a  d2@sddlmZmZmZmZddlmZmZddlm Z m Z ddl m Z ddl mZmZmZmZddlmZmZeeeegsddgZn esdgZGd dde Zd S) )AnyOptionalSequenceUnion)Tensortensor)_srmr_arg_validate,speech_reverberation_modulation_energy_ratio)Metric)_GAMMATONE_AVAILABEL_MATPLOTLIB_AVAILABLE_TORCHAUDIO_AVAILABEL_TORCHAUDIO_GREATER_EQUAL_0_10)_AX_TYPE_PLOT_OUT_TYPE(SpeechReverberationModulationEnergyRatio-SpeechReverberationModulationEnergyRatio.plotc seZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e e ed <d Z e e ed <deee e e e eeed d fdd Zed dddZedddZdeeeed fe eedddZZS)raL Calculate `Speech-to-Reverberation Modulation Energy Ratio`_ (SRMR). SRMR is a non-intrusive metric for speech quality and intelligibility based on a modulation spectral representation of the speech signal. This code is translated from `SRMRToolbox`_ and `SRMRpy`_. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)`` As output of `forward` and `compute` the metric returns the following output - ``srmr`` (:class:`~torch.Tensor`): float scaler tensor .. note:: using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed. Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio`` and ``pip install git+https://github.com/detly/gammatone``. .. note:: This implementation is experimental, and might not be consistent with the matlab implementation `SRMRToolbox`_, especially the fast implementation. The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have a relatively small inconsistence. Args: fs: the sampling rate n_cochlear_filters: Number of filters in the acoustic filterbank low_freq: determines the frequency cutoff for the corresponding gammatone filterbank. min_cf: Center frequency in Hz of the first modulation filter. max_cf: Center frequency in Hz of the last modulation filter. If None is given, then 30 Hz will be used for `norm==False`, otherwise 128 Hz will be used. norm: Use modulation spectrum energy normalization fast: Use the faster version based on the gammatonegram. Note: this argument is inherited from `SRMRpy`_. As the translated code is based to pytorch, setting `fast=True` may slow down the speed for calculating this metric on GPU. Raises: ModuleNotFoundError: If ``gammatone`` or ``torchaudio`` package is not installed Example: >>> import torch >>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio >>> g = torch.manual_seed(1) >>> preds = torch.randn(8000) >>> srmr = SpeechReverberationModulationEnergyRatio(8000) >>> srmr(preds) tensor(0.3354) msumtotalFfull_state_updateTis_differentiablehigher_is_betterNplot_lower_boundplot_upper_bound}) fsn_cochlear_filterslow_freqmin_cfmax_cfnormfastkwargsreturnc stjfi|trtrts&tdt|||||||d||_||_||_ ||_ ||_ ||_ ||_ |jdtddd|jdtddddS) Naspeech_reverberation_modulation_energy_ratio requires you to have `gammatone` and `torchaudio>=0.10` installed. Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio>=0.10`` and ``pip install git+https://github.com/detly/gammatone``)rrrr r!r"r#rgsum)defaultZdist_reduce_fxrr)super__init__r rr ModuleNotFoundErrorrrrrr r!r"r#Z add_stater) selfrrrr r!r"r#r$ __class__`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/audio/srmr.pyr)as.   z1SpeechReverberationModulationEnergyRatio.__init__)predsr%c CsVt||j|j|j|j|j|j|j|j j }|j | 7_ |j | 7_ dS)zUpdate state with predictions.N)r rrrr r!r"r#torZdevicer&rZnumel)r+r0Zmetric_val_batchr.r.r/updatesz/SpeechReverberationModulationEnergyRatio.update)r%cCs |j|jS)zCompute metric.)rr)r+r.r.r/computesz0SpeechReverberationModulationEnergyRatio.compute)valaxr%cCs |||S)aNPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio >>> metric = SpeechReverberationModulationEnergyRatio(8000) >>> metric.update(torch.rand(8000)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio >>> metric = SpeechReverberationModulationEnergyRatio(8000) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(8000))) >>> fig_, ax_ = metric.plot(values) )Z_plot)r+r4r5r.r.r/plots&r)rrrNFF)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrrfloatrintrr)r2r3rrrrr6 __classcell__r.r.r,r/r%s8 3   ' N)typingrrrrZtorchrrZ"torchmetrics.functional.audio.srmrrr Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr r r rZtorchmetrics.utilities.plotrrallZ__doctest_skip__rr.r.r.r/s