g'ddlZddlmZddlZddlZddlmZmZm Z m Z ddl m Z m Z erddlmZe jeZded ed ejfd Zee d Gdde ZdS)N)Union)add_end_docstringsis_torch_availableis_torchaudio_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$rt dwxYw||}|d}t j|t j} | j ddkrt d| S)z? Helper function to read an audio file through ffmpeg. 1f32leffmpegz-izpipe:0z-acz-arz-fz -hide_bannerz -loglevelquietzpipe:1)stdinstdoutzFffmpeg was not found but is required to load audio files from filenamerzMalformed soundfile) subprocessPopenPIPEFileNotFoundError ValueError communicatenp frombufferfloat32shape) r raracformat_for_conversionffmpeg_commandffmpeg_process output_stream out_bytesaudios g/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/pipelines/audio_classification.py ffmpeg_readr*s  B B#      N c#). XbXghhh cccabbbc"..x88Ma I M)RZ 0 0E {1~./// Ls +AAT)has_feature_extractorcjeZdZdZfdZdeejee fffd Z d dZ dZ dZ d d ZxZS)AudioClassificationPipelinea Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio formats. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks") >>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"audio-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=audio-classification). cd|d<tj|i||jdkrtd|jd|t dS)Ntop_kptzThe z is only available in PyTorch.)super__init__ frameworkr __class__check_model_typer )selfargskwargsr5s r)r3z$AudioClassificationPipeline.__init__]slw$)&))) >T ! !RDNRRRSS S JKKKKKinputsc 8tj|fi|S)a Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str` or `dict`): The inputs is either : - `str` that is the filename of the audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) Raw audio at the correct sampling rate (no further check will be done) - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this pipeline do the resampling. The dict must be either be in the format `{"sampling_rate": int, "raw": np.array}`, or `{"sampling_rate": int, "array": np.array}`, where the key `"raw"` or `"array"` is used to denote the raw audio waveform. top_k (`int`, *optional*, defaults to None): The number of top labels that will be returned by the pipeline. If the provided number is `None` or higher than the number of labels available in the model configuration, it will default to the number of labels. function_to_apply(`str`, *optional*, defaults to "softmax"): The function to apply to the model output. By default, the pipeline will apply the softmax function to the output of the model. Valid options: ["softmax", "sigmoid", "none"]. Note that passing Python's built-in `None` will default to "softmax", so you need to pass the string "none" to disable any post-processing. Return: A list of `dict` with the following keys: - **label** (`str`) -- The label predicted. - **score** (`float`) -- The corresponding probability. )r2__call__)r7r;r9r5s r)r=z$AudioClassificationPipeline.__call__gs%L uww11&111r:Nc i}|+||jjjkr|jjj}||d<||dvrtd|d||d<nd|d<ii|fS)Nr0)softmaxsigmoidnonez'Invalid value for `function_to_apply`: z2. Valid options are ['softmax', 'sigmoid', 'none']function_to_applyr?)modelconfig num_labelsr)r7r0rBr9postprocess_paramss r)_sanitize_parametersz0AudioClassificationPipeline._sanitize_parameterss  tz(333 )4*/ w '  ( (FFF G>OGGG7H 2 3 36? 2 32)))r:ct|tr|ds|drtj|j}n@V@d%''&"*-- B@AA A v|   ! !eff f** $"8"FW[+  s*B  BBc |jdi|}|S)N)rC)r7 model_inputs model_outputss r)_forwardz$AudioClassificationPipeline._forwards" 22\22 r:r/r?ct|dkr!|jdd}n3|dkr |jd}n |jd}||\}}|}|}fdt ||D}|S)Nr?rr@cJg|]\}}|jjj|d S))scorelabel)rCrDid2label).0rr_idr7s r) z;AudioClassificationPipeline.postprocess..s5pppQ[QVX[EDJ,=,Fs,KLLpppr:)logitsr?r@topktolistzip)r7rmr0rBprobsscoresidslabelss` r) postprocessz'AudioClassificationPipeline.postprocesss  ) )!(+33B77EE ) + +!(+3355EE!(+Ejj'' jjllpppp_bcikn_o_oppp r:)NN)r/r?)__name__ __module__ __qualname____doc__r3rrrarWrPr=rGrirnr __classcell__)r5s@r)r-r-Bs2LLLLL&2bj%,-&2&2&2&2&2&2P****$555nr:r-)rtypingrr`rrRutilsrrrrbaser r models.auto.modeling_autor get_loggerrloggerrWintrKr*r-rkr:r)rs@\\\\\\\\\\\\44444444YXXXXXX  H % %!%!!!!!!H,,4HHHIIggggg(ggJIgggr: