o }oiM! @sddlmZmZmZmZmZddlZddlmZddl m Z ddl m Z ddl mZmZddlmZmZddlmZdd lmZmZdd lmZd gZe eeeee eeeg ZGd d d eZdS) )CallableIterableListOptionalTupleN)Metric)!PerceptualEvaluationSpeechQuality)PermutationInvariantTraining)#ScaleInvariantSignalDistortionRatioSignalDistortionRatio)ScaleInvariantSignalNoiseRatioSignalNoiseRatio)!ShortTimeObjectiveIntelligibility)SquimMOSMetricSquimObjectiveMetric)loggingAudioMetricWrapperc sXeZdZUdZdZeed<ejed< d#de de e de effd d Z d ejd ejd e ejejffddZed ejd ejdejd ee ejejffddZedeejd ejfddZd$d ejd ejde ejd dfddZd ejfddZ d$d ejd ejde ejd ejfddZd%fdd Zd efddZd ed efd!d"ZZS)&raA wrapper around an audio metric enabling selection of a specific channel and handling of examples in a batch with varying valid input length. Note: This class assumes that the underlying metric uses averaging to calculate the value over a batch. This assumption is only used by `forward` and does not impact other methods, such as `update` and `compute`. Args: metric: base metric that should be wrapped. It is assumed that calculation of the metric over a batch is done by averaging. channel: Optional, for selecting a channel from `preds` and `target` signals. If None, all channels are used. metric_using_batch_averaging: Optional, used to denote that the base metric is using averaging to calculate the metric value for a batch. Ffull_state_update num_examplesNmetricchannelmetric_using_batch_averagingcstt|tstd||s&t|tvr&td|d|jjd||_ ||_ |j dt dddtd |t|dS) NzNExpected argument `metric` to be an instance of `torchmetrics.Metric` but got zMetric z is not in verified metrics. aJ assumes reduction over batch is calculated using averaging. This should not affect the final results, but values for a single batch obtained using `forward` may be inaccurate if using `input_length`. To suppress this message, please confirm the used metric is using batch averaging and set "metric_using_batch_averaging = True"rrsum)defaultdist_reduce_fxzSetup metric %s, channel %s)super__init__ isinstancer ValueErrortype__VERIFIED_METRICS__ __class____name___metric_channel add_statetorchtensorrdebugstr)selfrrrr!X/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/audio/metrics/audio.pyrAs  zAudioMetricWrapper.__init__predstargetreturncCs:|jdur ||fS|dd|jdf|dd|jdffS)aPSelect a single channel from input signals. Args: preds: tensor with shape (B, C, T) target: tensor with shape (B, C, T) Returns: Original tensors if self.channel is None, shape (B, C, T). A single channel from input tensors if self.channel is set, shape (B, T) N.)r$)r*r.r/r,r,r-_select_channelTs (z"AudioMetricWrapper._select_channel input_lengthccsFt|D]\}}||dd|f}||dd|f}||fVqdS)aTrim input tensors to input_length samples. Args: preds: tensor with shape (B, C, T) target: tensor with shape (B, C, T) Returns: An iterable with tuples of (preds, target) with the correct length. .N) enumerate)r.r/r2b_idxb_lenb_predsb_targetr,r,r- _trim_inputsds  zAudioMetricWrapper._trim_inputs batch_valuescCst|t|S)aReduce metric values for each example in a batch to a single value for the whole batch. Args: batch_values: list of metric values for each example in a batch Returns: Average metric value over the batch. )rlen)r9r,r,r-_batch_reductionys z#AudioMetricWrapper._batch_reductioncCsn|j||d\}}|dur|jj||dn|j|||dD] \}}|jj||dq|j|d7_dS)aUpdate the underlying metric by taking into account channel selector and input length. Args: preds: tensor with predictions, shape (B, C, T) target: tensor with target signals, shape (B, C, T) input_length: Optional, input tensor with length (in samples) of each signal in the batch, shape (B,). If not provided, it is assumed that all samples are valid. r.r/Nr.r/r2r)r1r#updater8rsize)r*r.r/r2r6r7r,r,r-r>s  zAudioMetricWrapper.updatecCs |jS)zCompute the underlying metric.)r#computer*r,r,r-r@s zAudioMetricWrapper.computecCsd|j||d\}}|dur|j||dSg}|j|||dD]\}}||j||dq||S)a:Call underlying forward method to add the batch statistics to the accumulated metric state and return the result for the current batch. Args: preds: tensor with predictions, shape (B, C, T) target: tensor with target signals, shape (B, C, T) input_length: Optional, input tensor with length (in samples) of each signal in the batch, shape (B,). If not provided, it is assumed that all samples are valid. Returns: Underlying metric averaged on the current batch. r<Nr=)r1r#r8appendr;)r*r.r/r2r9r6r7r,r,r-forwards zAudioMetricWrapper.forwardcst|jdS)zReset the underlying metric.N)rresetr#rAr+r,r-rDs zAudioMetricWrapper.resetcCs*dt|jd|jd}|jj|}|S)z+Return string representation of the object.z (metric: z , channel: ))reprr#r$r!r")r* _op_metricrepr_strr,r,r-__repr__s zAudioMetricWrapper.__repr__r@cCs|S)z3Overwrite to do nothing, as in CompositionalMetric.r,)r*r@r,r,r- _wrap_computesz AudioMetricWrapper._wrap_compute)NN)N)r0N)r" __module__ __qualname____doc__rbool__annotations__r&Tensorrrintrrr1 staticmethodrr8rr;r>r@rCrDr)rIrrJ __classcell__r,r,r+r-r+sP   & &  )typingrrrrrr& torchmetricsrtorchmetrics.audio.pesqrtorchmetrics.audio.pitr torchmetrics.audio.sdrr r torchmetrics.audio.snrr r torchmetrics.audio.stoir$nemo.collections.audio.metrics.squimrr nemo.utilsr__all__r rr,r,r,r-s,