class WhisperForCTC extends AnnotatorModel[WhisperForCTC] with HasBatchedAnnotateAudio[WhisperForCTC] with HasAudioFeatureProperties with WriteTensorflowModel with WriteOpenvinoModel with WriteOnnxModel with HasEngine with HasGeneratorProperties with HasProtectedParams
Whisper Model with a language modeling head on top for Connectionist Temporal Classification (CTC).
Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. It transcribe in multiple languages, as well as translate from those languages into English.
The audio needs to be provided pre-processed an array of floats.
For multilingual models, the language and the task (transcribe or translate) can be set with
setLanguage
and setTask
.
Note that at the moment, this annotator only supports greedy search and only Spark Versions 3.4 and up are supported.
Pretrained models can be loaded with pretrained
of the companion object:
val speechToText = WhisperForCTC.pretrained() .setInputCols("audio_assembler") .setOutputCol("text")
The default model is "asr_whisper_tiny_opt"
, if no name is provided.
For available pretrained models please see the Models Hub.
To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see WhisperForCTCTestSpec.
References:
Robust Speech Recognition via Large-Scale Weak Supervision
Paper Abstract:
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero- shot transfer setting without the need for any fine- tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.
Example
import spark.implicits._ import com.johnsnowlabs.nlp.base._ import com.johnsnowlabs.nlp.annotators._ import com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC import org.apache.spark.ml.Pipeline val audioAssembler: AudioAssembler = new AudioAssembler() .setInputCol("audio_content") .setOutputCol("audio_assembler") val speechToText: WhisperForCTC = WhisperForCTC .pretrained() .setInputCols("audio_assembler") .setOutputCol("text") val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) val bufferedSource = scala.io.Source.fromFile("src/test/resources/audio/txt/librispeech_asr_0.txt") val rawFloats = bufferedSource .getLines() .map(_.split(",").head.trim.toFloat) .toArray bufferedSource.close val processedAudioFloats = Seq(rawFloats).toDF("audio_content") val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) result.select("text.result").show(truncate = false) +------------------------------------------------------------------------------------------+ |result | +------------------------------------------------------------------------------------------+ |[ Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.]| +------------------------------------------------------------------------------------------+
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- WhisperForCTC
- HasProtectedParams
- HasGeneratorProperties
- HasEngine
- WriteOnnxModel
- WriteOpenvinoModel
- WriteTensorflowModel
- HasAudioFeatureProperties
- HasBatchedAnnotateAudio
- AnnotatorModel
- CanBeLazy
- RawAnnotator
- HasOutputAnnotationCol
- HasInputAnnotationCols
- HasOutputAnnotatorType
- ParamsAndFeaturesWritable
- HasFeatures
- DefaultParamsWritable
- MLWritable
- Model
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implicit
class
ProtectedParam[T] extends Param[T]
- Definition Classes
- HasProtectedParams
-
type
AnnotationContent = Seq[Row]
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
type
AnnotatorType = String
- Definition Classes
- HasOutputAnnotatorType
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
$[T](param: Param[T]): T
- Attributes
- protected
- Definition Classes
- Params
-
def
$$[T](feature: StructFeature[T]): T
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[T](feature: SetFeature[T]): Set[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[T](feature: ArrayFeature[T]): Array[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
val
addedSpecialTokens: MapFeature[String, Int]
- Attributes
- protected[nlp]
-
def
afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
batchAnnotate(batchedAnnotations: Seq[Array[AnnotationAudio]]): Seq[Seq[Annotation]]
Takes audio annotations and produces transcribed document annotations.
Takes audio annotations and produces transcribed document annotations.
- batchedAnnotations
Audio annotations in batches
- returns
Transcribed audio as DOCUMENT type annotation
- Definition Classes
- WhisperForCTC → HasBatchedAnnotateAudio
-
def
batchProcess(rows: Iterator[_]): Iterator[Row]
- Definition Classes
- HasBatchedAnnotateAudio
-
val
batchSize: IntParam
Size of every batch (Default depends on model).
Size of every batch (Default depends on model).
- Definition Classes
- HasBatchedAnnotateAudio
-
val
beamSize: IntParam
Beam size for the beam search algorithm (Default:
4
)Beam size for the beam search algorithm (Default:
4
)- Definition Classes
- HasGeneratorProperties
-
def
beforeAnnotate(dataset: Dataset[_]): Dataset[_]
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
clear(param: Param[_]): WhisperForCTC.this.type
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
val
configProtoBytes: IntArrayParam
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
-
def
copy(extra: ParamMap): WhisperForCTC
requirement for annotators copies
requirement for annotators copies
- Definition Classes
- RawAnnotator → Model → Transformer → PipelineStage → Params
-
def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
val
doNormalize: BooleanParam
Whether or not to normalize the input with mean and standard deviation
Whether or not to normalize the input with mean and standard deviation
- Definition Classes
- HasAudioFeatureProperties
-
val
doSample: BooleanParam
Whether or not to use sampling, use greedy decoding otherwise (Default:
false
)Whether or not to use sampling, use greedy decoding otherwise (Default:
false
)- Definition Classes
- HasGeneratorProperties
-
val
engine: Param[String]
This param is set internally once via loadSavedModel.
This param is set internally once via loadSavedModel. That's why there is no setter
- Definition Classes
- HasEngine
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
explainParam(param: Param[_]): String
- Definition Classes
- Params
-
def
explainParams(): String
- Definition Classes
- Params
-
def
extraValidate(structType: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
extraValidateMsg: String
Override for additional custom schema checks
Override for additional custom schema checks
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
val
featureSize: IntParam
- Definition Classes
- HasAudioFeatureProperties
-
val
features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
val
generationConfig: StructFeature[GenerationConfig]
- Attributes
- protected[nlp]
-
def
get[T](feature: StructFeature[T]): Option[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[T](feature: SetFeature[T]): Option[Set[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getAddedSpecialTokens: Map[String, Int]
- Attributes
- protected[nlp]
-
def
getBatchSize: Int
Size of every batch.
Size of every batch.
- Definition Classes
- HasBatchedAnnotateAudio
-
def
getBeamSize: Int
- Definition Classes
- HasGeneratorProperties
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getConfigProtoBytes: Option[Array[Byte]]
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
-
final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getDoNormalize: Boolean
- Definition Classes
- HasAudioFeatureProperties
-
def
getDoSample: Boolean
- Definition Classes
- HasGeneratorProperties
-
def
getEngine: String
- Definition Classes
- HasEngine
-
def
getFeatureSize: Int
- Definition Classes
- HasAudioFeatureProperties
-
def
getGenerationConfig: GenerationConfig
- Attributes
- protected[nlp]
-
def
getInputCols: Array[String]
- returns
input annotations columns currently used
- Definition Classes
- HasInputAnnotationCols
- def getIsMultilingual: Boolean
- def getLanguage: Option[String]
-
def
getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
-
def
getMaxOutputLength: Int
- Definition Classes
- HasGeneratorProperties
-
def
getMinOutputLength: Int
- Definition Classes
- HasGeneratorProperties
- def getModelIfNotSet: Whisper
-
def
getNReturnSequences: Int
- Definition Classes
- HasGeneratorProperties
-
def
getNoRepeatNgramSize: Int
- Definition Classes
- HasGeneratorProperties
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
final
def
getOutputCol: String
Gets annotation column name going to generate
Gets annotation column name going to generate
- Definition Classes
- HasOutputAnnotationCol
-
def
getPaddingSide: String
- Definition Classes
- HasAudioFeatureProperties
-
def
getPaddingValue: Float
- Definition Classes
- HasAudioFeatureProperties
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
def
getPreprocessor: WhisperPreprocessor
- Attributes
- protected[nlp]
-
def
getRandomSeed: Option[Long]
- Definition Classes
- HasGeneratorProperties
-
def
getRepetitionPenalty: Double
- Definition Classes
- HasGeneratorProperties
-
def
getReturnAttentionMask: Boolean
- Definition Classes
- HasAudioFeatureProperties
-
def
getSamplingRate: Int
- Definition Classes
- HasAudioFeatureProperties
- def getSignatures: Option[Map[String, String]]
-
def
getStopTokenIds: Array[Int]
- Definition Classes
- HasGeneratorProperties
-
def
getTask: Option[String]
- Definition Classes
- HasGeneratorProperties
-
def
getTemperature: Double
- Definition Classes
- HasGeneratorProperties
-
def
getTopK: Int
- Definition Classes
- HasGeneratorProperties
-
def
getTopP: Double
- Definition Classes
- HasGeneratorProperties
- def getVocabulary: Map[String, Int]
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
-
def
hasParent: Boolean
- Definition Classes
- Model
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
val
inputAnnotatorTypes: Array[AnnotatorType]
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
- Definition Classes
- WhisperForCTC → HasInputAnnotationCols
-
final
val
inputCols: StringArrayParam
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
val
isMultilingual: ProtectedParam[Boolean]
Whether or not the model is multilingual.
-
final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
val
language: Param[String]
Optional language to set for the transcription.
Optional language to set for the transcription. The imported model needs to support multiple languages.
-
val
lazyAnnotator: BooleanParam
- Definition Classes
- CanBeLazy
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
val
maxInputLength: IntParam
max length of the input sequence (Default:
0
)max length of the input sequence (Default:
0
)- Definition Classes
- HasGeneratorProperties
-
val
maxOutputLength: IntParam
Maximum length of the sequence to be generated (Default:
20
)Maximum length of the sequence to be generated (Default:
20
)- Definition Classes
- HasGeneratorProperties
-
val
minOutputLength: IntParam
Minimum length of the sequence to be generated (Default:
0
)Minimum length of the sequence to be generated (Default:
0
)- Definition Classes
- HasGeneratorProperties
-
def
msgHelper(schema: StructType): String
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
val
nReturnSequences: IntParam
The number of sequences to return from the beam search.
The number of sequences to return from the beam search.
- Definition Classes
- HasGeneratorProperties
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
val
noRepeatNgramSize: IntParam
If set to int >
0
, all ngrams of that size can only occur once (Default:0
)If set to int >
0
, all ngrams of that size can only occur once (Default:0
)- Definition Classes
- HasGeneratorProperties
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
onWrite(path: String, spark: SparkSession): Unit
- Definition Classes
- WhisperForCTC → ParamsAndFeaturesWritable
-
val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
-
val
outputAnnotatorType: AnnotatorType
- Definition Classes
- WhisperForCTC → HasOutputAnnotatorType
-
final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
-
val
paddingSide: Param[String]
- Definition Classes
- HasAudioFeatureProperties
-
val
paddingValue: FloatParam
- Definition Classes
- HasAudioFeatureProperties
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[WhisperForCTC]
- Definition Classes
- Model
-
val
preprocessor: StructFeature[WhisperPreprocessor]
- Attributes
- protected[nlp]
-
val
randomSeed: Option[Long]
Optional Random seed for the model.
Optional Random seed for the model. Needs to be of type
Int
.- Definition Classes
- HasGeneratorProperties
-
val
repetitionPenalty: DoubleParam
The parameter for repetition penalty (Default:
1.0
).The parameter for repetition penalty (Default:
1.0
).1.0
means no penalty. See this paper for more details.- Definition Classes
- HasGeneratorProperties
-
val
returnAttentionMask: BooleanParam
- Definition Classes
- HasAudioFeatureProperties
-
val
samplingRate: IntParam
- Definition Classes
- HasAudioFeatureProperties
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
def
set[T](param: ProtectedParam[T], value: T): WhisperForCTC.this.type
Sets the value for a protected Param.
Sets the value for a protected Param.
If the parameter was already set, it will not be set again. Default values do not count as a set value and can be overridden.
- T
Type of the parameter
- param
Protected parameter to set
- value
Value for the parameter
- returns
This object
- Definition Classes
- HasProtectedParams
-
def
set[T](feature: StructFeature[T], value: T): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: SetFeature[T], value: Set[T]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: ArrayFeature[T], value: Array[T]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
set(paramPair: ParamPair[_]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): WhisperForCTC.this.type
- Definition Classes
- Params
-
def
setAddedSpecialTokens(value: Map[String, Int]): WhisperForCTC.this.type
- Attributes
- protected[nlp]
-
def
setBatchSize(size: Int): WhisperForCTC.this.type
Size of every batch.
Size of every batch.
- Definition Classes
- HasBatchedAnnotateAudio
-
def
setBeamSize(beamNum: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setConfigProtoBytes(bytes: Array[Int]): WhisperForCTC.this.type
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): WhisperForCTC.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): WhisperForCTC.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
def
setDoNormalize(value: Boolean): WhisperForCTC.this.type
- Definition Classes
- HasAudioFeatureProperties
-
def
setDoSample(value: Boolean): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setFeatureSize(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasAudioFeatureProperties
-
def
setGenerationConfig(value: GenerationConfig): WhisperForCTC.this.type
- Attributes
- protected[nlp]
-
final
def
setInputCols(value: String*): WhisperForCTC.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): WhisperForCTC.this.type
Overrides required annotators column if different than default
Overrides required annotators column if different than default
- Definition Classes
- HasInputAnnotationCols
- def setIsMultilingual(value: Boolean): WhisperForCTC.this.type
-
def
setLanguage(value: String): WhisperForCTC.this.type
Sets the language for the audio, formatted to e.g.
Sets the language for the audio, formatted to e.g.
