vania-janet/multiturn-rag-retrieval-data huggingface dataset:
oh "Oh, let me get that for you." Your browser does not support the audio tag. 
 uh "Uh, let me check." Your browser does not support the audio tag. 
 uh-huh "Uh-huh, that's right." Your browser does not support the audio tag. 
 um "That's, um, not quite right." Your browser does not support the audio tag. 





 Enabling or disabling interjections with SSML 

The service always interprets and pronounces most of the interjections as described in the previous section. However, you can use SSML to enable or disable the interjections aha and oh. The interpret-as attribute of the SSML <say-as> element accepts an additional value, interjection. When you use the interpret-as attribute with the interjection value, you include the additional enabled attribute with a value of true or false to indicate whether the word is to be pronounced as an interjection.

The following example HTTP request directs the service not to pronounce oh and aha as interjections. The example uses the en-US_AllisonExpressive voice.

IBM Cloud

curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --header "Accept: audio/wav" --output disabled-interjections.wav --data "{"text":"<say-as interpret-as='interjection' enabled='false'>Oh</say-as>, in addition, the <say-as interpret-as='interjection' enabled='false'>aha</say-as> wasp is endemic to Australia."}" "{url}/v1/synthesize?voice=en-US_AllisonExpesssive"

IBM Cloud Pak for Data
Expressive neural voices offer natural-sounding speech that is exceptionally clear, crisp, and fluid. The new voices are generally available (GA) for production use. They support the use of both standard International Phonetic Alphabet (IPA) and IBM Symbolic Phonetic Representation (SPR) phonetic symbols. For more information, see



* [Expressive neural voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voiceslanguage-voices-expressive)
* [English (Australian) symbols](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-auSymbols-new)



New Korean enhanced neural voice
: The service now supports a new enhanced neural voice for Korean: ko-KR_JinV3Voice. The new voice is generally available (GA) for production use. It supports the use of both standard International Phonetic Alphabet (IPA) and IBM Symbolic Phonetic Representation (SPR) phonetic symbols. For more information, see



* [Enhanced neural voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voiceslanguage-voices-enhanced-neural)
* [Korean symbols](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-koSymbols-new)



New beta Netherlands Dutch enhanced neural voice
: The service now supports a new enhanced neural female voice for Netherlands Dutch: nl-NL_MerelV3Voice. It supports the use of both standard International Phonetic Alphabet (IPA) and IBM Symbolic Phonetic Representation (SPR) phonetic symbols.

The new voice is beta functionality pending completion of support for SSML. At its initial release, the voice does not support use of the following SSML-related functionality:



* The <prosody> element with any speech synthesis request
* The rate_percentage and pitch_percentage parameters with any speech synthesis request
* The <mark> element with a WebSocket speech synthesis request
* The timings parameter of the JSON text message with a WebSocket speech synthesis request
Standard voices do not include a version string in their name (for example, en-US_AllisonVoice).
* Neural voices that use Deep Neural Networks (DNNs) to predict the acoustic (spectral) features of the speech. Neural voices include a version string (V3) in their name (for example, en-US_AllisonV3Voice).



Neural versions are available for all standard voices except for the ja-JP_EmiVoice voice, which is pending and will be available soon. You cannot use the SSML <express-as> and <voice-transformation> elements with the neural voices, and you cannot use the volume attribute of the <prosody> element with the neural voices. For more information about all available voices, see [Languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices).

V2 neural voices withdrawn from service
: The service no longer includes the V2 DNN voices that were previously available. If you use a V2 voice in your application, the service automatically uses the equivalent V3 voice instead.





 24 March 2019 

New V2 neural German voices
: The service now offers V2 Deep Neural Network (DNN) versions of its German voices:



* de-DE_BirgitV2Voice
* de-DE_DieterV2Voice



For more information about DNN-based voices, see [Languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices).

New support for pitch and rate attributes of the SSML <prosody> element
: All of the service's DNN-based voices now support the pitch and rate attributes of the SSML <prosody> element. The DNN-based voices do not support the volume attribute of the <prosody> element. For more information, see [The prosody element](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-elementsprosody_element).





 21 March 2019 

Visibility of service credentials now restricted by role
Customizing your model 

IBM is announcing the deprecation of the IBM Watson® Language Translator service for IBM Cloud® in all regions. As of 10 June 2023, the Language Translator tile will be removed from the IBM Cloud Platform for new customers; only existing customers will be able to access the product. As of 10 June 2024, the service will reach its End of Support date. As of 10 December 2024, the service will be withdrawn entirely and will no longer be available to any customers.

