Callhome Diarization Xvector Model. An xvector DNN trained on augmented Switchboard and NIST SREs. The directory also contains two PLDA backends for scoring.Diarization recipe for CALLHOME, AMI and DIHARD II by Brno University of Technology. The recipe consists of. computing x-vectors. doing agglomerative hierarchical clustering on x-vectors as a first step to produce an initialization. apply variational Bayes HMM over x-vectors to produce the diarization output. score the diarization output.A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech) - NVIDIA/NeMoSpeaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when". In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing.We would like to show you a description here but the site won’t allow us.Technical report This report describes the main principles behind version 2.1 of pyannote.audio speaker diarization pipeline. It also provides recipes explaining how to adapt the pipeline to your own set of annotated data. In particular, those are applied to the above benchmark and consistently leads to significant performance improvement over …Speaker indexing or diarization is an important task in audio processing and retrieval. Speaker diarization is the process of labeling a speech signal with labels corresponding …When you send an audio transcription request to Speech-to-Text, you can include a parameter telling Speech-to-Text to identify the different speakers in the audio sample. This feature, called speaker diarization, detects when speakers change and labels by number the individual voices detected in the audio. When you enable speaker …Dec 18, 2023 · The cost is between $1 to $3 per hour. Besides cost, STT vendors treat Speaker Diarization as a feature that exists or not without communicating its performance. Picovoice’s open-source Speaker Diarization benchmark shows the performance of Speaker Diarization capabilities of Big Tech STT engines varies. Also, there is a flow of SaaS startups ... Diart is a python framework to build AI-powered real-time audio applications. Its key feature is the ability to recognize different speakers in real time with state-of-the-art performance, a task commonly known as "speaker diarization". The pipeline diart.SpeakerDiarization combines a speaker segmentation and a speaker embedding …Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when". In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing. To get the final transcription, we’ll align the timestamps from the diarization model with those from the Whisper model. The diarization model predicted the first speaker to end at 14.5 seconds, and the second speaker to start at 15.4s, whereas Whisper predicted segment boundaries at 13.88, 15.48 and 19.44 seconds respectively. Attributing different sentences to different people is a crucial part of understanding a conversation. Photo by rawpixel on Unsplash History. The first ML-based works of Speaker Diarization began around 2006 but significant improvements started only around 2012 (Xavier, 2012) and at the time it was considered a extremely difficult …Jan 5, 2024 · As the demand for accurate and efficient speaker diarization systems continues to grow, it becomes essential to compare and evaluate the existing models. The main steps involved in the speaker diarization are VAD (Voice Activity Detection), segmentation, feature extraction, clustering, and labeling. Speaker diarization is the task of determining "who spoke when?" in an audio or video recording that contains an unknown amount of speech and an unknown number of speakers. It is a challenging ...Speaker diarization, which is to find the speech seg-ments of specific speakers, has been widely used in human-centered applications such as video conferences or human … AHC is a clustering method that has been constantly em-ployed in many speaker diarization systems with a number of di erent distance metric such as BIC [110, 129], KL [115] and PLDA [84, 90, 130]. AHC is an iterative process of merging the existing clusters until the clustering process meets a crite-rion. This repository has speaker diarization recipes which work by git cloning them into the kaldi egs folder. It is based off of this kaldi commit on Feb 5, 2020 ...Over recent years, however, speaker diarization has become an important key technology f or. many tasks, such as navigation, retrieval, or higher-le vel inference. on audio data. Accordingly, many ...of challenges introduce a new common task for diarization that is intended both to facilitate comparison of current and future systems through standardized data, tasks, and metrics …Recent years have seen various attempts to streamline the diarization process by merging distinct steps in the SD pipeline, aiming toward end-to-end diarization models. While some methods operate independently of transcribed text and rely only on the acoustic features, others feed the ASR output to the SD model to enhance the …pyannote.audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it comes with state-of-the-art pretrained models and pipelines, that can be further finetuned to your own data for even better performance.Callhome Diarization Xvector Model. An xvector DNN trained on augmented Switchboard and NIST SREs. The directory also contains two PLDA backends for scoring.Jul 22, 2023 · Speaker diarization is the process of automatically segmenting and identifying different speakers in an audio recording. The goal of speaker diarization is to partition the audio stream into ... Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual … Transcription of a file in Cloud Storage with diarization; Transcription of a file in Cloud Storage with diarization (beta) Transcription of a local file with diarization; Transcription with diarization; Use a custom endpoint with the Speech-to-Text API; AI solutions, generative AI, and ML Application development Application hosting Compute LIUM has released a free system for speaker diarization and segmentation, which integrates well with Sphinx. This tool is essential if you are trying to do recognition on long audio files such as lectures or radio or TV shows, which may also potentially contain multiple speakers. Segmentation means to split the audio into manageable, distinct ... diarization technologies, both in the space of modularized speaker diarization systems before the deep learning era and those based on neural networks of recent years, a proper group-ing would be helpful.The main categorization we adopt in this paper is based on two criteria, resulting total of four categories, as shown in Table1. In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different …Abstract. Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify “who spoke when”. In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing.Speaker diarization consist of automatically partitioning an input audio stream into homogeneous segments (segmentation) and assigning these segments to the ...detection, and diarization. Index Terms: speaker diarization, speaker recognition, robust ASR, noise, conversational speech, DIHARD challenge 1. Introduction Speaker diarization, often referred to as “who spoke when”, is the task of determining how many speakers are present in a conversation and correctly identifying all segments for each ...Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify “who spoke when”. In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing.Speaker diarization (aka Speaker Diarisation) is the process of splitting audio or video inputs automatically based on the speaker's identity. It helps you answer the question "who spoke when?". With the recent application and advancement in deep learning over the last few years, the ability to verify and identify speakers automatically (with …Jul 18, 2023 · Diarization refers to the ability to tell who spoke and when. It differentiates speakers in mono channel audio input based on their voice characteristics. This allows for the identification of speakers during conversations and can be useful in a variety of scenarios such as doctor-patient conversations, agent-customer interactions, and court ... Diarization is the process of separating an audio stream into segments according to speaker identity, regardless of channel. Your audio may have two speakers on one audio channel, one speaker on one audio channel and one on another, or multiple speakers on one audio channel and one speaker on multiple other channels--diarization will identify …Extract feats feats, feats_lengths = self._extract_feats(speech, speech_lengths) # 2. Data augmentation if self.specaug is not None and self.training: feats, feats_lengths = self.specaug(feats, feats_lengths) # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN if self.normalize is not None: feats, feats_lengths = self.normalize ...Add this topic to your repo. To associate your repository with the speaker-diarization topic, visit your repo's landing page and select "manage topics." Learn more. GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model. Two categories of features are explored: features derived directly from ASR output (phones, position-in-word and word boundaries) and features derived from a …Feb 8, 2024 · Speaker diarization is the process that partitions audio stream into homogenous segments according to the speaker identity. It solves the problem of "Who Speaks When". This API splits audio clip into speech segments and tags them with speakers ids accordingly. This API also supports speaker identification by speaker ID if the speaker was ... Recent years have seen various attempts to streamline the diarization process by merging distinct steps in the SD pipeline, aiming toward end-to-end diarization models. While some methods operate independently of transcribed text and rely only on the acoustic features, others feed the ASR output to the SD model to enhance the …accurate diarization results, the decoding of the diarization sys-tem may generate more precise outcomes. This is the motiva-tion behind our adoption of a multi-stage iterative approach. As shown in Figure2, the entire diarization inference pipeline con-sists of multi-stage NSD-MA-MSE decoding with increasingly accurate initialized diarization ...In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different …Diarization is an important step in the process of speech recognition, as it partitions an input audio recording into several speech recordings, each of which belongs to a single speaker. Traditionally, diarization combines the segmentation of an audio recording into individual utterances and the clustering of the resulting segments.Speaker Diarization with LSTM Paper to arXiv paper Authors Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno Abstract For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications.In this quickstart, you run an application for speech to text transcription with real-time diarization. Diarization distinguishes between the different speakers who …With speaker diarization, you can distinguish between different speakers in your transcription output. Amazon Transcribe can differentiate between a