vania-janet/multiturn-rag-retrieval-data huggingface dataset:
The value must match regular expression /^([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}|current|previous)$/.

--secret-version-action-prototype ([SecretVersionActionPrototype](https://cloud.ibm.com/docs/cli?topic=cli-secrets-manager-clicli-secret-version-action-prototype-example-schema))
: The request body to specify the properties of the action to create a secret version. Required.

Provide a JSON string option or specify a JSON file to read from by providing a filepath option that begins with a @, e.g. --secret-version-action-prototype=@path/to/file.json.





 Example 

ibmcloud secrets-manager secret-version-action-create --secret-id=0b5571f7-21e6-42b7-91c5-3f5ac9793a46 --id=eb4cf24d-9cae-424b-945e-159788a5f535 --secret-version-action-prototype='{"action_type": "private_cert_action_revoke_certificate"}'





 Example output 

The request body of the action to revoke private certificate versions.

{
"action_type" : "private_cert_action_revoke_certificate",
"revocation_time_seconds" : 1667982994
}









 Secret locks 

Secure your secret data by attaching a lock to a secret version and preventing any modification or deletion.



 ibmcloud secrets-manager secrets-locks 

List the secrets and their locks in your Secrets Manager instance. Note: If the --all-pages option is not set, the command will only retrieve a single page of the collection.
: The name of the target region. For example, us-south or eu-gb.

--no-region
: Forced login without targeting a region.

--skip-ssl-validation
: Bypass the SSL validation of HTTP requests. This option isn't recommended.

--vpc
: Use a VPC connection for a private API endpoint.





 Examples 

Log in interactively:

ibmcloud login

Log in to a private endpoint:

ibmcloud login -a private.cloud.ibm.com

Two regions are currently supported: us-south and us-east.

Log in with a user name and password, and set a target account:

ibmcloud login -u username -p password -c MyAccountID

Log in with federated ID, and set a target account:

ibmcloud login --sso -c MyAccountID

Use an API key with an associated account:

ibmcloud login --apikey api-key-string

ibmcloud login --apikey @filename

Use an API key with no associated account:

ibmcloud login --apikey api-key-string -c MyAccountID

ibmcloud login --apikey @fileName -c MyAccountID

If the API key has an associated account, switching to another account isn't supported.

Log in as a specific user with a federated ID:

ibmcloud login -u UserID --ssobash

Then, the CLI provides a URL link and prompts you for the passcode:

Get One Time Code from https://identity-1.us-south.iam.cloud.ibm.com/identity/passcode to proceed.
Open the URL in the default browser? [Y/n] >

Open the link in a browser to get a passcode. Enter the passcode in the console to log in.

Log in as an IKS Compute Resource linked to a trusted profile:
Setting the signature thresholds to a value greater than one enables [quorum authentication](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-understand-conceptsquorum-authenticaion-concept) from multiple administrators for sensitive operations.

When an EP11 smart card with a valid administrator signature key is inserted in smart card reader 1 and the PIN is entered, the smart card can be used to sign multiple commands. In Step 5, if the reader contains an EP11 smart card with a valid signature key and the PIN is entered, you're not prompted to insert an EP11 smart card with a signature key in smart card reader 1.





 Step 5: Load the master key 

For more information about the state transition of the master key register, see [Understanding how master key is loaded](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-introduce-serviceunderstand-key-ceremony).



 Load the new master key register 



1. Select the Master keys tab and click Load.
2. If prompted, insert an EP11 smart card with an administrator signature key that is defined to the selected crypto units in smart card reader 1. And then, enter the smart card PIN on the smart card reader PIN pad.
3. When prompted, enter the number of master key parts to be loaded. Only 2 or 3 master key parts are accepted.
4. When prompted, insert the EP11 smart card that contains the first master key part in smart card reader 2 and enter the smart card PIN on the smart card reader PIN pad.
5. Select the master key part to be loaded from the list of master key parts on the smart card.
6. Repeat substep 4 and 5 for each master key part to be loaded.
7. If prompted, in smart card reader 1, insert the EP11 smart card with an administrator signature key that is defined to the selected crypto units, and enter the smart card PIN on the smart card reader PIN pad.



After all master key parts are loaded, the new master key register is in Full uncommitted state.





