:jddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZmZmZddlmZmZeeegsdd gZnesd gZGd de Zy ) )Sequence)AnyOptionalUnion)Tensortensor)_srmr_arg_validate,speech_reverberation_modulation_energy_ratio)Metric)_GAMMATONE_AVAILABLE_MATPLOTLIB_AVAILABLE_TORCHAUDIO_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE(SpeechReverberationModulationEnergyRatioz-SpeechReverberationModulationEnergyRatio.plotceZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e e ed <d Z e e ed < dd ed ede de de e dedededd ffd Zdedd fdZdefdZddeeeed fde edefdZxZS)ra; 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 .. hint:: 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``. .. attention:: 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 inconsistency. 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: >>> from torch import randn >>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio >>> preds = randn(8000) >>> srmr = SpeechReverberationModulationEnergyRatio(8000) >>> srmr(preds) tensor(0.3191) msumtotalFfull_state_updateTis_differentiablehigher_is_betterNplot_lower_boundplot_upper_boundfsn_cochlear_filterslow_freqmin_cfmax_cfnormfastkwargsreturnc Lt |d i|trts t dt |||||||||_||_||_||_ ||_ ||_ ||_ |jdtdd|jdtddy) 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``)rrrrrrr rgsum)defaultdist_reduce_fxrr)super__init__rr ModuleNotFoundErrorr rrrrrrr add_stater) selfrrrrrrr r! __class__s l/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/audio/srmr.pyr)z1SpeechReverberationModulationEnergyRatio.__init__as "6"$,@%n  1 "4       vvc{5I wq %Hpredsc t||j|j|j|j|j |j |jj|jj}|xj|jz c_ |xj|jz c_ y)zUpdate state with predictions.N)r rrrrrrr tordevicer$rnumel)r,r0metric_val_batchs r.updatez/SpeechReverberationModulationEnergyRatio.updatesG 477D33T]]DKKQUQ\Q\^b^g^gimirir "TYY     %))++  &,,.. r/c4|j|jz S)zCompute metric.)rr)r,s r.computez0SpeechReverberationModulationEnergyRatio.computesyy4::%%r/valaxc&|j||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) )_plot)r,r9r:s r.plotz-SpeechReverberationModulationEnergyRatio.plotsLzz#r""r/)}NFF)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrrfloatrintrr)r6r8rrrrr= __classcell__)r-s@r.rr%s%1f L M#t#"t"!d!(,huo,(,huo, #%"&%I %I %I %I  %I  %I%I%I%I %IN/F/t/&&&#fhv&6<=&#(S[J\&#hv&#r/N)collections.abcrtypingrrrtorchrr"torchmetrics.functional.audio.srmrr r torchmetrics.metricr torchmetrics.utilities.importsr r rtorchmetrics.utilities.plotrrall__doctest_skip__rr'r/r.rSsb%'' ' A "7 89BDst GHV#vV#r/