vania-janet/multiturn-rag-retrieval-data huggingface dataset:
As it develops transcription hypotheses, the service optimizes how it parses audio to balance between competing strings of different lengths. You can indicate that the service is to bias its analysis toward shorter or longer strings.







 Speaker labels 



* [Speaker labels](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-speaker-labels) identify different speakers from the audio of a multi-participant exchange. The transcription labels the words and times of each speaker's contributions to a multi-participant conversation. Speakers labels are beta functionality.







 Keyword spotting and word alternatives 



* [Keyword spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spottingkeyword-spotting) identifies spoken phrases that match specified keyword strings with a user-defined level of confidence. Keyword spotting is especially useful when individual phrases from the audio are more important than the full transcription. For example, a customer support system might identify keywords to determine how to route user requests.
* [Word alternatives](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spottingword-alternatives) request alternative words that are acoustically similar to the words of a transcript. The words that it identifies must meet a minimum confidence threshold that is specified by the user. The service identifies similar-sounding words and provides their start and end times, as well as its confidence in the possible alternatives.







 Response formatting and filtering 



* [Smart formatting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formattingsmart-formatting) converts dates, times, numbers, currency values, phone numbers, and internet addresses into more readable, conventional forms in final transcripts. For US English, you can also provide keyword phrases to include certain punctuation symbols in final transcripts. Smart formatting is beta functionality.
"a",
8.41,
8.48
],
. . .
],
"confidence": 0.99,
"transcript": ". . . that %HESITATION that's a . . ."
}
],
"final": true
}
]
}
Show more

Unless you need to use them for your application, you can safely filter hesitation markers from a transcript.





 Hesitations for next-generation models 

For next-generation models, the service includes the actual hesitations words in all transcription results. Next-generation models treat hesitations as words, so hesitations can appear in interim results, final results, and other fields such as the results for word timestamps. Next-generation models do not produce hesitation markers, and enabling smart formatting does not cause hesitations to be removed from final results. Different languages can identify different hesitation words:



* For US English, common hesitation words, as for previous-generation models, are aah, ah, hm, hmm, huh, huh-uh, hum, ohh, ugh, uh, uh-huh, uh-oh, uh-uh, um, and um-hum. Not all of these hesitation words might appear in transcripts.
* For Japanese, hesitation words typically consist of half-width characters such as ｱ, ｱﾉｰ, ｳｰﾝ, ｴｰﾄ, ﾏ, ﾃ, ﾏ, and ﾝｰﾄ. Some hesitations might be recognized as full-width characters.



To increase the likelihood of seeing hesitations in your response, you can use a custom language model. In the custom model, add corpora that include the hesitations or create custom words whose sounds-likes capture the way users say the disfluencies. For more information about custom language models, see [Creating a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate).

The following example shows the hesitation "uhm" in a US English transcript:

{
"result_index": 0,
"results": [
{
"alternatives":
{
Time travel SQL commands 

A DATA_VERSION_RETENTION_TIME subcommand is added to the following commands:



*  [CREATE TABLE](https://www.ibm.com/docs/en/netezza?topic=npsscr-create-table-2)
*  [ALTER TABLE](https://www.ibm.com/docs/en/netezza?topic=npsscr-alter-table-2)
*  [CREATE SCHEMA](https://www.ibm.com/docs/en/netezza?topic=npsscr-create-schema-2)
*  [ALTER SCHEMA](https://www.ibm.com/docs/en/netezza?topic=npsscr-alter-schema-2)
*  [CREATE DATABASE](https://www.ibm.com/docs/en/netezza?topic=npsscr-create-database-2)
*  [ALTER DATABASE](https://www.ibm.com/docs/en/netezza?topic=npsscr-alter-database-2)
*  [SET SYSTEM DEFAULT](https://www.ibm.com/docs/en/netezza?topic=npsscr-set-system-default-2)
*  [SHOW SYSTEM DEFAULT](https://www.ibm.com/docs/en/netezza?topic=npsscr-show-system-default-2)



See also:



*  [SHOW TEMPORAL HISTOGRAM](https://www.ibm.com/docs/en/netezza?topic=reference-show-temporal-histogram)
Managing time travel space usage 

Historical rows in temporal tables take up space on your Netezza Performance Server disks over and above that which is necessary for current rows. The amount of this extra space, which might be significant, depends on the following factors:



* The number of days in the retention time interval.
* The rate of change (rows or bytes deleted and updated) of data in the table.
The size of the current data in and of itself is not a factor if the rate of change is measured in absolute, not relative, terms. For example, 40% of a larger table means more rows or bytes than 40% of a smaller table.



