# Copyright 2017-2022 John Snow Labs
#
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Contains classes concerning WhisperForCTC."""
from sparknlp.common import *
[docs]class WhisperForCTC(AnnotatorModel,
HasBatchedAnnotateAudio,
HasAudioFeatureProperties,
HasEngine, HasGeneratorProperties):
"""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.
Note that at the moment, this annotator only supports greedy search and only Spark Versions
3.4 and up are supported.
For multilingual models, the language and the task (transcribe or translate) can be set with
``setLanguage`` and ``setTask``.
Pretrained models can be loaded with ``pretrained`` of the companion object:
.. code-block:: python
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 <https://sparknlp.org/models>`__.
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 <https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/audio/WhisperForCTCTest.scala>`__.
**References:**
`Robust Speech Recognition via Large-Scale Weak Supervision <https://arxiv.org/abs/2212.04356>`__
**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.*
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``AUDIO`` ``DOCUMENT``
====================== ======================
Parameters
----------
task
The formatted task for the audio. Either `<|translate|>` or `<|transcribe|>`.
language
The language for the audio, formatted to e.g. `<|en|>`. Check the model description for
supported languages.
isMultilingual
Whether the model is multilingual
minOutputLength
Minimum length of the sequence to be generated
maxOutputLength
Maximum length of output text
doSample
Whether or not to use sampling; use greedy decoding otherwise
temperature
The value used to module the next token probabilities
topK
The number of highest probability vocabulary tokens to keep for top-k-filtering
topP
If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are
kept for generation
repetitionPenalty
The parameter for repetition penalty. 1.0 means no penalty.
See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details
noRepeatNgramSize
If set to int > 0, all ngrams of that size can only occur once
beamSize
The Number of beams for beam search
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> audioAssembler = AudioAssembler() \\
... .setInputCol("audio_content") \\
... .setOutputCol("audio_assembler")
>>> speechToText = WhisperForCTC.pretrained() \\
... .setInputCols(["audio_assembler"]) \\
... .setOutputCol("text")
>>> pipeline = Pipeline().setStages([audioAssembler, speechToText])
>>> processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content")
>>> 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.]|
+------------------------------------------------------------------------------------------+
"""
name = "WhisperForCTC"
inputAnnotatorTypes = [AnnotatorType.AUDIO]
outputAnnotatorType = AnnotatorType.DOCUMENT
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with "
"config_proto.SerializeToString()",
TypeConverters.toListInt)
language = Param(Params._dummy(), "language", "Optional parameter to set the language for the transcription.",
typeConverter=TypeConverters.toString)
isMultilingual = Param(Params._dummy(), "isMultilingual", "Whether the model is multilingual.",
typeConverter=TypeConverters.toBoolean)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def getLanguage(self):
"""Gets the langauge for the transcription."""
return self.getOrDefault(self.language)
[docs] def getIsMultilingual(self):
"""Gets whether the model is multilingual."""
return self.getOrDefault(self.isMultilingual)
[docs] def setLanguage(self, value):
"""Sets the language for the audio, formatted to e.g. `<|en|>`. Check the model description for
supported languages.
Parameters
----------
value : String
Formatted language code
"""
return self._call_java("setLanguage", value)
[docs] def setTask(self, value):
"""Sets the formatted task for the audio. Either `<|translate|>` or `<|transcribe|>`.
Only multilingual models can do translation.
Parameters
----------
value : String
Formatted task
"""
return self._call_java("setTask", value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC",
java_model=None):
super(WhisperForCTC, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
minOutputLength=0,
maxOutputLength=448,
doSample=False,
temperature=1.0,
topK=1,
topP=1.0,
repetitionPenalty=1.0,
noRepeatNgramSize=0,
batchSize=2,
beamSize=1,
nReturnSequences=1,
isMultilingual=True,
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
WhisperForCTC
The restored model
"""
from sparknlp.internal import _WhisperForCTC
jModel = _WhisperForCTC(folder, spark_session._jsparkSession)._java_obj
return WhisperForCTC(java_model=jModel)
@staticmethod
[docs] def pretrained(name="asr_whisper_tiny_opt", lang="xx", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"asr_hubert_large_ls960"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
WhisperForCTC
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(WhisperForCTC, name, lang, remote_loc)