maximum of 10 unique speakers and labels the text from each unique speaker with a unique value (spk_0 through spk_9).In addition to the standard transcript sections (transcripts and items), requests …Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization …In this quickstart, you run an application for speech to text transcription with real-time diarization. Diarization distinguishes between the different speakers who …The Process of Speaker Diarization. The typical workflow for speaker diarization involves several steps: Voice Activity Detection (VAD): This step identifies whether a segment of audio contains ...Jul 1, 2023 · Diarization systems started to incorporate machine learning models such as Gaussian mixture models (GMM). A key work was the one of Reynolds et al. (2000) which introduced the speaker-independent GMM-Universal Background Model (GMM-UBM) for speaker verification. In this work, each vector of features is derived in a data-driven fashion from a ... Speaker Diarization pipeline based on OpenAI Whisper I'd like to thank @m-bain for Wav2Vec2 forced alignment, @mu4farooqi for punctuation realignment algorithm. Please, star the project on github (see top-right corner) if …detection, and diarization. Index Terms: speaker diarization, speaker recognition, robust ASR, noise, conversational speech, DIHARD challenge 1. Introduction Speaker diarization, often referred to as “who spoke when”, is the task of determining how many speakers are present in a conversation and correctly identifying all segments for each ...Audio-Visual People Diarization (AVPD) is an original framework that simultaneously improves audio, video, and audiovisual diarization results. Following a literature review of people diarization for both audio and video content and their limitations, which includes our own contributions, we describe a proposed method for associating … AHC is a clustering method that has been constantly em-ployed in many speaker diarization systems with a number of di erent distance metric such as BIC [110, 129], KL [115] and PLDA [84, 90, 130]. AHC is an iterative process of merging the existing clusters until the clustering process meets a crite-rion. Speaker diarization is the process of partitioning an audio signal into segments according to speaker identity. It answers the question "who spoke when" without prior knowledge of the speakers and, depending on the application, without prior …Speaker diarization is a process of separating individual speakers in an audio stream so that, in the automatic speech recognition (ASR) transcript, each …Mar 21, 2024 · Clustering speaker embeddings is crucial in speaker diarization but hasn't received as much focus as other components. Moreover, the robustness of speaker diarization across various datasets hasn't been explored when the development and evaluation data are from different domains. To bridge this gap, this study thoroughly examines spectral clustering for both same-domain and cross-domain ... pyannote.audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. The term Diarization was initially associated with the task of detecting and segmenting homogeneous audio regions based on speaker identity. This task, widely known as speaker diariza-tion (SD), generates the answer for “who spoke when”. In the past few years, the term diarization has also been used in lin-guistic context. With speaker diarization, you can distinguish between different speakers in your transcription output. Amazon Transcribe can differentiate between a maximum of 10 unique speakers and labels the text from each unique speaker with a unique value (spk_0 through spk_9).In addition to the standard transcript sections (transcripts and items), requests …This process is called speech diarization and can be acchieved using the pyannote-audio library. This is based on PyTorch and hosted on the huggingface site. Here is some code for using it, mostly adapted from code from Dwarkesh Patel. To do this you need a recent GPU probably with at least 6-8GB of VRAM to load the medium model.Speaker diarization is the process of segmenting audio recordings by speaker labels and aims to answer the question “who spoke when?”. Speaker diarization ma...Speaker diarization aims to answer the question of “who spoke when”. In short: diariziation algorithms break down an audio stream of multiple speakers into segments corresponding to the individual speakers. By combining the information that we get from diarization with ASR transcriptions, we can transform the generated transcript …0:18 - Introduction3:31 - Speaker turn detection 6:58 - Turn-to-Diarize 12:20 - Experiments16:28 - Python Library17:29 - Conclusions and future workCode: htt...accurate diarization results, the decoding of the diarization sys-tem may generate more precise outcomes. This is the motiva-tion behind our adoption of a multi-stage iterative approach. As shown in Figure2, the entire diarization inference pipeline con-sists of multi-stage NSD-MA-MSE decoding with increasingly accurate initialized diarization ...Speaker diarization is the process of recognizing “who spoke when.”. In an audio conversation with multiple speakers (phone calls, conference calls, dialogs etc.), the Diarization API identifies the speaker at precisely the time they spoke during the conversation. Below is an example audio from calls recorded at a customer care center ...AssemblyAI. AssemblyAI is a leading speech recognition startup that offers Speech-to-Text transcription with high accuracy, in addition to offering Audio Intelligence features such as Sentiment Analysis, Topic Detection, Summarization, Entity Detection, and more. Its Core Transcription API includes an option for Speaker Diarization.Aug 29, 2023 · diarization ( uncountable) In voice recognition, the process of partitioning an input audio stream into homogeneous segments according to the speaker identity, so as to identify different speakers' turns in a conversation . 