Train End-to-End Speaker Separation Model

This example uses:

Speaker separation is a challenging and critical speech processing task. Downstream applications include speech recognition, speaker diarization, speaker identification, and listening enhancement. In this example, you train an end-to-end deep learning network for speaker-independent speech separation.

To use a pretrained speaker separation model, see separateSpeakers.

For a comparison of the performance of different models, see Compare Speaker Separation Models.

Perform Speaker Separation Using Pretrained Model

The Conv-TasNet architecture [1] was an early and important development in end-to-end speech separation. Although it has been surpassed by transformer-based models in speaker separation metrics, it remains relevant due to its speed, small size, and ease of training. These qualities also make Conv-TasNet a good candidate for speaker separation solutions intended to deploy for specific noise conditions.

To access a pretrained Conv-TasNet model, as well as transformer-based models, use the separateSpeakers function. Speaker separation is usually tasked with separating speech signals into a fixed number of speakers. However, in real-world scenarios, the number of speakers is often not known or changing. The Conv-TasNet model used by separateSpeakers was trained with a one-and-rest objective, meaning it will always try to isolate the most dominant speaker and then output the single speaker signal and the "rest" (also known as the residual) [2]. To separate multiple speakers, the residual can be recursively fed back into the model until no more speakers are detected in the residual.

Read in a sample audio file, mix the channels and listen to the results.

[targets,fs] = audioread('MultipleSpeakers-16-8-4channel-5secs.flac');
x = mean(targets,2);
sound(x,fs),pause(size(x,1)/fs + 1)

Separate the speakers using the default one-and-rest Conv-TasNet model. Call the function a second time without outputs to visualize the results. Listen to the results as well.

[y,r] = separateSpeakers(x,fs);
separateSpeakers(x,fs)

for ii = 1:size(y,2)
    sound(y(:,ii),fs),pause(size(x,1)/fs + 1)
end
sound(r,fs)

To evaluate the permutation-invariant scale-invariant signal-to-noise ratio (SI-SNR), use the permutationInvariantSISNR function. The permutation invariant SI-SNR metric is the most common evaluation and training metric for speaker separation.

permutationInvariantSISNR(y,targets)

ans = single

6.1334

The default parameters of this example define an utterance-level permutation invariant training (uPIT) pipeline to train a two-speaker speech separation model similar to the pretrained one you can access through separateSpeakers. There are parameters throughout the example that you can modify to instead specify one-and-rest permutation invariant training (OR-PIT) to separate speech into one-and-rest, or to include or exclude noise in the system.

To speed up the example by running on a reduced data set, set speedupExample to true. To run the full example as published, set speedupExample to false.

speedupExample = false;

Download Data

Train and Validation Data

If you speed up the example, the example downloads and unzips LibriSpeech's dev-clean set for both training and development. If you do not speed up the example, the example uses the train-clean-100 subset of LibriSpeech and uses the dev-clean set exclusively for validation.

downloadDatasetFolder = tempdir;

trainDatasetFolder = {};
devDatasetFolder = {};

if speedupExample
    trainDatasetFolder{end+1} = char(downloadAndUnzip("dev-clean",downloadDatasetFolder));
else
    devDatasetFolder{end+1} = char(downloadAndUnzip("dev-clean",downloadDatasetFolder));
    trainDatasetFolder{end+1} = char(downloadAndUnzip("train-clean-100",downloadDatasetFolder));

    % Uncomment following lines to train on entire LibriSpeech dataset
    %trainDatasetFolder{end+1} = char(downloadAndUnzip("train-clean-360",downloadDatasetFolder));
    %trainDatasetFolder{end+1} = char(downloadAndUnzip("train-other-500",downloadDatasetFolder));
end

adsTrain = audioDatastore(trainDatasetFolder,IncludeSubfolders=true,OutputDataType="single");

if speedupExample
    adsTrain = shuffle(adsTrain);
    adsValidation = subset(adsTrain,1:20);
    adsTrain = subset(adsTrain,21:200);
else
    adsValidation = audioDatastore(devDatasetFolder,IncludeSubfolders=true,OutputDataType="single");
end

Noise Data

Create a datastore that contains background noise files.

noiseDatasetFolder = "WashingMachine-16-8-mono-1000secs.mp3";
adsNoise = audioDatastore(noiseDatasetFolder,IncludeSubfolders=true,OutputDataType="single");

Define Preprocessing Pipeline

Resample Audio

Define transforms to resample the train and validation data to 8 kHz. Because Conv-TasNet accepts audio directly, it is common practice to use a low sample rate to avoid out-of-memory issues.

fs = 8e3;
adsTrain = transform(adsTrain,@(x,info)resampleTransform(x,info,fs),IncludeInfo=true);
adsValidation = transform(adsValidation,@(x,info)resampleTransform(x,info,fs),IncludeInfo=true);

