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UnivNet

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UnivNet

Overview

The UnivNet model was proposed in UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kin, and Juntae Kim. The UnivNet model is a generative adversarial network (GAN) trained to synthesize high fidelity speech waveforms. The UnivNet model shared in transformers is the generator, which maps a conditioning log-mel spectrogram and optional noise sequence to a speech waveform (e.g. a vocoder). Only the generator is required for inference. The discriminator used to train the generator is not implemented.

The abstract from the paper is the following:

Most neural vocoders employ band-limited mel-spectrograms to generate waveforms. If full-band spectral features are used as the input, the vocoder can be provided with as much acoustic information as possible. However, in some models employing full-band mel-spectrograms, an over-smoothing problem occurs as part of which non-sharp spectrograms are generated. To address this problem, we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms in real time. Inspired by works in the field of voice activity detection, we added a multi-resolution spectrogram discriminator that employs multiple linear spectrogram magnitudes computed using various parameter sets. Using full-band mel-spectrograms as input, we expect to generate high-resolution signals by adding a discriminator that employs spectrograms of multiple resolutions as the input. In an evaluation on a dataset containing information on hundreds of speakers, UnivNet obtained the best objective and subjective results among competing models for both seen and unseen speakers. These results, including the best subjective score for text-to-speech, demonstrate the potential for fast adaptation to new speakers without a need for training from scratch.

Tips:

  • The noise_sequence argument for UnivNetModel.forward() should be standard Gaussian noise (such as from torch.randn) of shape ([batch_size], noise_length, model.config.model_in_channels), where noise_length should match the length dimension (dimension 1) of the input_features argument. If not supplied, it will be randomly generated; a torch.Generator can be supplied to the generator argument so that the forward pass can be reproduced. (Note that UnivNetFeatureExtractor will return generated noise by default, so it shouldn’t be necessary to generate noise_sequence manually.)
  • Padding added by UnivNetFeatureExtractor can be removed from the UnivNetModel output through the UnivNetFeatureExtractor.batch_decode() method, as shown in the usage example below.
  • Padding the end of each waveform with silence can reduce artifacts at the end of the generated audio sample. This can be done by supplying pad_end = True to UnivNetFeatureExtractor.call(). See this issue for more details.

Usage Example:

import torch
from scipy.io.wavfile import write
from datasets import Audio, load_dataset

from transformers import UnivNetFeatureExtractor, UnivNetModel

model_id_or_path = "dg845/univnet-dev"
model = UnivNetModel.from_pretrained(model_id_or_path)
feature_extractor = UnivNetFeatureExtractor.from_pretrained(model_id_or_path)

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# Resample the audio to the model and feature extractor's sampling rate.
ds = ds.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
# Pad the end of the converted waveforms to reduce artifacts at the end of the output audio samples.
inputs = feature_extractor(
    ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], pad_end=True, return_tensors="pt"
)

with torch.no_grad():
    audio = model(**inputs)

# Remove the extra padding at the end of the output.
audio = feature_extractor.batch_decode(**audio)[0]
# Convert to wav file
write("sample_audio.wav", feature_extractor.sampling_rate, audio)

This model was contributed by dg845. To the best of my knowledge, there is no official code release, but an unofficial implementation can be found at maum-ai/univnet with pretrained checkpoints here.

UnivNetConfig

class transformers.UnivNetConfig

< >

( model_in_channels = 64 model_hidden_channels = 32 num_mel_bins = 100 resblock_kernel_sizes = [3, 3, 3] resblock_stride_sizes = [8, 8, 4] resblock_dilation_sizes = [[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]] kernel_predictor_num_blocks = 3 kernel_predictor_hidden_channels = 64 kernel_predictor_conv_size = 3 kernel_predictor_dropout = 0.0 initializer_range = 0.01 leaky_relu_slope = 0.2 **kwargs )

Parameters

  • model_in_channels (int, optional, defaults to 64) — The number of input channels for the UnivNet residual network. This should correspond to noise_sequence.shape[1] and the value used in the UnivNetFeatureExtractor class.
  • model_hidden_channels (int, optional, defaults to 32) — The number of hidden channels of each residual block in the UnivNet residual network.
  • num_mel_bins (int, optional, defaults to 100) — The number of frequency bins in the conditioning log-mel spectrogram. This should correspond to the value used in the UnivNetFeatureExtractor class.
  • resblock_kernel_sizes (Tuple[int] or List[int], optional, defaults to [3, 3, 3]) — A tuple of integers defining the kernel sizes of the 1D convolutional layers in the UnivNet residual network. The length of resblock_kernel_sizes defines the number of resnet blocks and should match that of resblock_stride_sizes and resblock_dilation_sizes.
  • resblock_stride_sizes (Tuple[int] or List[int], optional, defaults to [8, 8, 4]) — A tuple of integers defining the stride sizes of the 1D convolutional layers in the UnivNet residual network. The length of resblock_stride_sizes should match that of resblock_kernel_sizes and resblock_dilation_sizes.
  • resblock_dilation_sizes (Tuple[Tuple[int]] or List[List[int]], optional, defaults to [[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]]) — A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the UnivNet residual network. The length of resblock_dilation_sizes should match that of resblock_kernel_sizes and resblock_stride_sizes. The length of each nested list in resblock_dilation_sizes defines the number of convolutional layers per resnet block.
  • kernel_predictor_num_blocks (int, optional, defaults to 3) — The number of residual blocks in the kernel predictor network, which calculates the kernel and bias for each location variable convolution layer in the UnivNet residual network.
  • kernel_predictor_hidden_channels (int, optional, defaults to 64) — The number of hidden channels for each residual block in the kernel predictor network.
  • kernel_predictor_conv_size (int, optional, defaults to 3) — The kernel size of each 1D convolutional layer in the kernel predictor network.
  • kernel_predictor_dropout (float, optional, defaults to 0.0) — The dropout probability for each residual block in the kernel predictor network.
  • initializer_range (float, optional, defaults to 0.01) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • leaky_relu_slope (float, optional, defaults to 0.2) — The angle of the negative slope used by the leaky ReLU activation.

