This section will take you through how to send a file to the V2 Speech API in the Batch Virtual Appliance and receive a finished transcript. It will also show you how to configure the transcription to use supported speech features.
This quick start guide will show you how to submit a media file for processing and then retrieve a transcript in the format of your choice via the V2 API, the recommended method of using the Batch Virtual Appliance. It will also show you optionally how to check the status of a job and to delete it once it has completed.
Pre Requisites
All examples in this document use curl to make the REST API call from a command line. We recommend using retry parameters, so that retry attempts can be made for at least one minute. With the curl command this is done with the --retry 5 --retry-delay 10 parameters. This has been omitted from the examples in this document for brevity.
Note: If you are using a self-signed certificate (your own, or the Speechmatics certificate that is used by default), then you will see a warning like this when using the curl command to access the Speech API using HTTPS:
curl: (60) SSL certificate problem: self signed certificate
We recommend, if you are going to use the secure Speech API, that you upload your own SSL certificate (signed by a CA) to the appliance, to avoid this problem. See the Installation and Admin Guide for details of how to do this.
To successfully submit a job you must send a HTTP POST request to your chosen endpoint with:
type. This is always transcriptionAn example is below for a transcript request in English:
curl -X POST 'https://${APPLIANCE_HOST}/v2/jobs/' \
-F data_file=@example.wav \
-F config='{
"type": "transcription",
"transcription_config": { "language": "en" }
}' \
If you are successful, you will receive a HTTP 201 request and a Job ID. A Job ID is a unique sequential numeric string. You will need this job ID to retrieve any transcript generated.
Speechmatics supports two different models within each language pack; a standard or an enhanced model. The standard model is the faster of the two, whilst the enhanced model provides a higher accuracy, but a slower turnaround time.
The enhanced model is a premium model. Please contact your account manager or Speechmatics if you would like access to this feature.
An example of requesting the enhanced model is below
{
"type": "transcription",
"transcription_config": {
"language": "en",
"operating_point": "enhanced"
}
}
Please note: standard, as well as being the default option, can also be explicitly requested with the operating_point parameter.
If you want to see the progress of an individual job you can make a GET request. You must include the Job ID you want to check in the GET request.
To retrieve a job run the following request:
curl -X GET 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID'
Here is an example of a successful response for a completed job:
{
"jobs": [
{
"config": {
"transcription_config": {
"language": "en"
},
"type": "transcription"
},
"created_at": "2020-12-08T09:49:39.907Z",
"data_name": "Can robots care for us_.mp3",
"duration": 379,
"id": "1",
"status": "done"
}
]
}
In the response you will receive:
done. If the job is still being processed it will return running.If you wish you can retrieve all jobs submitted to the appliance within the last 24 hours by not including the job ID in the GET request. An example is below
curl -X GET 'https://${APPLIANCE_HOST}/v2/jobs/'
If successful you will receive a 200 response and all available jobs:
{
"jobs": [
{
"created_at": "2021-01-08T11:58:04.124Z",
"data_name": "IsTheRecyclingSystemBroken.mp3",
"duration": 377,
"id": "2",
"status": "running"
},
{
"created_at": "2021-01-08T11:57:48.945Z",
"data_name": "Can robots care for us_.mp3",
"duration": 379,
"id": "1",
"status": "running"
}
]
}
Please note if you request to see all jobs, you will not see the configuration for each job. Configuration information can only be retrieved for a request for an individual job. If you have changed the clean up job on the appliance to run at more frequent intervals than the default 24 hours you will only see jobs posted after that clean-up job ran.
You can now retrieve a transcript from the appliance.
Here is an example request to retrieve a transcript from a completed job:
curl -X GET 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID/transcript'
You must put the job ID within the URL path that you received upon successfully requesting the transcription job.
If you request a transcript before it has finished processing, you will receive a HTTP 404 message. To avoid this, you can configure notifications so that you can retrieve transcripts via callback when completed. For details of setting up notifications, please see the section on 'Configuring the Job Request'.
The default format for any transcript is json-v2. Speechmatics also supports transcripts in plain text (TXT) and SubRip Title (SRT) formats. To do so you must explicitly request these.
An example of a successful retrieval of a transcript in plain TXT format:
curl -X GET 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID/transcript?format=txt'
Here is an example of a successful request of a transcript in SRT format:
curl -X GET 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID/transcript?format=srt'
You can receive transcripts in multiple output formats simultaneously via notifications requested in the initial POST submission.
You should now have been able to submit a file and retrieve a transcript.
