# Copyright 2023 The HuggingFace Team. All rights reserved. # # 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 UnivNetModel.""" from typing import Any, Dict, List, Optional, Union import numpy as np from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging logger = logging.get_logger(__name__) class UnivNetFeatureExtractor(SequenceFeatureExtractor): r""" Constructs a UnivNet feature extractor. This class extracts log-mel-filter bank features from raw speech using the short time Fourier Transform (STFT). The STFT implementation follows that of TacoTron 2 and Hifi-GAN. 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. Args: feature_size (`int`, *optional*, defaults to 1): The feature dimension of the extracted features. sampling_rate (`int`, *optional*, defaults to 24000): The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). padding_value (`float`, *optional*, defaults to 0.0): The value to pad with when applying the padding strategy defined by the `padding` argument to [`UnivNetFeatureExtractor.__call__`]. Should correspond to audio silence. The `pad_end` argument to `__call__` will also use this padding value. do_normalize (`bool`, *optional*, defaults to `False`): Whether to perform Tacotron 2 normalization on the input. Normalizing can help to significantly improve the performance for some models. num_mel_bins (`int`, *optional*, defaults to 100): The number of mel-frequency bins in the extracted spectrogram features. This should match `UnivNetModel.config.num_mel_bins`. hop_length (`int`, *optional*, defaults to 256): The direct number of samples between sliding windows. Otherwise referred to as "shift" in many papers. Note that this is different from other audio feature extractors such as [`SpeechT5FeatureExtractor`] which take the `hop_length` in ms. win_length (`int`, *optional*, defaults to 1024): The direct number of samples for each sliding window. Note that this is different from other audio feature extractors such as [`SpeechT5FeatureExtractor`] which take the `win_length` in ms. win_function (`str`, *optional*, defaults to `"hann_window"`): Name for the window function used for windowing, must be accessible via `torch.{win_function}` filter_length (`int`, *optional*, defaults to 1024): The number of FFT components to use. If `None`, this is determined using `transformers.audio_utils.optimal_fft_length`. max_length_s (`int`, *optional*, defaults to 10): The maximum input lenght of the model in seconds. This is used to pad the audio. fmin (`float`, *optional*, defaults to 0.0): Minimum mel frequency in Hz. fmax (`float`, *optional*): Maximum mel frequency in Hz. If not set, defaults to `sampling_rate / 2`. mel_floor (`float`, *optional*, defaults to 1e-09): Minimum value of mel frequency banks. Note that the way [`UnivNetFeatureExtractor`] uses `mel_floor` is different than in [`transformers.audio_utils.spectrogram`]. center (`bool`, *optional*, defaults to `False`): Whether to pad the waveform so that frame `t` is centered around time `t * hop_length`. If `False`, frame `t` will start at time `t * hop_length`. compression_factor (`float`, *optional*, defaults to 1.0): The multiplicative compression factor for dynamic range compression during spectral normalization. compression_clip_val (`float`, *optional*, defaults to 1e-05): The clip value applied to the waveform before applying dynamic range compression during spectral normalization. normalize_min (`float`, *optional*, defaults to -11.512925148010254): The min value used for Tacotron 2-style linear normalization. The default is the original value from the Tacotron 2 implementation. normalize_max (`float`, *optional*, defaults to 2.3143386840820312): The max value used for Tacotron 2-style linear normalization. The default is the original value from the Tacotron 2 implementation. model_in_channels (`int`, *optional*, defaults to 64): The number of input channels to the [`UnivNetModel`] model. This should match `UnivNetModel.config.model_in_channels`. pad_end_length (`int`, *optional*, defaults to 10): If padding the end of each waveform, the number of spectrogram frames worth of samples to append. The number of appended samples will be `pad_end_length * hop_length`. return_attention_mask (`bool`, *optional*, defaults to `True`): Whether or not [`~UnivNetFeatureExtractor.__call__`] should return `attention_mask`. """ model_input_names = ["input_features", "noise_sequence", "padding_mask"] def __init__( self, feature_size: int = 1, sampling_rate: int = 24000, padding_value: float = 0.0, do_normalize: bool = False, num_mel_bins: int = 100, hop_length: int = 256, win_length: int = 1024, win_function: str = "hann_window", filter_length: Optional[int] = 1024, max_length_s: int = 10, fmin: float = 0.0, fmax: Optional[float] = None, mel_floor: float = 1e-9, center: bool = False, compression_factor: float = 1.0, compression_clip_val: float = 1e-5, normalize_min: float = -11.512925148010254, normalize_max: float = 2.3143386840820312, model_in_channels: int = 64, pad_end_length: int = 10, return_attention_mask=True, **kwargs, ): super().