 Commit the new master key register 



1.
All virtual data centers in the same region have the same admin password. Resetting the password on a virtual data center resets the admin password for accessing the VMware Cloud Director Management console for all virtual data centers in the same region.







 Related links 



* [Resizing virtual data centers](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-shared_resize)
* [Operating VMware Shared](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-shared_vcd-ops-guide)
* [Deleting VMware Shared virtual data centers](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-shared_deletinginstance)
* [Viewing the virtual data center summary](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-shared_viewing-vdc-summary)
* [Viewing virtual data center details](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-shared_viewing-vdc-details)
* [VMware Cloud Director](https://www.vmware.com/ca/products/cloud-director.html)
Retaining information across dialog turns 

The dialog in a dialog skill is stateless, meaning that it does not retain information from one interaction with the user to the next. When you add a dialog skill to an assistant and deploy it, the assistant saves the context from one message call and then re-submits it on the next request throughout the current session. The current session lasts for as long a user interacts with the assistant plus the designated session inactivity time frame. The maximum session inactivity time allowed ranges from 5 minutes to 7 days, depending on your plan type. If you do not add the dialog skill to an assistant, it is your responsibility as the custom application developer to maintain any continuing information that the application needs.

The application can pass information to the dialog, and the dialog can update this information and pass it back to the application, or to a subsequent node. The dialog does so by using context variables.







 Context variables 

A context variable is a variable that you define in a node. You can specify a default value for it. Other nodes, application logic, or user input can subsequently set or change the value of the context variable.

You can condition against context variable values by referencing a context variable from a dialog node condition to determine whether to execute a node. You can also reference a context variable from dialog node response conditions to show different reponses depending on a value provided by an external service or by the user.

Learn more:



* [Passing context from the application](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-runtime-contextdialog-runtime-context-from-app)
* [Passing context from node to node](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-runtime-contextdialog-runtime-context-node-to-node)
* [Defining a context variable](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-runtime-contextdialog-runtime-context-var-define)
Note: Set to any value between 5 minutes and 30 days.

See the following example of default value settings.

Details for topic testit
Topic name Internal? Partition count Replication factor
testit false 1 3

Partition details for topic testit
Partition ID Leader Replicas In-sync
0 1 [1 5 0] [1 5 0]

Configuration parameters for topic testit
Name Value
cleanup.policy delete
min.insync.replicas 2
segment.bytes 536870912
retention.ms 86400000
segment.ms 604800000
retention.bytes 1073741824
segment.index.bytes 10485760
Show more





 How long does Event Streams set the log retention window for the consumer offsets topic? 

Event Streams retains consumer offsets for 7 days. This corresponds to the Kafka configuration offsets.retention.minutes.

Offset retention is system-wide so you cannot set it at an individual topic level. All consumer groups get only 7 days of stored offsets even if using a topic with a log retention that has been increased to the maximum of 30 days.

The internal Kafka __consumer_offsets topic is visible to you as read-only on the Enterpise plan. You are strongly recommended not to attempt to manage the topic in any way. You cannot access the __consumer_offsets topic in any way on the Standard plan.





 How can I clean up a consumer group with no consumers? 

After consumers have left, a group continues to exist only if it has offsets. Consumer offsets are deleted after 7 days of inactivity. Consequently, a consumer group is deleted when the last committed offset for that group expires.

If you want to explicitly delete a group at a time you choose, you can use the [deleteConsumerGroups() API](http://kafka.apache.org/23/javadoc/org/apache/kafka/clients/admin/AdminClient.htmldeleteConsumerGroups-java.util.Collection).





 How long are messages retained?
* Results from asynchronous HTTP requests are deleted as soon as their time to live expires. For more information, see [Asynchronous job retention](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-data-securitydata-security-async).



You can choose to opt out of logging at either the account level or the API request level. For more information, see [Controlling request logging for Watson services](https://cloud.ibm.com/docs/watson?topic=watson-gs-logging-overview).





 Asynchronous job retention 

You use the POST /v1/recognitions method of the asynchronous HTTP interface to initiate an asynchronous speech recognition job. The service retains the results of each job until its time to live expires. The default time to live for a job is one week, though you can specify a shorter retention period.