A longer retention time interval might be suitable for a slowly changing dimension table, but not for a table where the number of rows or bytes that are deleted or updated per day is large.

For historical rows that are no longer visible to time travel queries, you can reclaim the space that is taken by these rows with the GROOM TABLE RECORDS ALL command. In some cases, not all rows can be reclaimed. For example, if the rows are also needed to support incremental backup. You can also schedule automatic grooming with [AutoMaint](https://cloud.ibm.com/docs/netezza?topic=netezza-autoadmin) in the web console.

If you altered the retention time interval for a temporal table and the table had historical rows that had been retained based on the earlier retention time interval (before you changed the retention time), you can groom the table to reclaim space.

Run the SHOW TEMPORAL HISTOGRAM command or by use the web console to view the space that is used by deleted rows per day or per range of days into the past to estimate the space that might be reclaimed.



 Viewing space usage with the SHOW TEMPORAL HISTOGRAM command 

SHOW TEMPORAL HISTOGRAM <TEMPORAL TABLE> [DAYSPERROW <number of days>]

Example:

show temporal histogram flight;
BASE_TIMESTAMP | STARTDAYSAGO | ENDDAYSAGO | DELETEDROWS | MBYTESUSED | USAGEPERCENT
When you restore a volume from a snapshot, and the tags that are applied to the new volume match the tags in a backup policy, the new volume is backed up. But you can't directly back up a snapshot that has tags in a backup policy.





 How long are backups retained? 

You can specify that backups be kept 1 - 30 days (default). The retention period can't be shorter than the backup frequency or it returns an error.

You can also specify the number of backups to retain, up to 750 per volume, after which the oldest backups are deleted.





 Are there limitations on how many backups I can take? 

Yes. You can create 10 backup policies per account and up to 750 backups of a volume. For other limitations of this release, see [Limitations in this release](https://cloud.ibm.com/docs/vpc?topic=vpc-backup-service-about&interface=uibackup-service-limitations).





 How do I create a new volume from a backup? 

Restoring from a backup snapshot creates a volume with data from the snapshot. You can restore data from a backup by using the UI, the CLI, or the API. You can restore boot and data volumes during instance creation, when you modify an existing instance, or when you provision a stand-alone volume. When you restore data from a backup snapshot, the data is pulled from an Object Storage bucket. For best performance, you can enable backup snapshots for fast restore. By using the fast restore feature, you can restore a volume that is fully provisioned when the volume is created. When you use fast restore, the data is pulled from a cached backup snapshot in another zone of your VPC. For more information, see [About restoring from a backup snapshot](https://cloud.ibm.com/docs/vpc?topic=vpc-baas-vpc-restore).





 Am I charged for usage? 

Yes. Cost for backups is calculated based on GB capacity that is stored per month, unless the duration is less than one month.
If your audio produces multiple final results, concatenate the transcript elements of the final results to assemble the complete transcription of the audio. Assemble the results in the order in which you receive them. When you assemble a complete final transcript, you can ignore interim results for which the final field is false.





 Additional response content 

Many speech recognition parameters impact the contents of the service's response. Some parameters can cause the service to return multiple transcription results:



* end_of_phrase_silence_time
* interim_results
* split_transcript_at_phrase_end



Some parameters can modify the contents of a transcript:



* profanity_filter
* redaction
* smart_formatting



Other parameters can add more information to the results:



* audio_metrics
* keywords and keywords_threshold
* max_alternatives
* processing_metrics and processing_metrics_interval
* speaker_labels
* timestamps
* word_alternatives_threshold
* word_confidence



For more information about the available parameters, see [Using speech recognition parameters](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-service-featuresfeatures-parameters) and the [Parameter summary](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-summary).







 Pauses and silence 

How the service returns results depends on the interface and the model that you use for speech recognition, as well as the audio that you pass to the service. By default, the service transcribes an entire audio stream as a single utterance and returns a single final result for all of the audio. However, the service can return multiple final results in response to the following conditions:



* The audio contains a pause or extended silence between spoken words or phrases. For most languages, the default pause interval that the service uses to determine separate final results is 0.8 seconds. For Chinese, the default interval is 0.6 seconds. You can use the end_of_phrase_silence_time parameter to change the duration of the pause interval.
The service returns three final results, adding a result for the semantic stop after the first sentence. Each result includes the end_of_utterance field to identify the reason for the split.

{
"result_index": 0,
"results": [
{
"alternatives":
{
"confidence": 0.92,
"transcript": "I have a valid identification card "
}
],
"final": true,
"end_of_utterance": "full_stop"
},
{
"alternatives":
{
"confidence": 0.97,
"transcript": "the number is one two three "
}
],
"final": true,
"end_of_utterance": "silence"
},
{
"alternatives":
{
"confidence": 0.99,
"transcript": "four five six "
}
],
"final": true,
"end_of_utterance": "end_of_data"
}
]
}
Show more







 Character insertion bias 

The character_insertion_bias parameter is beta functionality that is available for all next-generation models. The parameter is not available for previous-generation models.

The character_insertion_bias parameter controls the service's bias for competing strings of different lengths during speech recognition. With next-generation models, the service parses audio character by character. As it does, it establishes hypotheses of previous character strings to help determine viable next characters. During this process, it collects candidate strings of different lengths.

By default, each model uses a default character_insertion_bias of 0.0. This value is optimized to produce the best balance between hypotheses with different numbers of characters. The default is typically adequate for most speech recognition. However, certain use cases might benefit from favoring hypotheses with shorter or longer strings of characters. In such cases, specifying a change from the default can improve speech recognition.
When low latency is enabled, the service curtails its analysis of the audio, which can reduce the accuracy of the transcription. This trade-off might be acceptable if your application requires lower response time more than it does the highest possible accuracy.

The low_latency parameter impacts your use of the interim_results parameter with the WebSocket interface. Interim results are available only for those next-generation models that support low latency, and only if both the interim_results and low_latency parameters are set to true.



* For more information about the next-generation models and their capabilities, see [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng).
* For more information about language support for next-generation models and about which next-generation models support low latency, see [Supported next-generation language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ngmodels-ng-supported).
* For more information about feature support for next-generation models, see [Supported features for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ngmodels-ng-features) and [Unsupported features for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ngmodels-ng-unsupported).
* For more information about the low_latency parameter, see [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interimlow-latency).
* For more information about the interaction between the low_latency and interim_results parameters for next-generation models, see [Requesting interim results and low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency).



Arabic language broadband model renamed
* Significantly greater capacity of up to 500 concurrent transcriptions with the option to add more, far above the limit of 100 concurrent transcriptions for the Plus plan.
* Your first 150,000 minutes of speech recognition each month at no charge.



To learn more or to make a purchase, [contact an IBM representative](https://ibm.biz/contact-wdc-premium).





 Can I provision a premium instance right now? 

No, you must first contact your IBM sales representative to purchase an entitlement and share your requirements. To make a purchase, [contact an IBM representative](https://ibm.biz/contact-wdc-premium).
For the Text to Speech service, only data for custom models, custom words, speaker models, and custom prompts is stored on IBM Cloud. Your disaster recovery plan includes knowing, preserving, and being prepared to restore your customization information.



 Backing up custom models and speaker models 

Preserve the following information about your custom models, custom entries, speaker models, and custom prompts:



* A list of all of your custom models and their definitions. To list information about your custom models:



* Use the GET /v1/customizations method to list information about all custom models. For more information, see [Querying all custom models](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customModelscuModelsQueryAll).
* Use the GET /v1/customizations/{customization_id} method to list information about a specified custom model. For more information, see [Querying a custom model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customModelscuModelsQuery).



* Information about all custom entries (word/translation pairs) in your custom models:



* Use the GET /v1/customizations/{customization_id}/words method to list information about all word/translation pairs from a custom model. For more information, see [Querying all words from a custom model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customWordscuWordsQueryModel).
* Use the GET /v1/customizations/{customization_id}/words/{word} method to list information about a specified word/translation pair from a custom model. For more information, see [Querying a single word from a custom model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customWordscuWordQueryModel).



* A list of all of your speaker models and their definitions. To list information about your speaker models:
* [Supported audio formats](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-formats)



* How much audio data can I submit to the service?

