o ZhI@sdZddlZddlmZmZmZmZmZddlZ ddl Z ddl m Z m Z mZddlmZddlmZddlmZmZdd lmZeeZed d Gd d d eZd gZdS)z!Feature extractor class for CLAP.N)AnyDictListOptionalUnion)mel_filter_bank spectrogramwindow_function)SequenceFeatureExtractor) BatchFeature) TensorTypelogging)requires)torch)backendscs eZdZdZddgZ       d(dededeededef fdd Z de ee ffddZ d)de jdee jde jfddZddZde jde jfd d!Z d*d"ee jeeee jeeefdeedeed#eed$eed%eeeefdefd&d'ZZS)+ClapFeatureExtractora Constructs a CLAP feature extractor. This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. This class extracts mel-filter bank features from raw speech using a custom numpy implementation of the *Short Time Fourier Transform* (STFT) which should match pytorch's `torch.stft` equivalent. Args: feature_size (`int`, *optional*, defaults to 64): The feature dimension of the extracted Mel spectrograms. This corresponds to the number of mel filters (`n_mels`). sampling_rate (`int`, *optional*, defaults to 48000): The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). This only serves to warn users if the audio fed to the feature extractor does not have the same sampling rate. hop_length (`int`,*optional*, defaults to 480): Length of the overlapping windows for the STFT used to obtain the Mel Spectrogram. The audio will be split in smaller `frames` with a step of `hop_length` between each frame. max_length_s (`int`, *optional*, defaults to 10): The maximum input length of the model in seconds. This is used to pad the audio. fft_window_size (`int`, *optional*, defaults to 1024): Size of the window (in samples) on which the Fourier transform is applied. This controls the frequency resolution of the spectrogram. 400 means that the fourier transform is computed on windows of 400 samples. padding_value (`float`, *optional*, defaults to 0.0): Padding value used to pad the audio. Should correspond to silences. return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not the model should return the attention masks corresponding to the input. frequency_min (`float`, *optional*, defaults to 0): The lowest frequency of interest. The STFT will not be computed for values below this. frequency_max (`float`, *optional*, defaults to 14000): The highest frequency of interest. The STFT will not be computed for values above this. top_db (`float`, *optional*): The highest decibel value used to convert the mel spectrogram to the log scale. For more details see the `audio_utils.power_to_db` function truncation (`str`, *optional*, defaults to `"fusion"`): Truncation pattern for long audio inputs. Two patterns are available: - `fusion` will use `_random_mel_fusion`, which stacks 3 random crops from the mel spectrogram and a downsampled version of the entire mel spectrogram. If `config.fusion` is set to True, shorter audios also need to to return 4 mels, which will just be a copy of the original mel obtained from the padded audio. - `rand_trunc` will select a random crop of the mel spectrogram. padding (`str`, *optional*, defaults to `"repeatpad"`): Padding pattern for shorter audio inputs. Three patterns were originally implemented: - `repeatpad`: the audio is repeated, and then padded to fit the `max_length`. - `repeat`: the audio is repeated and then cut to fit the `max_length` - `pad`: the audio is padded. input_features is_longer@逻 Fr6Nfusion repeatpad frequency_min frequency_maxtop_db truncationpaddingc  stjd||||d| | |_| |_| |_||_|d?d|_||_||_|||_ ||_ ||_ | |_ t |j||| |ddd|_t |j||| |ddd|_dS)N) feature_size sampling_rate padding_valuereturn_attention_maskZhtk)Znum_frequency_binsZnum_mel_filtersZ min_frequencyZ max_frequencyr$ZnormZ mel_scaleZslaney)super__init__r r!r"fft_window_sizeZnb_frequency_bins hop_length max_length_snb_max_samplesr$rrr mel_filtersmel_filters_slaney)selfr#r$r,r-r+r%r&rrr r!r"kwargs __class__r(_/var/www/auris/lib/python3.10/site-packages/transformers/models/clap/feature_extraction_clap.pyr*VsJ   zClapFeatureExtractor.__init__returncCs8t|j}|jj|d<d|vr|d=d|vr|d=|S)a) Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance, except for the mel filter banks, which do not need to be saved or printed as they are too long. Zfeature_extractor_typer/r0)copydeepcopy__dict__r4__name__)r1outputr(r(r5to_dicts  zClapFeatureExtractor.to_dictwaveformr/c Cs(t|t|jd|j|jd|dd}|jS)a Compute the log-mel spectrogram of the provided `waveform` using the Hann window. In CLAP, two different filter banks are used depending on the truncation pattern: - `self.mel_filters`: they correspond to the default parameters of `torchaudio` which can be obtained from calling `torchaudio.transforms.MelSpectrogram().mel_scale.fb`. These filters are used when `truncation` is set to `"fusion"`. - `self.mel_filteres_slaney` : they