`i+ ddlZddlmZmZddlZddlZddlmZm Z m Z m Z m Z ddl mZmZe rddlmZe jeZded ed ejfd Zeed GddeZdS)N)AnyUnion)add_end_docstringsis_torch_availableis_torchaudio_availableis_torchcodec_availablelogging)Pipelinebuild_pipeline_init_args),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMESbpayload sampling_ratereturnc ~|}d}d}dddd|d|d|d d d d g } tj|tjtj }n#t$rt dwxYw||}|d}t j|t j} | j ddkrt d| S)z? Helper function to read an audio file through ffmpeg. 1f32leffmpegz-izpipe:0z-acz-arz-fz -hide_bannerz -loglevelquietzpipe:1)stdinstdoutzFffmpeg was not found but is required to load audio files from filenamerzMalformed soundfile) subprocessPopenPIPEFileNotFoundError ValueError communicatenp frombufferfloat32shape) rraracformat_for_conversionffmpeg_commandffmpeg_process output_stream out_bytesaudios /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/pipelines/audio_classification.py ffmpeg_readr,s  B B#      N c#). XbXghhh cccabbbc"..x88Ma I M)RZ 0 0E {1~./// Ls +AAT)has_feature_extractorc eZdZdZdZdZdZdZfdZde e j e e efdedeee efffd Zdd Zd Zd ZddZxZS)AudioClassificationPipelinea Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio formats. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks") >>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"audio-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=audio-classification). FTcd|vr|dd|d<n d|vrd|d<tj|i||jdkrtd|jd|t dS)Ntop_kptzThe z is only available in PyTorch.)super__init__ frameworkr __class__check_model_typer)selfargskwargsr7s r+r5z$AudioClassificationPipeline.__init__bs f  !8"F7OO F " "F7O$)&))) >T ! !RDNRRRSS S JKKKKKinputsr;rc 8tj|fi|S)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. 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