o .wi@sddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZmZmZddlmZmZeeegsCdd gZnesHd gZGd dde Zd S) )Sequence)AnyOptionalUnion)Tensortensor)_srmr_arg_validate,speech_reverberation_modulation_energy_ratio)Metric)_GAMMATONE_AVAILABLE_MATPLOTLIB_AVAILABLE_TORCHAUDIO_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE(SpeechReverberationModulationEnergyRatio-SpeechReverberationModulationEnergyRatio.plotcseZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e e ed <d Z e e ed <   d#dedede de de e dedededd ffdd Zdedd fddZdefddZd$deeeed fd e edefd!d"ZZS)%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_bound}fsn_cochlear_filterslow_freqmin_cfmax_cfnormfastkwargsreturnc stjd i|tr tstdt|||||||d||_||_||_||_ ||_ ||_ ||_ |j dtddd|j dtddddS) 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``)rrrrr r!r"rgsum)defaultdist_reduce_fxrr)super__init__r r ModuleNotFoundErrorrrrrrr r!r" add_stater) selfrrrrr r!r"r# __class__r(T/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/audio/srmr.pyr*as.  z1SpeechReverberationModulationEnergyRatio.__init__predsc CsVt||j|j|j|j|j|j|j|j j }|j | 7_ |j | 7_ dS)zUpdate state with predictions.N)r rrrrr r!r"tordevicer%rnumel)r-r1metric_val_batchr(r(r0updates z/SpeechReverberationModulationEnergyRatio.updatecCs |j|jS)zCompute metric.)rr)r-r(r(r0computes z0SpeechReverberationModulationEnergyRatio.computevalaxcCs |||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-r8r9r(r(r0plots &r)rrrNFF)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrrfloatrintrr*r6r7rrrrr; __classcell__r(r(r.r0r%sJ 3     ' 2N)collections.abcrtypingrrrtorchrr"torchmetrics.functional.audio.srmrrr torchmetrics.metricr torchmetrics.utilities.importsr r r torchmetrics.utilities.plotrrall__doctest_skip__rr(r(r(r0s