correspond to the default parameters of `librosa` which used `librosa.filters.mel` when computing the mel spectrogram. These filters were only used in the original implementation when the truncation mode is not `"fusion"`. Zhanng@ZdB)Z frame_lengthr,powerr/Zlog_mel)r r r+r,T)r1r=r/Zlog_mel_spectrogramr(r(r5_np_extract_fbank_featuress   z/ClapFeatureExtractor._np_extract_fbank_featuresc Cs&tttd||dd}t|ddkrdg|d<t|ddkr)dg|d<tj|d}tj|d}tj|d}||||ddf}||||ddf} ||||ddf} t|ddddf}tj j j ||dgddd} | dd } tj | || | gdd } | S) Nrr'rrZbilinearF)sizemodeZ align_cornersZaxis)npZ array_splitlistrangelenrandomchoicerZtensornnZ functionalZ interpolatenumpystack) r1mel total_frames chunk_framesrangesZ idx_frontZ idx_middleZidx_backZmel_chunk_frontZmel_chunk_middleZmel_chunk_backZ mel_shrinkZ mel_fusionr(r(r5_random_mel_fusions$   z'ClapFeatureExtractor._random_mel_fusionc Cs|jd|krv|dkr5d}t||}tjd|d}||||}|||jdddf}||fS|dkrn|||j} ||jd} | jd} | | kratj | | | | gdd}d}||fS| | | | }d}||fSt d |d d}|jd|kr|d krt |t|} t || dd|}|d krt |t|} t || }tj|d||jdfd dd}|dkr|||j}tj ||||gdd}||fS|||jdddf}||fS)a Extracts the mel spectrogram and prepares it for the mode based on the `truncation` and `padding` arguments. Four different path are possible: - `truncation="fusion"` and the length of the waveform is greater than the max length: the mel spectrogram will be computed on the entire audio. 3 random crops and a dowsampled version of the full mel spectrogram are then stacked together. They will later be used for `feature_fusion`. - `truncation="rand_trunc"` and the length of the waveform is smaller than the max length: the audio is padded based on `padding`. - `truncation="fusion"` and the length of the waveform is smaller than the max length: the audio is padded based on `padding`, and is repeated `4` times. - `truncation="rand_trunc"` and the length of the waveform is greater than the max length: the mel spectrogram will be computed on a random crop of the waveform. rZ rand_truncTr'NrrDFzdata_truncating z not implementedrepeatrZconstant)rCZconstant_values)shaperHrErIrandintr@r0r/r,rMrRNotImplementedErrorintZtilepad) r1r= max_lengthr!r"longeroverflowidx input_melrNrPrOZn_repeatr(r(r5_get_input_melsF "   z#ClapFeatureExtractor._get_input_mel raw_speechrYr$return_tensorsc s durnjr nj|dur/|jkr.tdjjdjdjd|d n tdjjdt|t j oFt |j d k}|rWt |j d krWtd |pjt|t tfojt|d t j tt f} | rud d|D}n&| st|t j st j|t jd}nt|t j r|jt t jur|t j}| st |g}fdd|D} g} g} | D]\} }| | | |qdkrt| d krt jd t | }d| |<t| d trdd| D} dd| D} | | d}t|}|dur||}|S)af Main method to featurize and prepare for the model one or several sequence(s). Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep. truncation (`str`, *optional*): Truncation pattern for long audio inputs. Two patterns are available: - `fusion` will use `_random_mel_fusion`, which stacks 3 random crops from the mel spectrogram and a downsampled version of the entire mel spectrogram. If `config.fusion` is set to True, shorter audios also need to to return 4 mels, which will just be a copy of the original mel obtained from the padded audio. - `rand_trunc` will select a random crop of the mel spectrogram. padding (`str`, *optional*): Padding pattern for shorter audio inputs. Three patterns were originally implemented: - `repeatpad`: the audio is repeated, and then padded to fit the `max_length`. - `repeat`: the audio is repeated and then cut to fit the `max_length` - `pad`: the audio is padded. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.np.array` objects. - `'np'`: Return Numpy `np.ndarray` objects. sampling_rate (`int`, *optional*): The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition pipeline. Nz3The model corresponding to this feature extractor: z& was trained using a sampling rate of zI. 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Failing to do so can result in silent errors that might be hard to debug.r'rAz2Only mono-channel audio is supported for input to rcSg|] }tj|tjdqSdtyperEasarrayfloat64).0Zspeechr(r(r5 Ez1ClapFeatureExtractor.__call__..rdcs&g|]}|r njqSr()r^r.)rir=rYr"r1r!r(r5rjPsrTcSrbrcrf)rifeaturer(r(r5rjarkcSsg|]}|gqSr(r()rirZr(r(r5rjds)rr)r!r"r$ ValueErrorr4r:loggerwarning isinstancerEndarrayrHrTrFtuplergrhreZastypeappendsumrIrUrr Zconvert_to_tensors)r1r_r!r"rYr$r`r2Zis_batched_numpyZ is_batchedZ padded_inputsr]rrNrZZrand_idxrr(rlr5__call__sd)  "        zClapFeatureExtractor.__call__) rrrrrrFrrNrr)N)NNNNN)r: __module__ __qualname____doc__Zmodel_input_namesfloatrrWstrr*rrr<rEarrayrrr@rRr^rrr r rv __classcell__r(r(r3r5r!sd1     5"=" r)ryr7typingrrrrrrLrErZ audio_utilsrr r Z!feature_extraction_sequence_utilsr Zfeature_extraction_utilsr utilsr rZutils.import_utilsrZ get_loggerr:ror__all__r(r(r(r5s      O