In addition, you can delete a transcript using a HTTP DELETE request only once it has finished processing. The default retention period for a transcript on the Batch Virtual Appliance is 24 hours. You can alter the configuration of the appliance to shorten this retention period via the Management API; how to do so is documented in the installation guide
You must include in the request the Job ID you wish to delete
curl -X DELETE 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID/'
If you have successfully deleted the transcript, you will receive a HTTP 200 response, and a summary of the job you have just deleted. An example is below
{
"job": {
"config": {
"transcription_config": {
"language": "en"
},
"type": "transcription"
},
"created_at": "2020-12-10T15:38:33.866Z",
"data_name": "Can robots care for us_.mp3",
"duration": 379,
"id": "5",
"status": "deleted"
}
}
You cannot delete multiple jobs at once.
Via the V2 API, you are now able to delete a running job. In this case, no transcript will be returned, and any seconds deducted for processing the transcript will be returned to the license.
To cancel a running job, use the query parameter force=true when sending a DELETE request. An example is below
curl -x DELETE 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID/?force=true'
The response will show the job, and a status of deleted: an example is below:
{
"job": {
"config": {
"transcription_config": {
"language": "en"
},
"type": "transcription"
},
"created_at": "2021-02-02T13:45:37.074Z",
"data_name": "6MinuteEnglish-20200528-IsTheRecyclingSystemBroken.mp3",
"duration": 378,
"id": "9",
"status": "deleted"
}
}
By default the force flag is false. This means a DELETE request without the force or force=false flag for a running job will return HTTP 423 Resource Locked and the transcript will continue to be processed. If the job has already finished, the request will be handled as a normal DELETE request (e.g. the transcript will be deleted, but no time will be returned to the appliance license)
The following sections will show how to use the configuration object when submitting a request in order to use various Speechmatics features in the Batch Virtual Appliance. Where features are only supported in the V2 API this will be made explicit.
To configure any transcription request you must alter the relevant part of the configuration object:
The previous example showed how to create a job from a locally uploaded audio file. If you store your digital media in cloud storage (for example AWS S3 or Azure Blob Storage) you can also submit a job by providing the URL of the audio file. The configuration uses a fetch_data section, which looks like this:
curl -X POST 'https://${APPLIANCE_HOST}/v2/jobs' \
-F config='{
"type": "transcription",
"transcription_config": { "language": "en" },
"fetch_data": { "url": "https://s3.us-east-2.amazonaws.com/bucketname/jqld_/20180804102000/profile.m4v" }
}' \
A note on best practice
If you are using pre-signed URLs, please ensure these have not expired before sending them to the appliance, as the job will fail.
If you need to additional authentication or authorization the appliance supports an optional auth_headers parameter where these can be supplied: e.g. when using an OAuth2 Bearer token.
Please note: when submitting many jobs at once, please note that the audio will be fetched after a job ID is returned and before a job can be processed by an ASR worker. Please ensure when submitting large numbers of jobs in a small space of time that there is sufficient space on the appliance for the number of files you wish to submit.
Speechmatics offers four different modes for separating out different speakers in the audio:
| Type | Description | Use Case |
|---|---|---|
| speaker diarization | Aggregates all audio channels into a single stream for processing and picks out unique speakers based on acoustic matching. | Used in cases where there are multiple speakers embedded in the same audio recording and it's required to understand what each unique speaker said. |
| channel diarization | Transcribes each audio channel separately and treats each channel as a unique speaker. | Used when it's possible to record each speaker on separate audio channels. |
| speaker change (beta) | Provides the point in transcription when there is believed to be a new speaker. | Used for when you just need to know the speaker has changed usually in a real-time application. |
| channel diarization & speaker change | Transcribes each audio channel separately and within each channel provides the point when there is believed to be a new speaker. | Used when it's possible to record some speakers on a separate audio channel, but some channels there are multiple speakers. |
Each of these modes can be enabled by using the diarization config. The following are valid values:
The default value is none - e.g. the transcript will not be diarized.
| Type | Config Value |
|---|---|
| speaker diarization | speaker |
| channel diarization | channel |
| speaker change | speaker_change |
| channel diarization & speaker change | channel_and_speaker_change |
Speaker diarization aggregates all audio channels into a single stream for processing, and picks out different speakers based on acoustic matching.
By default the feature is disabled. To enable speaker diarization the following must be set when you are using the config object:
{
"type": "transcription",
"transcription_config": {
"language": "en",
"diarization": "speaker"
}
}
When enabled, every word and punctuation object in the output results will be a given "speaker" property which is a label
indicating who said that word. There are two kinds of labels you will see:
S# - S stands for speaker and the # will be an incrementing integer identifying an individual speaker. S1 will appear first in the results, followed by S2 and S3 etc.UU - Diarization is disabled or individual speakers cannot be identified. UU can appear for example if some background noise is transcribed as speech, but the diarization system does not recognise it as a speaker.Note: Enabling diarization increases the amount of time taken to transcribe an audio file. In general we expect diarization to take roughly the same amount of time as transcription does, therefore expect the use of diarization to roughly double the overall processing time.
The example below shows relevant parts of a transcript with 3 speakers. The output shows the configuration information passed in the config.json object and relevant segments with the different speakers in the JSON output. Only part of the transcript is shown here to highlight how different speakers are displayed in the output.
"format": "2.7",
"metadata": {
"created_at": "2020-07-01T13:26:48.467Z",
"type": "transcription",
"transcription_config": {
"language": "en",
"diarization": "speaker"
}
},
"results": [
{
"alternatives": [
{
"confidence": 0.93,
"content": "hello",
"language": "en",
"speaker": "S1"
}
],
"end_time": 0.51,
"start_time": 0.36,
"type": "word"
},
{
"alternatives": [
{
"confidence": 1.0,
"content": "hi",
"language": "en",
"speaker": "S2"
}
],
"end_time": 12.6,
"start_time": 12.27,
"type": "word"
},
{
"alternatives": [
{
"confidence": 1.0,
"content": "good",
"language": "en",
"speaker": "S3"
}
],
"end_time": 80.63,
"start_time": 80.48,
"type": "word"
}
In our JSON output, start_time identifies when a person starts speaking each utterance and end_time identifies when they finish speaking.
The sensitivity of the speaker detection is set to a sensible default that gives the optimum performance under most circumstances. However, you can change this value based on your specific requirements by using the speaker_sensitivity setting in the speaker_diarization_config section of the job config object, which takes a value between 0 and 1 (the default is 0.5). A higher sensitivity will increase the likelihood of more unique speakers returning. For example, if you see fewer speakers returned than expected, you can try increasing the sensitivity value, or if too many speakers are returned try reducing this value. It's not guaranteed to change since several factors can affect the number of speakers detected. Here's an example of how to set the value:
{
"type": "transcription",
"transcription_config": {
"language": "en",
"diarization": "speaker",
"speaker_diarization_config": {
"speaker_sensitivity": 0.6
}
}
}
To enhance the accuracy of our speaker diarization, we make small corrections to the speaker labels based on the punctuation in the transcript. For example if our system originally thought that 9 words in a sentence were spoken by speaker S1, and only 1 word by speaker S2, we will correct the incongruous S2 label to be S1. This only works if punctuation is enabled in the transcript.
Therefore if you disable punctuation, for example by removing all permitted_marks in the punctuation_overrides section
of the config.json then expect the accuracy of speaker diarization to vary slightly.
Speaker diarization will timeout if it takes too long to run for a particular audio file. Currently the timeout is set to 5 minutes or 0.5 * the audio duration; whichever is longer. For example, with a 2 hour audio file the timeout is 1 hour. If a timeout happens the transcript will still be returned but without the speaker labels set.
If the diarization does timeout you will see an ERROR message in the logs that looks like this:
Speaker diarization took too long and timed out (X seconds).
If a timeout occurs then all speaker labels in the output will be labelled as UU.
Under normal operation we do not expect diarization to timeout, but diarzation can be affected by a number of factors including audio quality and the number of speakers. If you do encounter timeouts frequently then please get in contact with Speechmatics support.
Channel diarization allows individual channels in an audio file to be labelled. This is ideal for audio files with multiple channels (up to 6) where each channel is a unique speaker.