You can also use the DELETE /v1/recognitions/{id} method to delete the results of a job. You typically delete a job after you obtain its results from the service. Once you delete a job, its results are no longer available. For more information, see [Deleting a job](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-asyncdelete-async).





 Data separation and encryption 

IBM Cloud

The service's Plus and Premium pricing plans offer different levels of data separation and encryption for users:



* Plus plans are multi-tenant solutions that provide logical separation of data by using common encryption keys.
* Premium plans are single-tenant solutions that provide physical separation of data. Premium plans provide dedicated data storage accounts that use unique encryption keys.



Users of Premium plans can also integrate with IBM® Key Protect for IBM Cloud® to create, import, and manage their encryption keys. This process is commonly referred to as Bring your own keys (BYOK).
If you are training a model with custom words that you added by using the POST /v1/customizations/{customization_id}/words or PUT /v1/customizations/{customization_id}/words/{word_name} method, allow for some minutes of extra training time for the model. For more information, see



* [Add words to the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreateaddWords)
* [Monitoring the train model request](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreatemonitorTraining-language)



Maximum hours of audio resources for custom acoustic models in the Tokyo location has been increased
: The maximum hours of audio resources that you can add to custom acoustic models in the Tokyo location is again 200 hours. Previously, the maximum was reduced to 50 hours for the Tokyo region. That reduction has been rescinded and postponed until next year. For more more information, see [Maximum hours of audio](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audioResourcesaudioMaximum).





 5 December 2022 

New Netherlands Dutch next-generation multimedia model
: The service now offers a next-generation multimedia model for Netherlands Dutch: nl-NL_Multimedia. The new model supports low latency and is generally available. It also supports language model customization and grammars. For more information about next-generation models and low latency, see



* [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng)
* [Customization support for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-ng)
* Performance for audio with more than six speakers is undefined. The feature can handle a maximum of six speakers.
* Performance for cross-talk, or speaker overlap, is poor. Cross-talk can be difficult or impossible to transcribe accurately for any audio.
* Performance for short utterances can be less accurate than for long utterances. The service produces better results when participants speak for longer amounts of time, at least 30 seconds per speaker. The relative amount of audio that is available for each speaker can also affect performance.
* Performance can degrade for the first 30 seconds of speech. It usually improves to a reasonable level after 1 minute of audio, as the service receives more data to work with.



As with all transcription, performance can also be affected by poor audio quality, background noise, a person's manner of speech, and other aspects of the audio. IBM continues to refine and improve the performance of the speaker labels feature. For more information about the latest improvements, see [IBM Research AI Advances Speaker Diarization in Real Use Cases](https://www.ibm.com/blogs/research/2020/07/speaker-diarization-in-real-use-cases/).





 Speaker labels for multichannel audio 

As described in [Channels](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-terminologyaudio-terminology-channels), the Speech to Text service downmixes audio with multiple channels to one-channel mono and uses that audio for speech recognition. The service performs this conversion for all audio, including audio that is passed to generate speaker labels.

If you have multichannel audio that records each speaker's contributions on a separate channel, you can use the following alternative approach to achieve the equivalent of speaker labels:



1. Extract each individual channel from the audio.
2. Transcribe the audio from each channel separately and without using the speaker_labels parameter. Include the timestamps parameter to learn precise timing information for the words of the transcripts.
3. Merge the results of the individual requests to re-create a single transcript of the complete exchange. You can use the timestamps of the individual requests to reconstruct the conversation.
* end_of_phrase_silence_time specifies the duration of the pause interval at which the service splits a transcript into multiple final results. For more information, see [End of phrase silence time](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsingsilence-time).
* split_transcript_at_phrase_end directs the service to split the transcript into multiple final results based on semantic features of the input. For more information, see [Split transcript at phrase end](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsingsplit-transcript).



Defect fix: Correct speaker labels documentation
: Defect fix: Documentation of speaker labels included the following erroneous statement in multiple places: For next-generation models, speaker labels are not supported for use with interim results or low latency. Speaker labels are supported for use with interim results and low latency for next-generation models. For more information, see [Speaker labels](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-speaker-labels).





 28 February 2022 

Updates to English and French next-generation multimedia models to support low latency
: The following multimedia models have been updated to support low latency:



* Australian English: en-AU_Multimedia
* UK English: en-GB_Multimedia
* US English: en-US_Multimedia
* French: fr-FR_Multimedia



You do not need to upgrade custom language models that are built on these base models. For more information about the next-generation models and low latency, see



* [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng)
* [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interimlow-latency)



New Castilian Spanish next-generation multimedia model
* additional time from the current value (Additional-Retention-Period or similar method)
* new extension period in seconds (Extend-Retention-From-Current-Time or similar method)
* new retention expiry date of the object (New-Retention-Expiration-Date or similar method)



The current retention period that is stored in the object metadata is either increased by the given extra time or replaced with the new value, depending on the parameter that is set in the extendRetention request. In all cases, the extend retention parameter is checked against the current retention period and the extended parameter is only accepted if the updated retention period is greater than the current retention period.

Supported ISO 8601 format for New-Retention-Expiration-Date is [YYYY]-[MM]-[DD]T[hh]:[mm]:[ss]Z or [YYYY][DD]T[hh][ss]Z (for example, 2020-11-28T03:10:01Z or 20201128T031001Z are both valid).

Objects in protected buckets that are no longer under retention (retention period has expired and the object does not have any legal holds), when overwritten, will again come under retention. The new retention period can be provided as part of the object overwrite request or the default retention time of the bucket will be given to the object.

Syntax

POST https://{endpoint}/{bucket-name}/{object-name}?extendRetention= path style
POST https://{bucket-name}.{endpoint}/{object-name}?extendRetention= virtual host style

Example request

POST /BucketName/ObjectName?extendRetention HTTP/1.1
Host: myBucket.mydsNet.corp.com
Date: Fri, 8 Dec 2018 17:50:00GMT
Authorization: authorization string
Content-Type: text/plain
Additional-Retention-Period: 31470552

Example response

HTTP/1.1 200 OK
Date: Fri, 8 Dec 2018 17:50:00GMT
Connection: close



* Java
* Python
* Node
For more information about support for individual language models, see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support).





 28 February 2020 (Version 1.1.3) 

Version 1.1.3 is available
: Speech to Text for IBM Cloud Pak for Data version 1.1.3 is now available.

New end_of_phrase_silence_time parameter
: For speech recognition, the service now supports the end_of_phrase_silence_time parameter. The parameter specifies the duration of the pause interval at which the service splits a transcript into multiple final results. Each final result indicates a pause or extended silence that exceeds the pause interval. For most languages, the default pause interval is 0.8 seconds; for Chinese the default interval is 0.6 seconds.

You can use the parameter to effect a trade-off between how often a final result is produced and the accuracy of the transcription. Increase the interval when accuracy is more important than latency. Decrease the interval when the speaker is expected to say short phrases or single words.

For more information, see [End of phrase silence time](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsingsilence-time).

New split_transcript_at_phrase_end parameter
: For speech recognition, the service now supports the split_transcript_at_phrase_end parameter. The parameter directs the service to split the transcript into multiple final results based on semantic features of the input, such as at the conclusion of sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

The parameter causes the service to add an end_of_utterance field to each final result to indicate the motivation for the split: full_stop, silence, end_of_data, or reset.
* [Supported previous-generation language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-modelsmodels-supported)
* [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support)



New end_of_phrase_silence_time parameter for speech recognition
: For speech recognition, the service now supports the end_of_phrase_silence_time parameter. The parameter specifies the duration of the pause interval at which the service splits a transcript into multiple final results. Each final result indicates a pause or extended silence that exceeds the pause interval. For most languages, the default pause interval is 0.8 seconds; for Chinese the default interval is 0.6 seconds.

You can use the parameter to effect a trade-off between how often a final result is produced and the accuracy of the transcription. Increase the interval when accuracy is more important than latency. Decrease the interval when the speaker is expected to say short phrases or single words.

For more information, see [End of phrase silence time](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsingsilence-time).

New split_transcript_at_phrase_end parameter for speech recognition
: For speech recognition, the service now supports the split_transcript_at_phrase_end parameter. The parameter directs the service to split the transcript into multiple final results based on semantic features of the input, such as at the conclusion of sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

The parameter causes the service to add an end_of_utterance field to each final result to indicate the motivation for the split: full_stop, silence, end_of_data, or reset.

For more information, see [Split transcript at phrase end](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsingsplit-transcript).