codingutf8 2021 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license feature extractor class for speech2text modelinputnames inputfeatures attentionmask def init self featuresize80 samplingrate16000 nummelbins80 paddingvalue0 0 doceptralnormalizetrue normalizemeanstrue normalizevarstrue kwargs super initfeaturesizefeaturesize samplingratesamplingrate paddingvaluepaddingvalue kwargs self nummelbins nummelbins self doceptralnormalize doceptralnormalize self normalizemeans normalizemeans self normalizevars normalizevars self returnattentionmask true if not isspeechavailable melfilters melfilterbank numfrequencybins256 nummelfiltersself nummelbins minfrequency20 maxfrequencysamplingrate 2 samplingratesamplingrate normnone melscalekaldi triangularizeinmelspacetrue self melfilters np padmelfilters 0 1 0 0 self window windowfunction400 povey periodicfalse def extractfbankfeatures self waveform np ndarray np ndarray waveform waveform 215 kaldi compliance 16bit signed integers if isspeechavailable waveform torch fromnumpywaveform unsqueeze0 features takaldi fbankwaveform nummelbinsself nummelbins samplefrequencyself samplingrate features features numpy else waveform np squeezewaveform features spectrogram waveform self window framelength400 hoplength160 fftlength512 power2 0 centerfalse preemphasis0 97 melfiltersself melfilters logmellog melfloor1 192092955078125e07 removedcoffsettrue t return features staticmethod def utterancecmvn x np ndarray inputlength int normalizemeans optionalbool true normalizevars optionalbool true paddingvalue float 0 0 np ndarray make sure we normalize float32 arrays if normalizemeans mean x inputlength meanaxis0 x np subtractx mean if normalizevars std x inputlength stdaxis0 x np dividex std if inputlength x shape0 xinputlength paddingvalue make sure array is in float32 x x astypenp float32 return x def normalize self inputfeatures listnp ndarray attentionmask optionalnp ndarray none listnp ndarray lengths attentionmask sum1 if attentionmask is not none else x shape0 for x in inputfeatures return self utterancecmvnx n self normalizemeans self normalizevars self paddingvalue for x n in zipinputfeatures lengths def call self rawspeech unionnp ndarray listfloat listnp ndarray listlistfloat padding unionbool str paddingstrategy false maxlength optionalint none truncation bool false padtomultipleof optionalint none returntensors optionalunionstr tensortype none samplingrate optionalint none returnattentionmask optionalbool none kwargs batchfeature if samplingrate is not none if samplingrate self samplingrate raise valueerror fthe model corresponding to this feature extractor self was trained using a sampling rate of f self samplingrate please make sure that the provided rawspeech input was sampled with f self samplingrate and not samplingrate else logger warning it is strongly recommended to pass the samplingrate argument to this function failing to do so can result in silent errors that might be hard to debug isbatchednumpy isinstancerawspeech np ndarray and lenrawspeech shape 1 if isbatchednumpy and lenrawspeech shape 2 raise valueerrorfonly monochannel audio is supported for input to self isbatched isbatchednumpy or isinstancerawspeech list tuple and isinstancerawspeech0 np ndarray tuple list if isbatched rawspeech np asarrayspeech dtypenp float32 for speech in rawspeech elif not isbatched and not isinstancerawspeech np ndarray rawspeech np asarrayrawspeech dtypenp float32 elif isinstancerawspeech np ndarray and rawspeech dtype is np dtypenp float64 rawspeech rawspeech astypenp float32 always return batch if not isbatched rawspeech rawspeech extract fbank features features self extractfbankfeatureswaveform for waveform in rawspeech convert into correct format for padding encodedinputs batchfeatureinputfeatures features paddedinputs self pad encodedinputs paddingpadding maxlengthmaxlength truncationtruncation padtomultipleofpadtomultipleof returnattentionmaskreturnattentionmask kwargs make sure list is in array format inputfeatures paddedinputs getinputfeatures if isinstanceinputfeatures0 list paddedinputsinputfeatures np asarrayfeature dtypenp float32 for feature in inputfeatures attentionmask paddedinputs getattentionmask if attentionmask is not none paddedinputsattentionmask np asarrayarray dtypenp int32 for array in attentionmask utterancelevel cepstral mean and variance normalization if self doceptralnormalize attentionmask np arrayattentionmask dtypenp int32 if self getpaddingstrategiespadding maxlengthmaxlength is not paddingstrategy donotpad else none paddedinputsinputfeatures self normalize paddedinputsinputfeatures attentionmaskattentionmask if returntensors is not none paddedinputs paddedinputs converttotensorsreturntensors return paddedinputs coding utf 8 2021 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license feature extractor class for speech2text constructs a speech2text feature extractor this feature extractor inherits from speech2textfeatureextractor 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 torchaudio if installed or using numpy otherwise and applies utterance level cepstral mean and variance normalization to the extracted features args feature_size int optional defaults to 80 the feature dimension of the extracted features sampling_rate int optional defaults to 16000 the sampling rate at which the audio files should be digitalized expressed in hertz hz num_mel_bins int optional defaults to 80 number of mel frequency bins padding_value float optional defaults to 0 0 the value that is used to fill the padding vectors do_ceptral_normalize bool optional defaults to true whether or not to apply utterance level cepstral mean and variance normalization to extracted features normalize_means bool optional defaults to true whether or not to zero mean normalize the extracted features normalize_vars bool optional defaults to true whether or not to unit variance normalize the extracted features get mel filter bank features using torchaudio note that torchaudio requires 16 bit signed integers as inputs and hence the waveform should not be normalized before feature extraction kaldi compliance 16 bit signed integers make sure we normalize float32 arrays make sure array is in float32 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 padding bool str or utils paddingstrategy optional defaults to true select a strategy to pad the returned sequences according to the model s padding side and padding index among true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided max_length pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided false or do_not_pad default no padding i e can output a batch with sequences of different lengths max_length int optional maximum length of the returned list and optionally padding length see above truncation bool activates truncation to cut input sequences longer than max_length to max_length pad_to_multiple_of int optional if set will pad the sequence to a multiple of the provided value this is especially useful to enable the use of tensor cores on nvidia hardware with compute capability 7 5 volta or on tpus which benefit from having sequence lengths be a multiple of 128 return_attention_mask bool optional whether to return the attention mask if left to the default will return the attention mask according to the specific feature_extractor s default what are attention masks glossary attention mask tip for speech2texttransformer models attention_mask should always be passed for batched inference to avoid subtle bugs tip 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 tensor 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 padding_value float defaults to 0 0 the value that is used to fill the padding values vectors always return batch extract fbank features convert into correct format for padding make sure list is in array format utterance level cepstral mean and variance normalization
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, is_speech_available, logging