2009, Vaclav Matousek, Pavel Mautner, Text, Speech and Dialogue: 12th International Conference, TSD 2009, Pilsen, Czech ... A review of speaker diarization, a task to label audio or video recordings with speaker identity, and its applications. The paper covers the historical development, the neural …Speaker diarization, which is to find the speech seg-ments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization without …Speaker diarization systems are challenged by a trade-off between the temporal resolution and the fidelity of the speaker representation. By obtaining a superior temporal resolution with an enhanced accuracy, a multi-scale approach is a way to cope with such a trade-off. In this paper, we propose a more advanced multi-scale diarization …support speaker diarization research through the creation and distribution of novel data sets; measure and calibrate the performance of systems on these data sets; The task evaluated in the challenge is speaker diarization; that is, the task of determining “who spoke when” in a multispeaker environment based only on audio recordings.Recent years have seen various attempts to streamline the diarization process by merging distinct steps in the SD pipeline, aiming toward end-to-end diarization models. While some methods operate independently of transcribed text and rely only on the acoustic features, others feed the ASR output to the SD model to enhance the …To gauge our new diarization model’s performance in terms of inference speed, we compared the total turnaround time (TAT) for ASR + diarization against leading competitors using repeated ASR requests (with diarization enabled) for each model/vendor in the comparison. Speed tests were performed with the same static 15-minute file.To develop diarization methods for these challenging videos, we create the AVA Audio-Visual Diarization (AVA-AVD) dataset. Our experiments demonstrate that adding AVA-AVD into training set can produce significantly better diarization models for in-the-wild videos despite that the data is relatively small.Speaker Diarization is the task of segmenting audio recordings by speaker labels. A diarization system consists of Voice Activity Detection (VAD) model to get the time stamps of audio where speech is being spoken ignoring the background and Speaker Embeddings model to get speaker embeddings on segments that were previously time stamped. Without speaker diarization, we cannot distinguish the speakers in the transcript generated from automatic speech recognition (ASR). Nowadays, ASR combined with speaker diarization has shown immense use in many tasks, ranging from analyzing meeting transcription to media indexing. Specifically, we combine LSTM-based d-vector audio embeddings with recent work in non-parametric clustering to obtain a state-of-the-art speaker diarization system. Our system is evaluated on three standard public datasets, suggesting that d-vector based diarization systems offer significant advantages over traditional i-vector based systems.Download PDF Abstract: While standard speaker diarization attempts to answer the question "who spoken when", most of relevant applications in reality are more interested in determining "who spoken what". Whether it is the conventional modularized approach or the more recent end-to-end neural diarization (EEND), an additional …Feb 28, 2019 · Attributing different sentences to different people is a crucial part of understanding a conversation. Photo by rawpixel on Unsplash History. The first ML-based works of Speaker Diarization began around 2006 but significant improvements started only around 2012 (Xavier, 2012) and at the time it was considered a extremely difficult task. Speaker Diarization is the task of identifying start and end time of a speaker in an audio file, together with the identity of the speaker i.e. “who spoke when”. Diarization has many applications in speaker indexing, retrieval, speech recognition with speaker identification, diarizing meeting and lectures. In this paper, we have reviewed state-of-art …Speaker diarization is a process of separating individual speakers in an audio stream so that, in the automatic speech recognition (ASR) transcript, each …In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different …Feb 28, 2019 · Attributing different sentences to different people is a crucial part of understanding a conversation. Photo by rawpixel on Unsplash History. The first ML-based works of Speaker Diarization began around 2006 but significant improvements started only around 2012 (Xavier, 2012) and at the time it was considered a extremely difficult task. In this paper, we present a novel speaker diarization system for streaming on-device applications. In this system, we use a transformer transducer to detect the speaker turns, represent each speaker turn by a speaker embedding, then cluster these embeddings with constraints from the detected speaker turns. Compared with …Download the balanced bilingual code-switched corpora soapies_balanced_corpora.tar.gz and unzip it to a directory of your choice. tar -xf soapies_balanced_corpora.tar.gz -C /path/to/corpora. Set up your environment. This step is optional (the main dependencies are PyTorch and Pytorch Lightning ), but you'll hit snags along the way, which may be ...A fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN), given extracted speaker-discriminative embeddings, which decodes in an online fashion while most state-of-the-art systems rely on offline clustering. Expand. 