Augment Data Using Dynamic Mixing

Create a DynamicMixer object. The DynamicMixer class definition is placed in your current folder when you open the example. To specify the SNR for noise addition, specify a range in dB. The object chooses from a uniform distribution within that range when mixing. To include noise in the target signals, specify NoiseInTargets as 'mix' or 'append'. When NoiseInTargets is set to 'exclude', the model simultaneously attempts to denoise the input signal while performing speaker separation. Set the MaxLength of the audio signals output. Set the NumSignals property to a range of how many signals can be included in each training example. For uPIT training, NumSignals must be a scalar.

mixer = DynamicMixer( ...
    AugFiles=adsTrain.UnderlyingDatastores{1}.Files, ...
    SignalWeightRange=[0.9 1], ...
    NoiseFiles=adsNoise.Files, ...
    NoiseWeightRange=[20 60], ... % SNR dB
    NoiseInTargets='exclude', ...
    MaxLength=fs *5, ... % move the slider to specify max length in seconds
    NumSignals=[2 2]);

Read a sample from the validation set and apply the mixing.

[x,xinfo] = read(adsValidation);
xt = mix(mixer,x,xinfo);
x1 = xt{1};
t1 = xt{2};

Inspect the predictor and the target signals.

tiledlayout(2,1)

timevec = (0:size(x1,1)-1)/fs;

nexttile()
plot(timevec,x1)
ylabel("Mixed Signal")

nexttile()
plot(timevec,t1)
ylabel("Target Signals")

sound(x1,fs),pause(size(x1,1)/fs + 1)
for ii = 1:size(t1,2)
    sound(t1(:,ii),fs),pause(size(t1,1)/fs + 1)
end

Create Mini-Batch Queue

To ensure all audio files in a mini batch have the same length and the targets are equal size, use minibatchqueue. The preprocessMiniBatch function pads or truncates the signals to the median length of the batch and then passes the batch as a cell array to the DynamicMixer. The DynamicMixer randomly selects one value in the NumSignals range per batch to augment the mini-batch with. This is necessary so that the targets are a consistent size for batch processing.

mbqTrain = minibatchqueue(adsTrain,2, ...
    OutputAsDlarray=true, ...
    MiniBatchFormat="TCB", ...
    MiniBatchFcn=@(m)preprocessMiniBatch(m,mixer,fs));

mbqValidation = minibatchqueue(adsValidation,2, ...
    OutputAsDlarray=true, ...
    MiniBatchFormat="TCB", ...
    MiniBatchFcn=@(m)preprocessMiniBatch(m,mixer,fs));

Define Model

Create Conv-TasNet Model

Use the supporting function, convtasnet, to construct a randomly initialized Conv-TasNet model based on [1]. You can find details and analysis of the hyperparameters exposed here in [1]. Constructing the model requires several custom layer class definitions which are placed in your current folder when you open this example.

net = convtasnet( ...
    NumFiltersAutoEncoder=512, ... N  - Number of filters in autoencoder
    FilterLength=16, ...            L  - Length of filters (in samples)
    NumChannelsBottleneck=128, ... B  - Number of channels in bottleneck
    NumChannelsConvBlocks=512, ... H  - Number of channels in convolutional blocks
    NumChannelsSkipPaths= 128, ... Sc - Number of channels in skip connection paths' 1x1-conv blocks
    KernelSizeConvBlocks=3, ...     P  - Kernel size in convolution blocks
    NumConvBlocksInRepeat=8, ...   X  - Number of convolutional blocks in each repeat
    NumRepeats=3, ...              R  - Number of repeats
    NumResponses=2);

To analyze the model, use analyzeNetwork.

analyzeNetwork(net)

Define Training Options

To define training options, use trainingOptions.

mbs = 6;
options = trainingOptions("adam", ...
    Shuffle="every-epoch", ...
    Plots="training-progress", ...
    MaxEpochs=10, ...
    MiniBatchSize=mbs, ...
    TargetDataFormats="TCB", ...
    Verbose=false, ...
    GradientThreshold=5, ...
    InitialLearnRate=0.00015, ...
    OutputNetwork="best-validation", ...
    ExecutionEnvironment="gpu", ...
    ValidationData=mbqValidation, ...
    ValidationFrequency=2*round(numel(adsTrain.UnderlyingDatastores{1}.Files)/mbs), ...
    ValidationPatience=3);

Train Model

To train the model, use trainnet. Use the negative of permutationInvariantSISNR as the loss function. The PermutationType can be set as utterance-level permutation invariant training (uPIT) or one-and-rest permutation invariant training (OR-PIT). If the NumSignals parameter of the dynamic mixer object is a range, then PermutationType must be set to "OR-PIT".