<|en|>
. Check the model description for supported languages. -
def
setLazyAnnotator(value: Boolean): WhisperForCTC.this.type
- Definition Classes
- CanBeLazy
-
def
setMaxInputLength(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setMaxOutputLength(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setMinOutputLength(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
- def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrappers: Option[EncoderDecoderWrappers], openvinoWrapper: Option[EncoderDecoderWrappers]): WhisperForCTC.this.type
-
def
setNReturnSequences(beamNum: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setNoRepeatNgramSize(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
final
def
setOutputCol(value: String): WhisperForCTC.this.type
Overrides annotation column name when transforming
Overrides annotation column name when transforming
- Definition Classes
- HasOutputAnnotationCol
-
def
setPaddingSide(value: String): WhisperForCTC.this.type
- Definition Classes
- HasAudioFeatureProperties
-
def
setPaddingValue(value: Float): WhisperForCTC.this.type
- Definition Classes
- HasAudioFeatureProperties
-
def
setParent(parent: Estimator[WhisperForCTC]): WhisperForCTC
- Definition Classes
- Model
-
def
setPreprocessor(value: WhisperPreprocessor): WhisperForCTC.this.type
- Attributes
- protected[nlp]
-
def
setRandomSeed(value: Long): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setRepetitionPenalty(value: Double): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setReturnAttentionMask(value: Boolean): WhisperForCTC.this.type
- Definition Classes
- HasAudioFeatureProperties
-
def
setSamplingRate(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasAudioFeatureProperties
- def setSignatures(value: Map[String, String]): WhisperForCTC.this.type
-
def
setStopTokenIds(value: Array[Int]): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setTask(value: String): WhisperForCTC.this.type
Sets the formatted task for the audio.
Sets the formatted task for the audio. Either
<|translate|>
or<|transcribe|>
.Only multilingual models can do translation.
- Definition Classes
- WhisperForCTC → HasGeneratorProperties
-
def
setTemperature(value: Double): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setTopK(value: Int): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
-
def
setTopP(value: Double): WhisperForCTC.this.type
- Definition Classes
- HasGeneratorProperties
- def setVocabulary(value: Map[String, Int]): WhisperForCTC.this.type
-
val
signatures: MapFeature[AnnotatorType, AnnotatorType]
It contains TF model signatures for the loaded saved model
-
val
stopTokenIds: IntArrayParam
Stop tokens to terminate the generation
Stop tokens to terminate the generation
- Definition Classes
- HasGeneratorProperties
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
val
task: Param[String]
Set transformer task, e.g.
Set transformer task, e.g.
"summarize:"
(Default:""
).- Definition Classes
- HasGeneratorProperties
-
val
temperature: DoubleParam
The value used to module the next token probabilities (Default:
1.0
)The value used to module the next token probabilities (Default:
1.0
)- Definition Classes
- HasGeneratorProperties
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
val
topK: IntParam
The number of highest probability vocabulary tokens to keep for top-k-filtering (Default:
50
)The number of highest probability vocabulary tokens to keep for top-k-filtering (Default:
50
)- Definition Classes
- HasGeneratorProperties
-
val
topP: DoubleParam
If set to float <
1.0
, only the most probable tokens with probabilities that add up totopP
or higher are kept for generation (Default:1.0
)If set to float <
1.0
, only the most probable tokens with probabilities that add up totopP
or higher are kept for generation (Default:1.0
)- Definition Classes
- HasGeneratorProperties
-
final
def
transform(dataset: Dataset[_]): DataFrame
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
- dataset
Dataset[Row]
- Definition Classes
- AnnotatorModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
transformSchema(schema: StructType): StructType
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
- Definition Classes
- RawAnnotator → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
uid: String
- Definition Classes
- WhisperForCTC → Identifiable
-
def
validate(schema: StructType): Boolean
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
- schema
to be validated
- returns
True if all the required types are present, else false
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
val
vocabulary: MapFeature[String, Int]
Vocabulary used to encode the words to ids
Vocabulary used to encode the words to ids
- Attributes
- protected[nlp]
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
wrapColumnMetadata(col: Column): Column
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
-
def
writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOpenvinoModel
-
def
writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOpenvinoModel
-
def
writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
- Definition Classes
- WriteTensorflowModel
Inherited from HasProtectedParams
Inherited from HasGeneratorProperties
Inherited from HasEngine
Inherited from WriteOnnxModel
Inherited from WriteOpenvinoModel
Inherited from WriteTensorflowModel
Inherited from HasAudioFeatureProperties
Inherited from HasBatchedAnnotateAudio[WhisperForCTC]
Inherited from AnnotatorModel[WhisperForCTC]
Inherited from CanBeLazy
Inherited from RawAnnotator[WhisperForCTC]
Inherited from HasOutputAnnotationCol
Inherited from HasInputAnnotationCols
Inherited from HasOutputAnnotatorType
Inherited from ParamsAndFeaturesWritable
Inherited from HasFeatures
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from Model[WhisperForCTC]
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.