__init__( feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, return_attention_mask=return_attention_mask, **kwargs, ) self.do_normalize = do_normalize self.num_mel_bins = num_mel_bins self.hop_length = hop_length self.win_length = win_length self.win_function = win_function self.filter_length = filter_length self.fmin = fmin if fmax is None: # Follows the librosa.filters.mel implementation fmax = float(sampling_rate) / 2 self.fmax = fmax self.mel_floor = mel_floor self.max_length_s = max_length_s self.num_max_samples = max_length_s * sampling_rate if self.filter_length is None: self.n_fft = optimal_fft_length(self.win_length) else: self.n_fft = self.filter_length self.n_freqs = (self.n_fft // 2) + 1 self.window = window_function(window_length=self.win_length, name=self.win_function, periodic=True) self.mel_filters = mel_filter_bank( num_frequency_bins=self.n_freqs, num_mel_filters=self.num_mel_bins, min_frequency=self.fmin, max_frequency=self.fmax, sampling_rate=self.sampling_rate, norm="slaney", mel_scale="slaney", ) self.center = center self.compression_factor = compression_factor self.compression_clip_val = compression_clip_val self.normalize_min = normalize_min self.normalize_max = normalize_max self.model_in_channels = model_in_channels self.pad_end_length = pad_end_length def normalize(self, spectrogram): return 2 * ((spectrogram - self.normalize_min) / (self.normalize_max - self.normalize_min)) - 1 def denormalize(self, spectrogram): return self.normalize_min + (self.normalize_max - self.normalize_min) * ((spectrogram + 1) / 2) def mel_spectrogram(self, waveform: np.ndarray) -> np.ndarray: """ Calculates log MEL spectrograms from a batch of waveforms. Note that the input waveform(s) will be padded by `int(self.n_fft - self.hop_length) / 2` on both sides using the `reflect` padding mode. Args: waveform (`np.ndarray` of shape `(length,)`): The input waveform. This must be a single real-valued, mono waveform. Returns: `numpy.ndarray`: Array containing a log-mel spectrogram of shape `(num_frames, num_mel_bins)`. """ # Do custom padding based on the official MelGAN and Hifi-GAN implementations # See https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/utils/stft.py#L84-L86 waveform = np.pad( waveform, (int((self.n_fft - self.hop_length) / 2), int((self.n_fft - self.hop_length) / 2)), mode="reflect", ) # Get the complex spectrogram. # Note: waveform must be unbatched currently due to the implementation of spectrogram(...). complex_spectrogram = spectrogram( waveform, window=self.window, frame_length=self.n_fft, hop_length=self.hop_length, fft_length=self.n_fft, power=None, center=self.center, mel_filters=None, mel_floor=None, ) # Apply the MEL filter bank and MEL floor manually since UnivNet uses a slightly different implementation amplitude_spectrogram = np.sqrt( np.real(complex_spectrogram) ** 2 + np.imag(complex_spectrogram) ** 2 + self.mel_floor ) mel_spectrogram = np.matmul(self.mel_filters.T, amplitude_spectrogram) # Perform spectral normalization to get the log mel spectrogram. log_mel_spectrogram = np.log( np.clip(mel_spectrogram, a_min=self.compression_clip_val, a_max=None) * self.compression_factor ) # Return spectrogram with num_mel_bins last return log_mel_spectrogram.T def generate_noise( self, noise_length: int, generator: Optional[np.random.Generator] = None, ) -> np.ndarray: """ Generates a random noise sequence of standard Gaussian noise for use in the `noise_sequence` argument of [`UnivNetModel.forward`]. Args: spectrogram_length (`int`): The length (dim 0) of the generated noise. model_in_channels (`int`, *optional*, defaults to `None`): The number of features (dim 1) of the generated noise. This should correspond to the `model_in_channels` of the [`UnivNetGan`] model. If not set, this will default to `self.config.model_in_channels`. generator (`numpy.random.Generator`, *optional*, defaults to `None`) An optional `numpy.random.Generator` random number generator to control noise generation. If not set, a new generator with fresh entropy will be created. Returns: `numpy.ndarray`: Array containing random standard Gaussian noise of shape `(noise_length, model_in_channels)`. """ if generator is None: generator = np.random.default_rng() noise_shape = (noise_length, self.model_in_channels) noise = generator.standard_normal(noise_shape, dtype=np.float32) return noise def batch_decode(self, waveforms, waveform_lengths=None) -> List[np.ndarray]: r""" Removes padding from generated audio after running [`UnivNetModel.forward`]. This returns a ragged list of 1D audio waveform arrays and not a single tensor/array because in general the waveforms will have different lengths after removing padding. Args: waveforms (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): The batched output waveforms from the [`UnivNetModel`]. waveform_lengths (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): The batched lengths of each waveform