[net,traininfo] = trainnet(mbqTrain,net, ...
    @(Y,T)-permutationInvariantSISNR(Y,T,PermutationType="uPIT"), ...
    options);

Evaluate Model

Read a sample from the validation set and apply mixing.

[x,xinfo] = read(adsValidation);
xt = mix(mixer,x,xinfo);
predictor = xt{1};
targets = xt{2};

Run the mixed signal through the model.

y = (predict(net,predictor))';

Evaluate the permutation invariant SI-SNR and get the indices of the best permutation of speaker signals.

[metric,idx] = permutationInvariantSISNR(y,targets);
display("Permutation Invariant SI-SINR: " + metric + " dB")

    "Permutation Invariant SI-SINR: 15.1941 dB"

Sort the predictions into the best permutation.

y = y(:,idx);

To calculate the permutation invariant SI-SNR improvement (SI-SNRi), use sisnr.

ametric = sisnr(predictor,targets);
bmetric = sisnr(y,targets);
metricImprovement = mean(bmetric - ametric);
display("Permutation Invariant SI-SNRi: " + metricImprovement + " dB")

    "Permutation Invariant SI-SNRi: 15.21 dB"

Listen to and plot the results of the separation.

sound(predictor,fs),pause(size(predictor,1)/fs+1)
y = y./max(abs(y));
sound(y(:,1),fs),pause(size(y,1)/fs+1)
sound(y(:,2),fs)

t = (0:size(predictor,1)-1)/fs;

tiledlayout(3,1)

nexttile()
plot(t,predictor)
ylabel('Input Signal')

nexttile()
plot(t,targets(:,1),'b',t,y(:,1),'r:')
ylabel('Signal 1')
legend('target','prediction')

nexttile()
plot(t,targets(:,2),'b',t,y(:,2),'r:')
ylabel('Signal 2')
legend('target','prediction')

Supporting Functions

Resample Transform

function [audioOut,adsInfo] = resampleTransform(audioIn,adsInfo,desiredFs)
    fs = adsInfo.SampleRate;

    % Convert to mono
    x = mean(audioIn,2);
    
    % Resample to 16 kHz
    if desiredFs~=fs
        x = resample(x,desiredFs,fs);
    end
    adsInfo.SampleRate = desiredFs;
    
    % Normalize so that the max absolute value of a signal is 1 
    audioOut = x/max(abs(x));
end

Create Conv-TasNet Model

function net = convtasnet(options)
% This model and its parameters are described in: 
% [1] Luo, Yi, and Nima Mesgarani. "Conv-TasNet: Surpassing Ideal
% Time–Frequency Magnitude Masking for Speech Separation." IEEE/ACM
% Transactions on Audio, Speech, and Language Processing, vol. 27, no. 8,
% Aug. 2019, pp. 1256–66. DOI.org (Crossref),
% https://doi.org/10.1109/TASLP.2019.2915167.
arguments
    options.NumFiltersAutoEncoder = 512   % N  - Number of filters in autoencoder
    options.FilterLength = 16             % L  - Length of filters (in samples)
    options.NumChannelsBottleneck = 128   % B  - Number of channels in bottleneck
    options.NumChannelsConvBlocks = 512   % H  - Number of channels in convolutional blocks
    options.NumChannelsSkipPaths = 128    % Sc - Number of channels in skip connection paths' 1x1-conv blocks
    options.KernelSizeConvBlocks = 3      % P  - Kernel size in convolution blocks
    options.NumConvBlocksInRepeat = 8     % X  - Number of convolutional blocks in each repeat
    options.NumRepeats = 3                % R  - Number of repeats
    options.NumResponses = 2              % Number of responses
end

N = options.NumFiltersAutoEncoder;
L = options.FilterLength;
B = options.NumChannelsBottleneck;
Sc = options.NumChannelsSkipPaths;
H = options.NumChannelsConvBlocks;
P = options.KernelSizeConvBlocks;
X = options.NumConvBlocksInRepeat; % Also referred to as "M" in [1].
R = options.NumRepeats;
numResponses = options.NumResponses;


net = dlnetwork();

% Encoder + Bottleneck -----------------------------------------------------
net = addLayers(net,[ ...
    sequenceInputLayer(1,MinLength=L,Name="input")
    convolution1dLayer(L,N,Stride=L/2,Name="encoder.conv1d")
    layerNormalizationLayer(OperationDimension="batch-excluded",Name="bottleneck.layernorm")
    convolution1dLayer(1,B,Name="bottleneck.conv1d")]);