The amount of audio that you can submit with a single speech recognition request depends on the interface that you are using:



* The WebSocket and synchronous HTTP interfaces accept a maximum of 100 MB of audio data.
* The asynchronous HTTP interface accepts a maximum of 1 GB of audio data.



For more information, see [Recognizing speech with the service](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-service-featuresfeatures-recognition).
* Can I transcribe speech from video files?

You cannot transcribe speech from a multimedia file that contains both audio and video. To transcribe speech from a video file, you must separate the audio data from the video data. For more information, see [Transcribing speech from video files](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-formatsaudio-formats-video).
* How can I improve transcription accuracy?

The Speech to Text service offers a customization interface that provides many features and options to improve the speech recognition capabilities of the supported base language models:



* If you are transcribing audio for a specific domain, you can create custom language models to expand and tailor a base model's vocabulary to include domain-specific terminology. When you use custom language models, you can also create and incorporate custom grammars to restrict the words that the service can recognize from your model's vocabulary. Language model customization is supported for both previous- and next-generation models. For more information, see [Creating a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate) and [Adding a grammar to a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarAdd).
You need to upgrade any custom models that are based on the fr-CA_Telephony model. For more information about upgrading custom models, see



* [Upgrading custom models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade)
* [Using upgraded custom models for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade-use)



Defect fix: Add documentation guidelines for creating Japanese sounds-likes based on next-generation models
: Defect fix: In sounds-likes for Japanese custom language models that are based on next-generation models, the character-sequence ウー is ambiguous in some left contexts. Do not use characters (syllables) that end with the phoneme /o/, such as ロ and ト. In such cases, use ウウ or just ウ instead of ウー. For example, use ロウウマン or ロウマン instead of ロウーマン. For more information, see [Guidelines for Japanese](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ngwordLanguages-jaJP-ng).

Adding words directly to custom models that are based on next-generation models increases the training time
: Adding custom words directly to a custom model that is based on a next-generation model causes training of a model to take a few minutes longer than it otherwise would. 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)
System.out.printf("New retention period on %s is %sn", objectName, additionalSeconds);
}





 List legal holds on a protected object 

This operation returns:



* Object creation date
* Object retention period in seconds
* Calculated retention expiration date based on the period and creation date
* List of legal holds
* Legal hold identifier
* Timestamp when legal hold was applied



If there are no legal holds on the object, an empty LegalHoldSet is returned. If there is no retention period specified on the object, a 404 error is returned.

public static void listLegalHoldsOnObject(String bucketName, String objectName) {
System.out.printf("List all legal holds on object %s in bucket %sn", objectName, bucketName);

ListLegalHoldsResult result = cos.listLegalHolds(
bucketName,
objectName
);

System.out.printf("Legal holds on bucket %s: n", bucketName);

List<LegalHold> holds = result.getLegalHolds();
for (LegalHold hold : holds) {
System.out.printf("Legal Hold: %s", hold);
}
}





 Create a hosted static website 

This operation requires an import statement to be added:

import com.ibm.cloud.objectstorage.services.s3.model.model.BucketWebsiteConfiguration;

This operation provides the following upon configuration and requires a correctly configured client:



* Bucket configuration for suffix (index document)
* Bucket configuration for key (error document)



cosClient.setBucketWebsiteConfiguration("<bucket_name>", new BucketWebsiteConfiguration("index.html", "error.html"));







 Next Steps 

For more information, [see the Javadoc](https://ibm.github.io/ibm-cos-sdk-java/).
* For more information about language model and acoustic model customization, see [Understanding customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-customization).
* For more information about grammars, see [Using grammars with custom language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammars).
* For more information about speech activity detection parameters, see [Speech activity detection](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection).







 Supported features for previous-generation models 

Previous-generation models are supported for use with almost all of the service's features. Most features and models are generally available for production use. Where indicated, some features and models are beta functionality. Restrictions apply for some features, for example:



* Features such as speaker labels, numeric redaction, and profanity filtering are limited to certain languages and models. Such restrictions are noted with the descriptions of the individual features. For more information about all available speech recognition parameters, see the [Parameter summary](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-summary).
* The low_latency parameter is supported only for next-generation models. For more information, see [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interimlow-latency).
* For more information about previous-generation models' support for customization, see [Customization support for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-pg).



Otherwise, when a feature is described as being available in general or available for a specific language or languages, it supports the previous-generation models.