before padding. Returns: `List[np.ndarray]`: A ragged list of 1D waveform arrays with padding removed. """ # Collapse the batched waveform tensor to a list of 1D audio waveforms waveforms = [waveform.detach().clone().cpu().numpy() for waveform in waveforms] if waveform_lengths is not None: waveforms = [waveform[: waveform_lengths[i]] for i, waveform in enumerate(waveforms)] return waveforms def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], sampling_rate: Optional[int] = None, padding: Union[bool, str, PaddingStrategy] = True, max_length: Optional[int] = None, truncation: bool = True, pad_to_multiple_of: Optional[int] = None, return_noise: bool = True, generator: Optional[np.random.Generator] = None, pad_end: bool = False, pad_length: Optional[int] = None, do_normalize: Optional[str] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, ) -> BatchFeature: """ 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. 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. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the input `raw_speech` waveforms (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). If `pad_end = True`, that padding will occur before the `padding` strategy is applied. max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). truncation (`bool`, *optional*, defaults to `True`): 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_noise (`bool`, *optional*, defaults to `True`): Whether to generate and return a noise waveform for use in [`UnivNetModel.forward`]. generator (`numpy.random.Generator`, *optional*, defaults to `None`): An optional `numpy.random.Generator` random number generator to use when generating noise. pad_end (`bool`, *optional*, defaults to `False`): Whether to pad the end of each waveform with silence. This can help reduce artifacts at the end of the generated audio sample; see https://github.com/seungwonpark/melgan/issues/8 for more details. This padding will be done before the padding strategy specified in `padding` is performed. pad_length (`int`, *optional*, defaults to `None`): If padding the end of each waveform, the length of the padding in spectrogram frames. If not set, this will default to `self.config.pad_end_length`. do_normalize (`bool`, *optional*): Whether to perform Tacotron 2 normalization on the input. Normalizing can help to significantly improve the performance for some models. If not set, this will default to `self.config.do_normalize`. 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) 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. """ do_normalize = do_normalize if do_normalize is not None else self.do_normalize if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a" f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input" f" was sampled with {self.sampling_rate} and not {sampling_rate}." ) else: logger.warning( f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. " "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) # always return batch if not is_batched: raw_speech = [np.asarray(raw_speech, dtype=np.float32)] # Pad end to reduce artifacts if pad_end: pad_length = pad_length if pad_length is not None else self.pad_end_length raw_speech = [ np.pad(waveform, (0, pad_length * self.hop_length), constant_values=self.padding_value) for waveform in raw_speech ] batched_speech = BatchFeature({"input_features": raw_speech}) padded_inputs = self.pad( batched_speech, padding=padding, max_length=max_length if max_length is not None else self.num_max_samples, truncation=truncation, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) # make sure list is in array format # input_features = padded_inputs.get("input_features").transpose(2, 0, 1) input_features = padded_inputs.get("input_features") mel_spectrograms = [self.mel_spectrogram(waveform) for waveform in input_features] if isinstance(input_features[0], List): batched_speech["input_features"] = [np.asarray(mel, dtype=np.float32) for mel in mel_spectrograms] else: batched_speech["input_features"] = [mel.astype(np.float32) for mel in mel_spectrograms] # convert attention_mask to correct format attention_mask = padded_inputs.get("attention_mask") if attention_mask is not None: batched_speech["padding_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask] if return_noise: noise = [ self.generate_noise(spectrogram.shape[0], generator) for spectrogram in batched_speech["input_features"] ] batched_speech["noise_sequence"] = noise if do_normalize: batched_speech["input_features"] = [ self.normalize(spectrogram) for spectrogram in batched_speech["input_features"] ] if return_tensors is not None: batched_speech = batched_speech.convert_to_tensors(return_tensors) return batched_speech def to_dict(self) -> Dict[str, Any]: output = super().to_dict() # Don't serialize these as they are derived from the other properties. names = ["window", "mel_filters", "n_fft", "n_freqs", "num_max_samples"] for name in names: if name in output: del output[name] return output __all__ = ["UnivNetFeatureExtractor"]