% TCN ----------------------------------------------------------------------
previousLayer = "bottleneck.conv1d";
dilationFactor = repmat((2.^(0:X-1)),1,R);
net = addLayers(net,additionLayer(X*R,Name="skip_connection"));

for ii = 1:(X*R)

    net = addLayers(net,[ ...
        convolution1dLayer(1,H,Name="TCN."+ii+".conv1d")
        preluLayer(Name="TCN."+ii+"prelu_1")
        layerNormalizationLayer(OperationDimension="batch-excluded",Name="TCN."+ii+".layernorm_1")

        groupedConvolution1dLayer(FilterSize=P,DilationFactor=dilationFactor(ii),Name="TCN."+ii+".groupconv1d")
        preluLayer(Name="TCN."+ii+".prelu_2")
        layerNormalizationLayer(OperationDimension="batch-excluded",Name="TCN."+ii+".layernorm_2")

        convolution1dLayer(1,Sc,Name="TCN."+ii+".skip.conv1d")
        ]);

    if ii~=(X*R)
        net = addLayers(net,[ ...
            convolution1dLayer(1,B,Name="TCN."+ii+".residual.conv1d") ...
            additionLayer(2,Name="TCN."+ii+".output+residual")]);

        net = connectLayers(net,"TCN."+ii+".layernorm_2","TCN."+ii+".residual.conv1d");
        net = connectLayers(net,previousLayer,"TCN."+ii+".output+residual"+"/in2");
    end

    net = connectLayers(net,previousLayer,"TCN."+ii+".conv1d");

    previousLayer = "TCN."+ii+".output+residual";

    net = connectLayers(net,"TCN."+(ii)+".skip.conv1d","skip_connection/in"+ii);
end


% Output -------------------------------------------------------------------
net = addLayers(net,[ ...
    preluLayer(Name="mask.prelu")
    convolution1dLayer(1,numResponses*N,Name="mask.conv1d")
    splitMaskLayer(NumSpeakers=numResponses,Name="splitMask")
    reluLayer(Name='relu') ... % The original paper uses sigmoid. Common practice now is ReLU, but results have not been compared.
    ]);
net = connectLayers(net,"skip_connection","mask.prelu");

% Apply Mask
net = addLayers(net,[ ...
    addDimLayer(Name="viewLayer_2",DimToAdd="S")
    multiplicationLayer(2,Name="apply_mask")
    reformatLayer(Name="viewLayer_3",Format="TCBS")
    transposedConv1dLayer(L,1,Stride=L/2,Name='return')
    reformatLayer(Name="viewLayer_4",Format="TCBS")
    removeDimLayer(Name="formatOutput",DimToRemove='C')]);

net = connectLayers(net,"encoder.conv1d","viewLayer_2");
net = connectLayers(net,"relu","apply_mask/in2");

net = initialize(net,dlarray(zeros(L,1,'single'),'TC'));

end

Preprocess Mini-Batch

function [X,T] = preprocessMiniBatch(Xin,mixer,fs)
% preprocessMiniBatch - Preprocess mini-batch
info.SampleRate = fs;

% Get the median signal length
signalLengths = cellfun(@(x)size(x,1),Xin,UniformOutput=false);
medianSignalLength = min(round(median(cat(1,signalLengths{:}))),mixer.MaxLength);

% Trim or pad signals to same length
Xin = cellfun(@(x)trimOrPad(x,medianSignalLength),Xin,UniformOutput=false);

% Apply mixing
dataOut = mixer.mix(Xin,info);

% Output the predictors and targets separately, with batches along the
% third dimension.
X = cat(3,dataOut{:,1});
T = cat(3,dataOut{:,2});

end

Download LibriSpeech

function datasetFolder = downloadAndUnzip(dataset,downloadDatasetFolder)
filename = dataset + ".tar.gz";
datasetFolder = fullfile(downloadDatasetFolder,"LibriSpeech",dataset);
url = "http://us.openslr.org/resources/12/" + filename;
if ~datasetExists(datasetFolder)
    gunzip(url,downloadDatasetFolder);
    unzippedFile = fullfile(downloadDatasetFolder,filename);
    untar(unzippedFile{1}(1:end-3),downloadDatasetFolder);
end
end

References

[1] Luo, Yi, and Nima Mesgarani. "Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation." IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 8, Aug. 2019, pp. 1256–66. DOI.org (Crossref), https://doi.org/10.1109/TASLP.2019.2915167.

[2] Takahashi, Naoya, et al. "Recursive Speech Separation for Unknown Number of Speakers." Interspeech 2019, ISCA, 2019, pp. 1348–52. DOI.org (Crossref), https://doi.org/10.21437/Interspeech.2019-1550.

Train End-to-End Speaker Separation Model

Perform Speaker Separation Using Pretrained Model

Download Data

Train and Validation Data

Noise Data

Define Preprocessing Pipeline

Resample Audio

Augment Data Using Dynamic Mixing

Create Mini-Batch Queue

Define Model

Create Conv-TasNet Model

Define Training Options

Train Model

Evaluate Model

Supporting Functions

Resample Transform

Create Conv-TasNet Model

Preprocess Mini-Batch

Download LibriSpeech

References

See Also

Related Topics