197. Highly Influential.Clustering speaker embeddings is crucial in speaker diarization but hasn't received as much focus as other components. Moreover, the robustness of speaker diarization across various datasets hasn't been explored when the development and evaluation data are from different domains. To bridge this gap, this study thoroughly … Speaker diarization is an advanced topic in speech processing. It solves the problem "who spoke when", or "who spoke what". It is highly relevant with many other techniques, such as voice activity detection, speaker recognition, automatic speech recognition, speech separation, statistics, and deep learning. It has found various applications in ... Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when". In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing.
Speaker diarization (aka Speaker Diarisation) is the process of splitting audio or video inputs automatically based on the speaker's identity. It helps you answer the question "who spoke when?". With the recent application and advancement in deep learning over the last few years, the ability to verify and identify speakers automatically (with …. Save a lot coupons
This repository has speaker diarization recipes which work by git cloning them into the kaldi egs folder. It is based off of this kaldi commit on Feb 5, 2020 ...MSDD [1] model is a sequence model that selectively weighs different speaker embedding scales. You can find more detail of this model here: MS Diarization with DSW. This particular MSDD model is designed to show the most optimized diarization performance on telephonic speech and based on 5 scales: [1.5,1.25,1.0,0.75,0.5] with hop lengths of [0. ...Dec 14, 2022 · High level overview of what's happening with OpenAI Whisper Speaker Diarization:Using Open AI's Whisper model to seperate audio into segments and generate tr... Transcription of a file in Cloud Storage with diarization; Transcription of a file in Cloud Storage with diarization (beta) Transcription of a local file with diarization; Transcription with diarization; Use a custom endpoint with the Speech-to-Text API; AI solutions, generative AI, and ML Application development Application hosting ComputeMake the most of it thanks to our consulting services. 🎹 Speaker diarization 3.0. This pipeline has been trained by Séverin Baroudi with pyannote.audio 3.0.0 using a combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. It ingests mono audio sampled at 16kHz and outputs ...Diart is the official implementation of the paper Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation by Juan Manuel Coria, Hervé Bredin, Sahar Ghannay and Sophie Rosset. We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer …For speaker diarization, the observation could be the d-vector embeddings. train_cluster_ids is also a list, which has the same length as train_sequences. Each element of train_cluster_ids is a 1-dim list or numpy array of strings, containing the ground truth labels for the corresponding sequence in train_sequences.Find papers, benchmarks, datasets and libraries for speaker diarization, the task of segmenting and co-indexing audio recordings by speaker. Compare models, methods and results for various … Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection; Speaker diarization using latent space clustering in generative adversarial network; A study of semi-supervised speaker diarization system using gan mixture model; Learning deep representations by multilayer bootstrap networks for speaker diarization In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different …Speaker diarization systems are challenged by a trade-off between the temporal resolution and the fidelity of the speaker representation. By obtaining a superior temporal resolution with an enhanced accuracy, a multi-scale approach is a way to cope with such a trade-off. In this paper, we propose a more advanced multi-scale diarization …Diarization recipe for CALLHOME, AMI and DIHARD II by Brno University of Technology. The recipe consists of. computing x-vectors. doing agglomerative hierarchical clustering on x-vectors as a first step to produce an initialization. apply variational Bayes HMM over x-vectors to produce the diarization output. score the diarization output. AHC is a clustering method that has been constantly em-ployed in many speaker diarization systems with a number of di erent distance metric such as BIC [110, 129], KL [115] and PLDA [84, 90, 130]. AHC is an iterative process of merging the existing clusters until the clustering process meets a crite-rion. Dec 1, 2012 · Most of diarization systems perform the task in a straight framework which contains some key components. The flow diagram of a conventional diarization system is presented in Fig. 1. A particular speaker diarization system starts with speech/non-speech detection or sometimes simply by just a silence removal. View PDF Abstract: End-to-end neural diarization (EEND) with encoder-decoder-based attractors (EDA) is a promising method to handle the whole speaker diarization problem simultaneously with a single neural network. While the EEND model can produce all frame-level speaker labels simultaneously, it disregards output label ….