By default the feature is disabled. To enable channel diarization the following must be set when you are using the config object:
{
"type": "transcription",
"transcription_config": {
"language": "en",
"diarization": "channel"
}
}
The following illustrates an example configuration to enable channel diarization on a 2-channel file that will use labels Customer for channel 1 and Agent for channel 2:
{
"type": "transcription",
"transcription_config": {
"language": "en",
"diarization": "channel",
"channel_diarization_labels": ["Customer", "Agent"]
}
}
For each named channel, the words will be listed in its own labelled block, for example:
{
"format": "2.7",
"metadata": {
"created_at": "2020-07-01T14:11:43.534Z",
"type": "transcription",
"transcription_config": {
"language": "en",
"diarization": "channel",
"channel_diarization_labels": ["Customer", "Agent"]
}
},
"results": [
{
"alternatives": [
{
"confidence": 0.87,
"content": "Hello",
"language": "en"
}
],
"channel": "Customer",
"end_time": 14.34,
"start_time": 14.21,
"type": "word"
},
{
"alternatives": [
{
"confidence": 0.87,
"content": "how",
"language": "en"
}
],
"channel": "Agent",
"end_time": 14.62,
"start_time": 14.42,
"type": "word"
},
{
"alternatives": [
{
"confidence": 0.87,
"content": "can",
"language": "en"
}
],
"channel": "Agent",
"end_time": 15.14,
"start_time": 14.71,
"type": "word"
},
{
"alternatives": [
{
"confidence": 0.79,
"content": "I",
"language": "en"
}
],
"channel": "Agent",
"end_time": 16.71,
"start_time": 16.3,
"type": "word"
},
{
"alternatives": [
{
"confidence": 0.67,
"content": "help",
"language": "en"
}
],
"channel": "Agent",
"end_time": 10.39,
"start_time": 10.17,
"type": "word"
}
Note:
channel as a diarization option, and do not assign channel_diarization_labels then default labels will be used (channel_1, channel_2 etc)This feature allows changes in the speaker to be detected and then marked in the transcript. It does not provide information about whether the speaker is the same as one earlier in the audio.
By default the feature is disabled. The config used to request speaker change detection looks like this:
{
"type": "transcription",
"transcription_config": {
"diarization": "speaker_change",
"speaker_change_sensitivity": 0.8
}
}
Note: Speaker change is only visible in the JSON V2 output, so make sure you use the json-v2 format when you retrieve the transcript.
The speaker_change_sensitivity property, if used, must be a numeric value between 0 and 1. It indicates to the algorithm how sensitive to speaker change events you want to make it. A low value will mean that very few changes will be signalled (with higher possibility of false negatives), whilst a high value will mean you will see more changes in the output (with higher possibility of false positives). If this property is not specified, a default of 0.4 is used.
Speaker change elements appear in resulting JSON transcript results array look like this:
{
"type": "speaker_change",
"start_time": 0.55,
"end_time": 0.55,
"alternatives": []
}
Note: Although there is an alternatives property in the speaker change element it is always empty, and can be ignored. The start_time and end_time properties are always identical, and provide the time when the change was detected.
A speaker change indicates where we think a different person has started talking. For example, if one person says "Hello James" and the other responds with "Hi", there should be a speaker_change element between "James" and "Hi", for example:
{
"format": "2.7",
"job": {
....
"results": [
{
"start_time": 0.1,
"end_time": 0.22,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "Hello",
"language": "en",
"speaker": "UU"
}
]
},
{
"start_time": 0.22,
"end_time": 0.55,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "James",
"language": "en",
"speaker": "UU"
}
]
},
{
"start_time": 0.55,
"end_time": 0.55,
"type": "speaker_change",
"alternatives": []
},
{
"start_time": 0.56,
"end_time": 0.61,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "Hi",
"language": "en",
"speaker": "UU"
}
]
}
]
}
Speaker change can be combined with channel diarization. It will transcribe each channel separately and indicate in the output each channel (with labels if set) and the speaker changes on each of the channels. For example, if a two-channel audio contains three people greeting each other (with a single speaker on channel 1 and two speakers on channel 2), the config submitted with the audio to request the speaker change detection is:
{
"type": "transcription",
"transcription_config": {
"diarization": "channel_and_speaker_change",
"speaker_change_sensitivity": 0.8
}
}
The output will have special elements in the results array between two words where a different person starts talking on the same channel.
{
"format": "2.7",
"job": {
....
},
"metadata": {
....
},
"results": [
{
"channel": "channel_2",
"start_time": 0.1,
"end_time": 0.22,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "Hello",
"language": "en",
"speaker": "UU"
}
]
},
{
"channel": "channel_2",
"start_time": 0.22,
"end_time": 0.55,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "James",
"language": "en",
"speaker": "UU"
}
]
},
{
"channel": "channel_1",
"start_time": 0.55,
"end_time": 0.55,
"type": "speaker_change",
"alternatives": []
},
{
"channel": "channel_2",
"start_time": 0.56,
"end_time": 0.61,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "Hi",
"language": "en",
"speaker": "UU"
}
]
},
{
"channel": "channel_1",
"start_time": 0.56,
"end_time": 0.61,
"type": "word",
"alternatives": [
{
"confidence": 0.71,
"content": "Hi",
"language": "en",
"speaker": "UU"
}
]
}
]
}
The Custom Dictionary feature allows a list of custom words to be added for each transcription job. This helps when a specific word is not recognised during transcription. It could be that it's not in the vocabulary for that language, for example a company or person's name. Adding custom words can improve the likelihood they will be output.