if is_speech_available():
    import torch
    import torchaudio.compliance.kaldi as ta_kaldi

logger = logging.get_logger(__name__)


class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
    r

    model_input_names = ["input_features", "attention_mask"]

    def __init__(
        self,
        feature_size=80,
        sampling_rate=16000,
        num_mel_bins=80,
        padding_value=0.0,
        do_ceptral_normalize=True,
        normalize_means=True,
        normalize_vars=True,
        **kwargs,
    ):
        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
        self.num_mel_bins = num_mel_bins
        self.do_ceptral_normalize = do_ceptral_normalize
        self.normalize_means = normalize_means
        self.normalize_vars = normalize_vars
        self.return_attention_mask = True

        if not is_speech_available():
            mel_filters = mel_filter_bank(
                num_frequency_bins=256,
                num_mel_filters=self.num_mel_bins,
                min_frequency=20,
                max_frequency=sampling_rate // 2,
                sampling_rate=sampling_rate,
                norm=None,
                mel_scale="kaldi",
                triangularize_in_mel_space=True,
            )

            self.mel_filters = np.pad(mel_filters, ((0, 1), (0, 0)))
            self.window = window_function(400, "povey", periodic=False)

    def _extract_fbank_features(
        self,
        waveform: np.ndarray,
    ) -> np.ndarray:
        
        waveform = waveform * (2**15)  
        if is_speech_available():
            waveform = torch.from_numpy(waveform).unsqueeze(0)
            features = ta_kaldi.fbank(waveform, num_mel_bins=self.num_mel_bins, sample_frequency=self.sampling_rate)
            features = features.numpy()
        else:
            waveform = np.squeeze(waveform)
            features = spectrogram(
                waveform,
                self.window,
                frame_length=400,
                hop_length=160,
                fft_length=512,
                power=2.0,
                center=False,
                preemphasis=0.97,
                mel_filters=self.mel_filters,
                log_mel="log",
                mel_floor=1.192092955078125e-07,
                remove_dc_offset=True,
            ).T
        return features

    @staticmethod
    def utterance_cmvn(
        x: np.ndarray,
        input_length: int,
        normalize_means: Optional[bool] = True,
        normalize_vars: Optional[bool] = True,
        padding_value: float = 0.0,
    ) -> np.ndarray:
        
        if normalize_means:
            mean = x[:input_length].mean(axis=0)
            x = np.subtract(x, mean)
        if normalize_vars:
            std = x[:input_length].std(axis=0)
            x = np.divide(x, std)

        if input_length < x.shape[0]:
            x[input_length:] = padding_value

        
        x = x.astype(np.float32)

        return x

    def normalize(
        self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None
    ) -> List[np.ndarray]:
        lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(x, n, self.normalize_means, self.normalize_vars, self.padding_value)
            for x, n in zip(input_features, lengths)
        ]

    def __call__(
        self,
        raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
        padding: Union[bool, str, PaddingStrategy] = False,
        max_length: Optional[int] = None,
        truncation: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        sampling_rate: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        **kwargs,
    ) -> BatchFeature:
        

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                    f" {self.sampling_rate} and not {sampling_rate}."
                )
        else:
            logger.warning(
                "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                "Failing to do so can result in silent errors that might be hard to debug."
            )

        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
        if is_batched_numpy and len(raw_speech.shape) > 2:
            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
        is_batched = is_batched_numpy or (
            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
        )

        if is_batched:
            raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech]
        elif not is_batched and not isinstance(raw_speech, np.ndarray):
            raw_speech = np.asarray(raw_speech, dtype=np.float32)
        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64):
            raw_speech = raw_speech.astype(np.float32)

        
        if not is_batched:
            raw_speech = [raw_speech]

        
        features = [self._extract_fbank_features(waveform) for waveform in raw_speech]

        
        encoded_inputs = BatchFeature({"input_features": features})

        padded_inputs = self.pad(
            encoded_inputs,
            padding=padding,
            max_length=max_length,
            truncation=truncation,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
            **kwargs,
        )

        
        input_features = padded_inputs.get("input_features")
        if isinstance(input_features[0], list):
            padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]

        attention_mask = padded_inputs.get("attention_mask")
        if attention_mask is not None:
            padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask]

        
        if self.do_ceptral_normalize:
            attention_mask = (
                np.array(attention_mask, dtype=np.int32)
                if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
                else None
            )
            padded_inputs["input_features"] = self.normalize(
                padded_inputs["input_features"], attention_mask=attention_mask
            )

        if return_tensors is not None:
            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)

        return padded_inputs