Most of the provided translation models in Language Translator can be extended to learn custom terms and phrases or a general style that's derived from your translation data. Follow these instructions to create your own custom translation model.



 Before you begin 



1. Make sure that your Language Translator service instance is on an Advanced or Premium pricing plan. The Lite and Standard plans do not support customization.
2. Copy the API Key and URL values for your service instance. For more information about finding the values, see [Getting started with Language Translator](https://cloud.ibm.com/docs/language-translator?topic=language-translator-gettingstarted).
3. Find a customizable base model for your language pair. You need the model ID of the base model in order to train your custom model.



* Search the customizable models listed on the [Supported languages for translation](https://cloud.ibm.com/docs/language-translator?topic=language-translator-translation-models) page. Look for the value "true" in the Customizable column, and make sure the Source and Target languages of the model match your language pair.
* Alternatively, you can use the [List models](https://cloud.ibm.com/apidocs/language-translatorlistmodels) method to search models programmatically. You can filter results by language with the source and target parameters.









 Step 1: Create your training data 

The service supports two types of customization:
"response_type": "speech_to_text",
"command_info": {
"type": "configure",
"parameters": {
"update_strategy": "merge_once",
"narrowband_recognize": {
"x-watson-learning-opt-out": true,
"grammar_name": "names-abnf",
"language_customization_id": "81d3630-ba58-11e7-aa4b-41bcd3f6f24d"
}
}
}
}
]
}
}
Show more





 Examples 

The following examples illustrate how to use the speech_to_text response type to send configuration commands to the Speech to Text service.



 Example: Setting the language model 

In this example, the language model is switched to Spanish (es-ES_NarrowbandModel), and smart formatting is enabled.

{
"output": {
"generic": [
{
"response_type": "speech_to_text",
"command_info": {
"type": "configure",
"parameters": {
"narrowband_recognize": {
"model": "es-ES_NarrowbandModel",
"smart_formatting": true
}
}
}
}
]
}
}

Show more





 Example: Updating the recognizeBody property for one conversation turn 

The following example shows how to specify the use of a custom language model for a single turn of the conversation turn. This is done by setting update_strategy to merge_once and specifying the the ID of the custom language model in the configuration parameters.

{
"output": {
"generic": [
{
"response_type": "speech_to_text",
"command_info": {
"type": "configure",
"parameters": {
"update_strategy": "merge_once",
The following examples use the regular pronunciation rules to alter the words' spelling for translation:



* Word: ayurvedic, Translation: aayervedic
* Word: gastroenteritis, Translation: gastro enteritis



As these examples show, developing sounds-like translations can be more trial-and-error than formulaic. You create a candidate translation based on your intuition and experience with the service. You then synthesize the word for the candidate translation as input text and listen to the resulting audio. If you are satisfied with the pronunciation, you can use the translation in your custom model; otherwise, you modify the translation and test it again.





 Phonetic translation 

The sounds-like method is a relatively simple and useful way of achieving a pronunciation. But it is not always possible to develop sounds-like translations. The direct alternative, the phonetic method, might appear to be more complicated and time-consuming, but it can achieve the pronunciation of any word.

Phonetic translation specifies a pronunciation in terms of phoneme symbols, syllable stress marks, and optional syllable boundaries that override the service's regular pronunciation rules. You specify a phonetic translation in one of the following formats:



* The standard International Phonetic Alphabet (IPA) representation
* The proprietary IBM Symbolic Phonetic Representation (SPR)



In either case, you specify a translation by using a specific phoneme format that is based on the Speech Synthesis Markup Language (SSML). SSML is an XML-based markup language that provides annotations of text for speech-synthesis applications. You specify the phonetic translation for a word by using the SSML <phoneme> element:

<phoneme alphabet="{ipa | ibm}" ph="{translation}"></phoneme>

The alphabet attribute specifies the phonetic representation type: ipa or ibm. The ph attribute specifies the phonetic translation string.

For example, consider the word trinitroglycerin. The service's regular pronunciation rules produce a pronunciation that differs from the one commonly used by chemists and physicians. The correct pronunciation can be achieved with a phonetic translation:



* IPA: tɹaɪnˈaɪtɹəglɪsəɹɨn
For example, accountants adhere to a common set of standards and procedures that are known as Generally Accepted Accounting Principles (GAAP). When you create a custom model for a financial domain, provide sentences that use the term GAAP in context. The sentences help the service distinguish between general phrases such as "the gap between them is small" and domain-centric phrases such as "GAAP provides guidelines for measuring and disclosing financial information."

In general, it is better for corpora to use words in different contexts, which can improve how the service learns the phrases. However, if users speak the words in only a couple of contexts, then showing the words in other contexts does not improve the quality of the custom model: Speakers never use the words in those contexts. If speakers are likely to use the same phrase frequently, repeating that phrase in the corpora can improve the quality of the model. In some cases, even adding a few custom words directly to a custom model can make a positive difference.



 Preparing a corpus text file 

Follow these guidelines to prepare a corpus text file:



* Provide a plain text file that is encoded in UTF-8 if it contains non-ASCII characters. The service assumes UTF-8 encoding if it encounters such characters.

Make sure that you know the character encoding of your corpus text files. The service preserves the encoding that it finds in the text files. You must use that same encoding when working with custom words in the custom model. For more information, see [Character encoding for custom words](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWordscharEncoding).
* Use consistent capitalization for words in the corpus. Mix upper- and lowercase letters and use capitalization only when intended.
* Include each sentence of the corpus on its own line, and terminate each line with a carriage return. Including multiple sentences on the same line can degrade accuracy.
* Add personal names as discrete units on separate lines. Do not add the individual elements of a name on separate lines or as individual custom words, and do not include multiple names on the same line of a corpus. The following example shows the correct way to improve recognition accuracy for three names:
* DELETE /v1/customizations/{customization_id}/grammars/{grammar_name} removes an existing grammar from a custom model.



You can use a grammar for speech recognition with the WebSocket and HTTP interfaces. Use the language_customization_id and grammar_name parameters to identify the custom model and the grammar that you want to use. Currently, you can use only a single grammar with a speech recognition request.

For more information about grammars, see the following documentation:



* [Using grammars with custom language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammars)
* [Understanding grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUnderstand)
* [Adding a grammar to a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarAdd)
* [Using a grammar for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUse)
* [Managing grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageGrammars)
* [Example grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarExamples)



For information about all methods of the interface, see the [API & SDK reference](https://cloud.ibm.com/apidocs/speech-to-text).

New numeric redaction feature for US English, Japanese, and Korean now available
: A new numeric redaction feature is now available to mask numbers that have three or more consecutive digits. Redaction is intended to remove sensitive personal information, such as credit card numbers, from transcripts. You enable the feature by setting the redaction parameter to true on a recognition request.
When a string is formed as the concatenation of disjoint substrings of another Text object, it also contains reference to the original Text object and relevant offset mapping information. Two Text objects are considered equal to each other if all their components are correspondingly equal to each other.
* Comparing values of type Span and Text Prioritization factors affect comparisons between values of type Span and type Text.







 Comparing values of type Span and Text 

Prioritization factors affect comparisons between values of type Span and type Text.

Values of type Span and type Text compare against each other in these ways:



* A null span value is always sorted lower than other values.
* A text value is always sorted higher than a span value.
* Values of type Text are sorted first by the lexical order of their string values, then by their original Text orders and offset mapping information if applicable.
* Values of type Span are sorted first by their underlying text objects, then by their begin offset, and then by their end offset. The span with a smaller begin offset is sorted lower. Between two spans that start on the same offset, the one with the smaller end offset sorts lower.







 Execution model 

The runtime component has a document-at-a-time execution model. The runtime component receives a collection of documents and runs the extractor on each document to extract information from that document.

An extractor consists of one or more AQL modules to create a collection of views that each defines a relation. Some of these views are designated as output views, while others are non-output views. Non-output views can include some views that are imported into or exported from a module. It is important to note that output views and exported views are orthogonal. For example, a view that is exported does not qualify as a view that is output. In addition to these views, the unique Document view represents the input document that is annotated by this extractor.



 Document view 

At the AQL module level, Document view is a special view that represents the current document that is being annotated by that module. When two or more modules are combined to form an extractor, the duplicate-free union of Document schemas of all modules is the required Document view.
All other spans are left untouched.
* LeftToRight

This policy processes the spans in order from left to right. When overlap occurs, it retains the leftmost, longest, non-overlapping span. This policy emulates the overlap-handling policy of most regular expression engines.



* <priority_column>

Specifies a column of type Text, String, Integer, or Float. Can be specified only with the LeftToRight consolidation policy.
* <priority_order>

Specifies either ascending or descending order. Can be specified only with the LeftToRight consolidation policy. The ascending order ensures that if a tuple T1 has priority 1 and a tuple T2 has priority 2, T1 has a higher priority than T2. By contrast, if the priority order is descending, T2 has a higher priority. The default value of the priority order is ascending.