The sounds_like feature is an extension to this to allow alternative pronunciations to be specified to aid recognition when the pronunciation is not obvious.
The Custom Dictionary feature can be accessed through the additional_vocab property.
Prior to using this feature, consider the following:
sounds_like is an optional setting recommended when the pronunciation is not obvious for the word or it can be pronounced in multiple ways; it is valid just to provide the content valuesounds_like only works with the main script for that languagesounds_like only supports full width Hiragana or Katakana"transcription_config": {
"language": "en",
"additional_vocab": [
{
"content": "gnocchi",
"sounds_like": [
"nyohki",
"nokey",
"nochi"
]
},
{
"content": "CEO",
"sounds_like": [
"C.E.O."
]
},
{
"content": "financial crisis"
}
]
}
In the above example, the words gnocchi and CEO have pronunciations applied to them; the phrase financial crisis does not require a pronunciation. The content property represents how you want the word to be output in the transcript.
It is possible to specify the spelling rules to be used when generating the transcription, based on locale. The output_locale configuration setting is used for this. As an example, the following configuration uses the Global English (en) language pack with an output locale of British English (en-GB):
{ "type": "transcription",
"transcription_config": {
"language": "en",
"output_locale": "en-GB"
}
}
The following locales are supported in the Global English language pack, if no locale is specified then the ASR engine will use whatever spelling it has learnt as part of our language model training (in other words it will be based on the training data used).
The following locales are supported for Chinese Mandarin. The default is simplified Mandarin.
All Speechmatics language packs support Advanced Punctuation. This uses machine learning techniques to add in more naturalistic punctuation, improving the readability of your transcripts.
The following punctuation marks are supported for each language:
| Language(s) | Supported Punctuation | Comment |
|---|---|---|
| Cantonese, Mandarin | ， 。 ？ ！ 、 | Full-width punctuation supported |
| Japanese | 。 、 | Full-width punctuation supported |
| Hindi | । ? , ! | |
| All other languages | . , ! ? |
If you do not want to see any of the supported punctuation marks in the output, then you can explicitly control this through the punctuation_overrides settings, for example:
"transcription_config": {
"language": "en",
"punctuation_overrides": {
"permitted_marks":[ ".", "," ]
}
}
This will exclude exclamation and question marks from the returned transcript.
All Speechmatics output formats support Advanced Punctuation. JSON output places punctuation marks in the results list marked with a type of "punctuation".
Note: Disabling punctuation may slightly harm the accuracy of speaker diarization. Please see the "Speaker diarization post-processing" section in these docs for more information.
Customers can poll the appliance to check on the status of the job, before making the call to retrieve the transcript. Where many jobs are being done at scale, this may not be sustainable. A more convenient method - and recommended approach - is to use notifications. This involves a callback to a web service that you control once a job is complete. An HTTP POST request is then made from the Speechmatics appliance once the transcript is available. PUT is also supported where specified
The notification support offered in V1 has been extended and generalized in V2 to support a wider range of customer integration scenarios:
jobinfo notification to one service, and the transcript notification to another.Important Notice
In the Batch Virtual Appliance, a user cannot request the audio file that was part of the original job submission.
The callback is specified by using the notification_config within the config object. For example:
curl -X POST 'https://{APPLIANCE_HOST}/v2/jobs' \
--form data_file=@example.wav \
--form config='{
"type": "transcription",
"transcription_config": { "language": "en" },
"notification_config": [
{
"url": "https://collector.example.org/callback",
"contents": [ "transcript" ],
"auth_headers": [
"Authorization: Bearer eyJ0eXAiOiJKV1QiLCJhb"
]
}
]
}
You need to ensure that the service that you implement to receive the callback notification is capable of processing the Speechmatics transcript using the format that has been specified in the config JSON. When testing your integration you should check the error logs on your web service to ensure that notifications are being accepted and processed correctly.
The callback appends the job ID as a query string parameter with name id, as well as the status of the job. As an example, if the job ID is 100, you'd see the following POST request:
POST /callback?id=100&status=success HTTP/1.1
Host: collector.example.org
The user agent is Speechmatics-API/2.0.