This is the configuration class to store the configuration of a UnivNetModel. It is used to instantiate a UnivNet vocoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the UnivNet dg845/univnet-dev architecture, which corresponds to the ‘c32’ architecture in maum-ai/univnet.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

>>> from transformers import UnivNetModel, UnivNetConfig

>>> # Initializing a Tortoise TTS style configuration
>>> configuration = UnivNetConfig()

>>> # Initializing a model (with random weights) from the Tortoise TTS style configuration
>>> model = UnivNetModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

UnivNetFeatureExtractor

class transformers.UnivNetFeatureExtractor

< >

( 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: typing.Optional[int] = 1024 max_length_s: int = 10 fmin: float = 0.0 fmax: typing.Optional[float] = None mel_floor: float = 1e-09 center: bool = False compression_factor: float = 1.0 compression_clip_val: float = 1e-05 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 )

Parameters

  • 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 call() should return attention_mask.

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 SequenceFeatureExtractor which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

__call__

< >

( raw_speech: typing.Union[numpy.ndarray, typing.List[float], typing.List[numpy.ndarray], typing.List[typing.List[float]]] sampling_rate: typing.Optional[int] = None padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True max_length: typing.Optional[int] = None truncation: bool = True pad_to_multiple_of: typing.Optional[int] = None return_noise: bool = True generator: typing.Optional[numpy.random._generator.Generator] = None pad_end: bool = False pad_length: typing.Optional[int] = None do_normalize: typing.Optional[str] = None return_attention_mask: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None )

Parameters

  • 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 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?

  • return_tensors (str or 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.

Main method to featurize and prepare for the model one or several sequence(s).

UnivNetModel

class transformers.UnivNetModel

< >

( config: UnivNetConfig )

Parameters

  • config (UnivNetConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

UnivNet GAN vocoder. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( input_features: FloatTensor noise_sequence: typing.Optional[torch.FloatTensor] = None padding_mask: typing.Optional[torch.FloatTensor] = None generator: typing.Optional[torch._C.Generator] = None return_dict: typing.Optional[bool] = None ) → transformers.models.univnet.modeling_univnet.UnivNetModelOutput or tuple(torch.FloatTensor)

Parameters

  • input_features (torch.FloatTensor) — Tensor containing the log-mel spectrograms. Can be batched and of shape (batch_size, sequence_length, config.num_mel_channels), or un-batched and of shape (sequence_length, config.num_mel_channels).
  • noise_sequence (torch.FloatTensor, optional) — Tensor containing a noise sequence of standard Gaussian noise. Can be batched and of shape (batch_size, sequence_length, config.model_in_channels), or un-batched and of shape (sequence_length, config.model_in_channels)`. If not supplied, will be randomly generated.
  • padding_mask (torch.BoolTensor, optional) — Mask indicating which parts of each sequence are padded. Mask values are selected in [0, 1]:

    • 1 for tokens that are not masked
    • 0 for tokens that are masked

    The mask can be batched and of shape (batch_size, sequence_length) or un-batched and of shape (sequence_length,).

  • generator (torch.Generator, optional) — A torch generator to make generation deterministic.
  • return_dict — Whether to return a ModelOutput subclass instead of a plain tuple.

Returns

transformers.models.univnet.modeling_univnet.UnivNetModelOutput or tuple(torch.FloatTensor)

A transformers.models.univnet.modeling_univnet.UnivNetModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (UnivNetConfig) and inputs.

  • waveforms (torch.FloatTensor of shape (batch_size, sequence_length)) — Batched 1D (mono-channel) output audio waveforms.
  • waveform_lengths (torch.FloatTensor of shape (batch_size,)) — The batched length in samples of each unpadded waveform in waveforms.

The UnivNetModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Converts a noise waveform and a conditioning spectrogram to a speech waveform. Passing a batch of log-mel spectrograms returns a batch of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech waveform.

Example:

>>> from transformers import UnivNetFeatureExtractor, UnivNetModel
>>> from datasets import load_dataset, Audio

>>> model = UnivNetModel.from_pretrained("dg845/univnet-dev")
>>> feature_extractor = UnivNetFeatureExtractor.from_pretrained("dg845/univnet-dev")

>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> # Resample the audio to the feature extractor's sampling rate.
>>> ds = ds.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
>>> inputs = feature_extractor(
...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
... )
>>> audio = model(**inputs).waveforms
>>> list(audio.shape)
[1, 140288]
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