 Usage notes 



* When the priority clause is present, the semantics of consolidation follow this order:



* Process spans from left to right, and when spans overlap, retain the leftmost spans.
* If you have multiple overlapping spans that start at the same offset, retain the ones with the highest priority according to the priority order.
* Break remaining ties by retaining the longest spans among those spans with the same priority.



* Consolidate treats nulls as identical. All inputs with a null <consolidate target> result in a single output tuple, which is chosen randomly among those inputs. This behavior is similar to how tuples are consolidated with an identical span in the target column. The exception to resulting in a single output tuple is if the policy is ContainsButNotEqual. In that case, the null <consolidate target> outputs all inputs with null consolidate target.







 Examples 

Example 1: Consolidate on single column

This example directs the system to examine the field Person.name of all output tuples and to use the ContainedWithin consolidation policy to resolve overlap.

consolidate on Person.name
using 'ContainedWithin'

Example 2: Consolidate on expression, involving multiple columns

This example directs the system to examine the result of applying the CombineSpans scalar function to fields Person.firstname and Person.lastname in each output tuple.
Ascending order is the default sort direction.<-- </section "id="section-sort" "> --><-- <section "id="section-highlight" "> --> highlight A Boolean value that specifies whether to include a highlight object in the returned output. When included, the highlight returns keys that are field names and values that are arrays. The arrays contain segments of query-matching text that is highlighted by using the HTML emphasis (<em>) tag.This parameter is ignored if passages.enabled and passages.per_document are true, in which case passages are returned for each document instead of highlights.Currently, if the query searches for an exact match of an enrichment mention, only lowercase matches are highlighted. When the includes operator is used, upper- and lowercase matches are highlighted.The output lists the highlight object after the enriched_text object, as shown in the following example. curl -H "Authorization: Bearer {token}" 'https://{hostname}/{instance_name}/v2/projects/{project_id}/collections/{collection_id}/query?version=2019-11-29&natural_language_query=Hybrid%20cloud%20companies&highlight=true'
The JSON that is returned has the following format: {
"highlight": {
"extracted_metadata.title":
"IBM to Acquire Sanovi Technologies to Expand Disaster Recovery Services for <em>Hybrid</em> <em>Cloud</em>"
],
"enriched_text.concepts.text":
"Privately held <em>company</em>",
"<em>Cloud</em> computing"
],
"text":
" Sanovi Technologies, a privately held <em>company</em> that provides <em>hybrid</em> <em>cloud</em> recovery, <em>cloud</em> migration",
"IBM to Acquire Sanovi Technologies to Expand Disaster Recovery Services for <em>Hybrid</em> <em>Cloud</em>nnPublished",
Response formatting and filtering 

The IBM Watson® Speech to Text service provides three features that you can use to parse transcription results. You can format a final transcript to include more conventional representations of certain strings and to include punctuation. You can redact sensitive numeric information from a final transcript, and you can filter profanity from most transcription results. All of these features are beta functionality and are restricted to certain languages.



 Smart formatting 

The smart formatting feature is beta functionality that is available for US English, Japanese, and Spanish (all dialects). It also also available for the en-WW_Medical_Telephony model when US English audio is recognized.

The smart_formatting parameter directs the service to convert the following strings into more conventional representations:



* Dates
* Times
* Series of digits and numbers
* Phone numbers
* Currency values (for US English and Spanish)
* Internet email and web addresses (for US English and Spanish)



You set the smart_formatting parameter to true to enable smart formatting. By default, the service does not perform smart formatting. The service applies smart formatting just before it returns the final results to the client, when text normalization is complete. The conversion makes the transcript more readable and promotes better post-processing of transcription results by representing these artifacts as they would normally be written.



 What results does smart formatting affect? 

Smart formatting impacts some transcription results and not others:



* Smart formatting affects only words in the transcript field of final results, those results for which the final field is true. It does not affect interim results, for which final is false.
* Smart formatting does not affect words in other fields of the response. For example, smart formatting is not applied to response data in the timestamps or alternatives fields.
* Speech hesitations, such as "uhm" and "uh", can adversely impact the conversion of phrases and strings by smart formatting for some languages. Previous-generation models produce hesitation markers to replace such hesitations in a transcript. Smart formatting has the following effect on hesitation markers for previous-generation models:



* For US English, smart formatting suppresses hesitation markers from the transcript field for final results.