Model Overview

This collection contains Multiscale Diarization Decoder (MSDD) trained on Fisher Corpus. The checkpoint contains two different models: pretrained TitaNet-L (around 25.3M parameters) and MSDD (around 5.8M parameters). MSDD models can be jointly trained and used for inference with pretrained speaker embedding extractors, such as TitaNet and ECAPA-TDNN.

Model Architecture

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 [0.75,0.625,0.5,0.375,0.25]. Therefore, the default temporal resolution is 0.25s while the hoplength could be changed to have better temporal resolution.

Training

The NeMo toolkit [2] was used for training the models for over 10 epochs. These model are trained with this example training script and this MSDD telephonic config.

Datasets

This diarization model is trained on 10,000 sessions (Approx. 1500h) in Fisher Corpus.

Performance

The performance of Speaker Diarization models is measured by Diarization Error Rate (DER). Since the MSDD model requires initializing clustering results, the final diarization accuracy is largely affected by clustering performance.

The model obtains the following DER scores on the following evaluation datasets -

Diarization evaluation condition:

\n
Number of speakers are estimated:
\n-oracle_num_speakers=False
\n
\n
Oracle VAD is used to factor out the performance of VAD:
\n-oracle_VAD=True
\n
\n
For clustering, NMESC is used with the following parameters:
\n-max_num_speakers=8
\n-max_rp_threshold=0.15
\n-sparse_search_volume=30
\n-multiscale_weights=[1,1,1,1,1]
\n
\n
MSDD parameters:
\n-diar_window_length=50
\n-sigmoid_threshold=0.7
\n-overlap_infer_spk_limit=5
\n

Diarization Performance:

CallHome American English (CHEAS, LDC97S42) 109 2-speaker subset: CH109

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

	Forgiving	Fair	Full
(collar, ignore_overlap)	(0.25, True)	(0.25, False)	(0.0, False)
False Alarm	-	0.62%	1.80%
Miss	-	2.47%	5.96%
Confusion	-	0.43%	2.10%
DER	0.58%	3.52%	9.86%

NIST-SRE-2000 (LDC2001S97) Disc8: CallHome

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

	Forgiving	Fair	Full
(collar, ignore_overlap)	(0.25, True)	(0.25, False)	(0.0, False)
False Alarm	-	1.05%	2.24%
Miss	-	7.62%	11.09%
Confusion	-	4.06%	6.03%
DER	4.15%	12.73%	19.37%

How to Use this Model

The model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr\ndiar_model = nemo_asr.models.EncDecDiarLabelModel.from_pretrained(model_name=\"diar_msdd_telephonic\")\n

Diarize speech recordings with this model

\\\n diarizer.msdd_model.model_path= \\\n diarizer.oracle_vad=False \\\n diarizer.manifest_filepath= \\\n diarizer.out_dir= \\\n\">

python [NEMO_GIT_FOLDER]/examples/speaker_tasks/diarization/multiscale_diar_decoder_infer.py \\\n    diarizer.vad.model_path=<NeMo VAD model path> \\\n    diarizer.msdd_model.model_path=<NeMo MSDD model path> \\\n    diarizer.oracle_vad=False \\\n    diarizer.manifest_filepath=<test_manifest> \\\n    diarizer.out_dir=<test_temp_dir> \\\n

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides estimated speaker labels and corresponding timestamps in RTTM format.

Limitations

Since this model is only trained on telephonic speech, diarization performance on other acoustic conditions might show a degraded performance compared to telephonic speech.

References

[1] Multi-scale speaker diarization with dynamic scale weighting
\n[2] NVIDIA NeMo Toolkit

License

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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The checkpoint contains two different models: pretrained TitaNet-L (around 25.3M parameters) and MSDD (around 5.8M parameters). MSDD models can be jointly trained and used for inference with pretrained speaker embedding extractors, such as TitaNet and ECAPA-TDNN.\n\n## Model Architecture\n\nMSDD [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](https://arxiv.org/abs/2203.15974).\n\nThis 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 `[0.75,0.625,0.5,0.375,0.25]`. Therefore, the default temporal resolution is `0.25s` while the hoplength could be changed to have better temporal resolution.\n\n## Training\n\nThe NeMo toolkit [2] was used for training the models for over 10 epochs. These model are trained with this [example training script](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py) and this [MSDD telephonic config](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/diarization/conf/neural_diarizer/msdd_5scl_15_05_50Povl_256x3x32x2.yaml).\n\n### Datasets\n\nThis diarization model is trained on 10,000 sessions (Approx. 1500h) in Fisher Corpus.\n\n## Performance\n\nThe performance of Speaker Diarization models is measured by Diarization Error Rate (DER). Since the MSDD model requires initializing clustering results, the final diarization accuracy is largely affected by clustering performance.\n\nThe model obtains the following DER scores on the following evaluation datasets -\n\nDiarization evaluation condition: \n- Number of speakers are estimated: \n -`oracle_num_speakers=False` \n \n- Oracle VAD is used to factor out the performance of VAD: \n -`oracle_VAD=True` \n \n- For clustering, NMESC is used with the following parameters: \n -`max_num_speakers=8` \n -`max_rp_threshold=0.15` \n -`sparse_search_volume=30` \n -`multiscale_weights=[1,1,1,1,1]` \n \n- MSDD parameters: \n -`diar_window_length=50` \n -`sigmoid_threshold=0.7` \n -`overlap_infer_spk_limit=5` \n\nDiarization Performance: \n\n**CallHome American English (CHEAS, LDC97S42) 109 2-speaker subset: CH109**\n| | Forgiving | Fair | Full |\n|-------------------------------------:|---------------------------------|----------------------------------|---------------------------------|\n| (collar, ignore_overlap)| (0.25, True) | (0.25, False) | (0.0, False) |\n| False Alarm | - | 0.62% | 1.80% |\n| Miss | - | 2.47% | 5.96% |\n| Confusion | - | 0.43% | 2.10% |\n| DER | **0.58%** | **3.52%** | **9.86%** |\n\n**NIST-SRE-2000 (LDC2001S97) Disc8: CallHome**\n| | Forgiving | Fair | Full |\n|-------------------------------------:|---------------------------------|----------------------------------|---------------------------------|\n| (collar, ignore_overlap)| (0.25, True) | (0.25, False) | (0.0, False) |\n| False Alarm | - | 1.05% | 2.24% |\n| Miss | - | 7.62% | 11.09% |\n| Confusion | - | 4.06% | 6.03% |\n| DER | **4.15%** | **12.73%** | **19.37%** |\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n\n```python\nimport nemo.collections.asr as nemo_asr\ndiar_model = nemo_asr.models.EncDecDiarLabelModel.from_pretrained(model_name=\"diar_msdd_telephonic\")\n```\n\n### Diarize speech recordings with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/speaker_tasks/diarization/multiscale_diar_decoder_infer.py \\\n diarizer.vad.model_path= \\\n diarizer.msdd_model.model_path= \\\n diarizer.oracle_vad=False \\\n diarizer.manifest_filepath= \\\n diarizer.out_dir= \\\n```\n\n### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides estimated speaker labels and corresponding timestamps in RTTM format.\n\n## Limitations\n\nSince this model is only trained on telephonic speech, diarization performance on other acoustic conditions might show a degraded performance compared to telephonic speech.\n\n## References\n\n[1] [Multi-scale speaker diarization with dynamic scale weighting](https://arxiv.org/abs/2203.15974) \n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) \n\n### License\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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