jS+SSKrSSKJr SSKrSSKrSSKJrJrJ r J r J r SSK J r Jr \"5(aSSKJr \ R""\5rS\S \S \R,4S jr\"\"S S 95"SS\ 55rg)N)Any)add_end_docstringsis_torch_availableis_torchaudio_availableis_torchcodec_availablelogging)Pipelinebuild_pipeline_init_args),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMESbpayload sampling_ratereturnc |UnSnSnSSSSUSUSUS S S S / n[R"U[R[RS 9nUR U5nUSn[ R"U[ R5n U RSS:Xa [ S5eU $![a [ S5ef=f)z7 Helper function to read an audio file through ffmpeg. 1f32leffmpegz-izpipe:0z-acz-arz-fz -hide_bannerz -loglevelquietzpipe:1)stdinstdoutzFffmpeg was not found but is required to load audio files from filenamerzMalformed soundfile) subprocessPopenPIPEFileNotFoundError ValueError communicatenp frombufferfloat32shape) rraracformat_for_conversionffmpeg_commandffmpeg_process output_stream out_bytesaudios ڹC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/pipelines/audio_classification.py ffmpeg_readr+s ?B B#      N c#)). XbXgXgh#..x8Ma I MM)RZZ 0E {{1~.// L cabbcs 2B%%B;T)has_feature_extractorc ^\rSrSrSrSrSrSrSrU4Sjr S\ R\ -\ -\-S\S\\\ \44U4S jjrSS jrS rS rSS jrSrU=r$)AudioClassificationPipelineBa Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio formats. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks") >>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"audio-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=audio-classification). FTc>SU;a UScSUS'O SU;aSUS'[TU]"U0UD6 UR[5 g)Ntop_k)super__init__check_model_typer )selfargskwargs __class__s r*r4$AudioClassificationPipeline.__init__csO f !8"F7O F "F7O $)&) JKinputsr8rc &>[TU]"U40UD6$)a Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str` or `dict`): The inputs is either : - `str` that is the filename of the audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) Raw audio at the correct sampling rate (no further check will be done) - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this pipeline do the resampling. The dict must be either be in the format `{"sampling_rate": int, "raw": np.array}`, or `{"sampling_rate": int, "array": np.array}`, where the key `"raw"` or `"array"` is used to denote the raw audio waveform. top_k (`int`, *optional*, defaults to None): The number of top labels that will be returned by the pipeline. If the provided number is `None` or higher than the number of labels available in the model configuration, it will default to the number of labels. function_to_apply (`str`, *optional*, defaults to "softmax"): The function to apply to the model output. By default, the pipeline will apply the softmax function to the output of the model. Valid options: ["softmax", "sigmoid", "none"]. Note that passing Python's built-in `None` will default to "softmax", so you need to pass the string "none" to disable any post-processing. Return: A list of `dict` with the following keys: - **label** (`str`) -- The label predicted. - **score** (`float`) -- The corresponding probability. )r3__call__)r6r<r8r9s r*r>$AudioClassificationPipeline.__call__msDw1&11r;c 00nUc$URRRUS'OGXRRR:a URRRnXS'UbUS;a[SUS35eX$S'OSUS'00U4$)Nr1)softmaxsigmoidnonez'Invalid value for `function_to_apply`: z2. Valid options are ['softmax', 'sigmoid', 'none']function_to_applyrA)modelconfig num_labelsr)r6r1rDr8postprocess_paramss r*_sanitize_parameters0AudioClassificationPipeline._sanitize_parameterss =*.***;*;*F*F w 'zz((333 ))44*/w '  ( (FF =>O=PQGG7H2 36? 2 32)))r;c[U[5(aqURS5(dURS5(a [R"USS9R nO%[ US5nUR5nSSS5 [U[5(a[XRR5n[5(a<SSK n[XR5(aUR5R!5n[#5(aVSSK nSSKn[XR&R(5(a*UR+5nUR,nXeR.S.n[U[05(GaUR35nSU;a S U;dS U;d [5S 5eUR7S S5nUc$UR7S S5 UR7S S5nUR7S5nUnXRR:waSSK n[95(aSS KJn O [?S5eU RA[U[BRD5(aURGU5OUUURR5R!5n[U[BRD5(d [IS5e[KURL5S:wa [5S5eURXRRSS9n URNbU RQURNS9n U $!,(df  GN=f)Nzhttp://zhttps://T)follow_redirectsrbr)arrayrrrawrNzWhen passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "raw" key containing the numpy array or torch tensor representing the audio and a "sampling_rate" key, containing the sampling_rate associated with that arraypath) functionalztorchaudio is required to resample audio samples in AudioClassificationPipeline. The torchaudio package can be installed through: `pip install torchaudio`.z2We expect a numpy ndarray or torch tensor as inputr zFWe expect a single channel audio input for AudioClassificationPipelinept)rreturn_tensors)dtype)) isinstancestr startswithhttpxgetcontentopenreadbytesr+feature_extractorrrtorchTensorcpunumpyr torchcodecdecoders AudioDecoderget_all_samplesdata sample_ratedictcopyrpopr torchaudiorQ ImportErrorresamplerndarray from_numpy TypeErrorlenr!rTto) r6r<fr_rc_audio_samples_array_inputsin_sampling_rateF processeds r* preprocess&AudioClassificationPipeline.preprocesss fc " "  ++v/@/@/L/L6DAII&$'1VVXF( fe $ $ )?)?)M)MNF    &,,//++- " $ $  &"5"5"B"BCC!'!7!7!9',,#)rIr{rr__static_attributes__ __classcell__)r9s@r*r.r.Bs2O!"OL"2rzzE1C7$>"2#"2RVW[\_ad\dWeRf"2H*,IVr;r.)rtypingrrXrbrutilsrrrrr baser r models.auto.modeling_autor get_loggerrloggerr]intror+r.r~r;r*rs uu4X   H %!%!! !H,4HIA(AJAr;