Once transcription is complete and the transcript file is available, the Speechmatics Batch Virtual Appliance will send the transcript file in a HTTP POST request (unless otherwise specified) to the client web server specified in the notification_config config object. If the appliance does not receive successful 2xx response it will keep trying to send the file until it reaches the set timeout threshold.
If the clients webserver cannot accept the file(s) because it is not configured with a large enough size limit, it will generate a 413 (Request Entity Too Large) response. If the appliance does not receive a 2xx response it will continue to retry sending the file. Users are recommended to check their webserver size limits to ensure they are adequate for the files that will be sent.
It is now possible to attach richer metadata to a job using the tracking configuration. This metadata can be used to identify transcripts for appropriate data storage and classification, especially where they may have passed through multiple systems using whatever information is relevant to you. The tracking object contains the following properties:
| Name | Type | Description | Notes |
|---|---|---|---|
title | str | The title of the job. | [optional] |
reference | str | External system reference. | [optional] |
tags | list[str] | Customer-defined tags | [optional] |
details | object | Customer-defined JSON structure. | [optional] |
Here is an example
curl -X POST 'https://${APPLIANCE_HOST}/v2/jobs' \
-H 'Authorization: Bearer NDFjOTE3NGEtOWVm' \
--form data_file=@example.wav \
--form config='{
"type": "transcription",
"transcription_config": { "language": "en" },
"tracking": {
"title": "ACME Q12018 Statement",
"reference": "/data/clients/ACME/statements/segs/2018Q1-seg8",
"tags": [ "quick-review", "segment" ],
"details": {
"client": "ACME Corp",
"segment": 8,
"seg_start": 963.201,
"seg_end": 1091.481
}
}
}
SubRip (SRT) is a subtitling format that can be used in to generate subtitles for video content or other workflows. Our SRT output will generate a transcript together with corresponding alignment timestamps. We follow best practice as recommended by major broadcasters in our default line length and number of lines output.
Speechmatics provides a default configuration output for SRT files for both number of lines and line length in characters. You can change these parameters, by passing configuration options described below. To alter default parameters, you must make parameter changes within the configuration file:
{
"type": "transcription",
"transcription_config": {
...
},
"output_config": {
"srt_overrides": {
"max_line_length": 37,
"max_lines": 2
}
}
}
max_line_length: sets maximum count of characters per subtitle line including white space (default: 37).max_lines: sets maximum count of lines in a subtitle section (default: 2).Speechmatics now outputs in JSON transcript only a metadata tag to indicate whether a word is a profanity or not. This is for the following languages:
The list of profanities is not alterable. Users do not have to take any action to access this - it is provided in our JSON output as standard Customers can use this tag for their own post-processing in order to identify, redact, or obfuscate profanities and integrate this data into their own workflows. An example of how this looks is below.
"results": [
{
"alternatives": [
{
"confidence": 1.0,
"content": "$PROFANITY",
"language": "en",
"speaker": "UU",
"tags": [
"profanity"
]
}
],
"end_time": 18.03,
"start_time": 17.61,
"type": "word"
}
]
Speechmatics now outputs in JSON transcript only a metadata tag to indicate whether a word is a disfluency or not in the English language only. A disfluency here refers to a set list of words in English that imply hesitation or indecision. Please note while disfluency can cover a range of items like stuttering and interjections, here it is only used to tag words such as 'hmm' or 'umm'. An example of how this looks is below:
"results": [
{
"alternatives": [
{
"confidence": 1.0,
"content": "hmm",
"language": "en",
"speaker": "UU",
"tags": [
"disfluency"
]
}
],
"end_time": 18.03,
"start_time": 17.61,
"type": "word"
}
]
In case something unexpected happens with your transcription job, you can use the V2 API to retrieve logging for any job. This can be used for internal debugging and troubleshooting, or for providing more information to Speechmatics Support in the event of continued failure.
This feature is available only when a Job ID is generated and returned at the audio submission time. If the audio upload fails and no Job ID is returned, the log will not be available. If a user submits a job and gets a 401 error back (for example) rather than a Job ID, we won't provide logs via this endpoint. The transcription job log is available when the job finishes successfully, but there was then an error with the file processing or the transcript retrieval failed (e.g. an HTTP 500 error when retrieving the transcript).
You must include the Job ID in the request to retrieve logs for any job. You can only request logs from one job ID at a time. Here is a simple example URL:
curl -X GET 'https://${APPLIANCE_HOST}/v2/jobs/$JOBID/log'