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Dec. 15, 2018
Oct. 23, 2018



This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm. UIS-RNN solves the problem of segmenting and clustering sequential data by learning from examples.

This algorithm was originally proposed in the paper Fully Supervised Speaker Diarization.

The work has been introduced by Google AI Blog.



This open source implementation is slightly different than the internal one which we used to produce the results in the paper, due to dependencies on some internal libraries.

We CANNOT share the data, code, or model for the speaker recognition system (d-vector embeddings) used in the paper, since the speaker recognition system heavily depends on Google's internal infrastructure and proprietary data.

This library is NOT an official Google product.


This library depends on:

  • python 3.5+
  • numpy 1.15.1
  • pytorch 0.4.0
  • scipy 1.1.0 (for evaluation only)

Getting Started

Run the demo

To get started, simply run this command:

python3 --train_iteration=1000 -l=0.001 -hl=100

This will train a UIS-RNN model using data/training_data.npz, then store the model on disk, perform inference on data/testing_data.npz, print the inference results, and save the averaged accuracy in a text file.

PS. The files under data/ are manually generated toy data, for demonstration purpose only. These data are very simple, so we are supposed to get 100% accuracy on the testing data.

Run the tests

You can also verify the correctness of this library by running:


If you fork this library and make local changes, be sure to use these tests as a sanity check.

Besides, these tests are also great examples for learning the APIs, especially tests/

Core APIs


General Machine Learning Speaker Diarization
Sequence Utterance
Observation Embedding / d-vector
Label / Cluster ID Speaker

Model construction

All algorithms are implemented as the UISRNN class. First, construct a UISRNN object by:

model = UISRNN(args)

The definitions of the args are described in model/ See model_parser.


Next, train the model by calling the fit() function:, train_cluster_id, args)

Here train_sequence should be a 2-dim numpy array of type float, for the concatenated observation sequences. For speaker diarization, this could be the d-vector embeddings.

For example, if you have M training utterances, and each utterance is a sequence of L embeddings. Each embedding is a vector of D numbers. Then the shape of train_sequence is N * D, where N = M * L.

train_cluster_id is a 1-dim list or numpy array of strings, of length N. It is the concatenated ground truth labels of all training data. For speaker diarization, these labels are the speaker identifiers for each observation (e.g. d-vector).

Since we are concatenating observation sequences, it is important to note that, ground truth labels in train_cluster_id across different sequences are supposed to be globally unique.

For example, if the set of labels in the first sequence is {'A', 'B', 'C'}, and the set of labels in the second sequence is {'B', 'C', 'D'}. Then before concatenation, we should rename them to something like {'1_A', '1_B', '1_C'} and {'2_B', '2_C', '2_D'}, unless 'B' and 'C' in the two sequences are meaningfully identical (in speaker diarization, this means they are the same speakers across utterances).

The reason we concatenate all training sequences is that, we will be resampling and block-wise shuffling the training data as a data augmentation process, such that we result in a robust model even when there is insufficient number of training sequences.

The definitions of the args are described in model/ See training_parser.


Once we are done with the training, we can run the trained model to perform inference on new sequences by calling the predict() function:

predicted_label = model.predict(test_sequence, args)

Here test_sequence should be a 2-dim numpy array of type float, corresponding to a single observation sequence.

The returned predicted_label is a list of integers, with the same length as test_sequence.

The definitions of the args are described in model/ See inference_parser.


Our paper is cited as:

  title={Fully Supervised Speaker Diarization},
  author={Zhang, Aonan and Wang, Quan and Zhu, Zhenyao and Paisley, John and Wang, Chong},
  journal={arXiv preprint arXiv:1810.04719},


Baseline diarization system

To learn more about our baseline diarization system based on unsupervised clustering algorithms, check out this site.

Specifically, the ground truth labels for the NIST SRE 2000 dataset (Disk6 and Disk8) can be found here.

Speaker recognizer/encoder

To learn more about our speaker embedding system, check out this site.

We are aware of several third-party implementations of this work:

Please use your own judgement to decide whether you want to use these implementations.

We are NOT responsible for the correctness of any third-party implementations.