONVERSATION_ID needs to be replaced with the actual Conversation ID (conversationId)

  • The AUTH_TOKEN needs to be replaced with the Bearer token generated during our authentication process.

The URL has the id of the member we want to append to PUT /members with the request body containing the updated name of this member.

There is also the option to include the email of the member. The email will be used as an identifier for tracking those specific members uniquely in that conversation. (Refer to the Updating the Detected Members section below for more details)

After the above call is successful, we will receive the following response:

{
"message": "Member with id: 2f69f1c8-bf0a-48ef-b47f-95ae5a4de325 for conversationId: <CONVERSATION_ID> updated successfully! The update should be reflected in all messages and insights along with this conversation"
}

The message is self-explanatory and tells us that all the references to the member with the id of 2f69f1c8-bf0a-48ef-b47f-95ae5a4de325 in the conversation should now reflect the new values we updated this member with. That includes insights, messages and the conversation’s members as well.

So if we call the GET /members API now, we would see the following result:

{
"members": [
{
"id": "9d6d34d9-5019-4694-9c9a-8ba7bfc8cfab",
"name": "Speaker 1"
},
{
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"email": "john@example.com",
"name": "John Doe"
}
]
}

And similarly, with the GET /messages API call, we would see the updates reflected below as well:

{
"messages": [
{
"id": "4591723946704896",
"text": "You're hired two words, everybody loves to hear.",
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"email": "john@example.com",
"name": "John Doe"
},
"startTime": "2020-08-04T07:18:17.573Z",
"endTime": "2020-08-04T07:18:21.573Z",
"conversationId": "5105430690791424"
},
{
"id": "6328236401229824",
"text": "But before we hear these words comes the interview today's video is part one in a series.",
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"email": "john@example.com",
"name": "John Doe"
},
"startTime": "2020-08-04T07:18:21.973Z",
"endTime": "2020-08-04T07:18:30.473Z",
"conversationId": "5105430690791424"
},
]
}

Curious about the GET /insights API? It would reflect these updates as well!

{
"insights": [
{
"id": "5501181057040384",
"text": "We need to go over three more common interview questions.",
"type": "action_item",
"score": 1,
"messageIds": [
"5710067261243392"
],
"entities": [],
"phrases": [
{
"type": "action_phrase",
"text": "go over three more common interview questions"
}
],
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"email": "john@example.com",
"name": "John Doe"
},
"definitive": true,
"assignee": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"name": "Speaker 2"
}
},
{
"id": "5519156904460288",
"text": "How did you hear about this position?",
"type": "question",
"score": 0.999988666660899,
"messageIds": [
"4616389407014912"
],
"from": {
"id": "2f69f1c8-bf0a-48ef-b47f-95ae5a4de325",
"email": "john@example.com",
"name": "John Doe"
}
},
]
}

Appending to an Existing Conversation With Speaker Separation#

Because conversations don’t neatly end at once and may resume later, our Async API allows you to update/append an existing conversation. You can read more about this capability here.

To enable Speaker Separation with the append capability, the request structure is the same as shown above for creating a new Conversation. You would need to pass in enableSpeakerDiarization=true and diarizationSpeakerCount=<NUMBER_OF_UNIQUE_SPEAKERS> query-parameters.

However, there is one caveat in how the ASR works with Speaker Separation. Consider the below:

An Example Scenario#

We send a recorded conversation to the Async API with 2 speakers John and Alice with enableSpeakerDiarization=true. The diarization identifies them as Speaker 1 and Speaker 2 respectively. We then update the above speakers with their email as john@example.com and alice@example.com.

Now we use the append call for appending another conversation with 2 speakers John and May with enableSpeakerDiarization=true. Let’s assume that the diarization would now identify these as Speaker 1 and Speaker 2 respectively. As discussed before, these numbers are arbitrary and have nothing to do with the order in which the speakers spoke in the conversation.

After this job is complete we will have 4 members in this conversation:

  1. John

  2. Alice

  3. Speaker 1 (Which is John again)

  4. Speaker 2 (Which is May)

Since John and Speaker 1 refer to the same speaker but are labeled as different speakers, their member references would be different for all messages and insights that they are a part of.

The Email Identifier#

This is where the email identifier comes in. The PUT members call can uniquely identify and merge a member with the same email parameter and eliminate any duplicate references with a single reference across the entire conversation which would update all the references including the members, messages and insights.

If we were to execute a PUT members call with the below body where 74001a1d-4e9e-456a-84ed-81bbd363333a refers to the id of Speaker 1 from the above scenario, this would eliminate this member and would update all the references with member represented by 2f69f1c8-bf0a-48ef-b47f-95ae5a4de325 which we know is John Doe.

$ curl --location --request PUT "https://api.symbl.ai/v1/conversations/$CONVERSATION_ID/members/74001a1d-4e9e-456a-84ed-81bbd363333a"
--header 'Content-Type: application/json'
--header "Authorization: Bearer $AUTH_TOKEN"
--data-raw '{
"id": "74001a1d-4e9e-456a-84ed-81bbd363333a",
"email": "john@example.com",
"name": "John Doe"
}'

This is possible because the email uniquely identifies that user.

Best Practices#

  • The diarizationSpeakerCount should be equal to the number of unique speakers present in the conversation for best results as the Diarization model uses this number to probabilistically determine the speakers. If this number is different than the actual speakers, then it might introduce false positives for some part of the transcriptions.

  • For the best experience, the Sample Rate of the data should be greater than or equal to 16000Hz.