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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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526],[364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380],{"_381":488,"_208":488,"_167":383,"_354":489},{"_381":483,"_208":483,"_167":383,"_354":484},{"_381":479,"_208":479,"_167":383,"_354":480},{"_381":474,"_208":474,"_167":383,"_354":475},{"_381":469,"_208":469,"_167":383,"_354":470},{"_381":457,"_208":457,"_167":383,"_354":458},{"_381":445,"_208":445,"_167":383,"_354":446},{"_381":440,"_208":440,"_167":383,"_354":441},{"_381":429,"_208":429,"_167":383,"_354":430},{"_381":424,"_208":424,"_167":383,"_354":425},{"_381":419,"_208":419,"_167":383,"_354":420},{"_381":414,"_208":414,"_167":383,"_354":415},{"_381":409,"_208":409,"_167":383,"_354":410},{"_381":404,"_208":404,"_167":383,"_354":405},{"_381":399,"_208":399,"_167":383,"_354":400},{"_381":392,"_208":392,"_167":383,"_354":393},{"_381":382,"_208":382,"_167":383,"_354":384},"hostName","amazon-adsystem.com","",[385,386],{"_78":391,"_388":382,"_389":31,"_167":390},{"_78":387,"_388":382,"_389":31,"_167":390},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[394,395],{"_78":398,"_388":392,"_389":31,"_167":397},{"_78":396,"_388":392,"_389":31,"_167":397},"IDE","This domain is owned by Doubleclick (Google). 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It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[447,448,449,450],{"_78":456,"_388":445,"_389":31,"_167":452},{"_78":454,"_388":445,"_389":31,"_167":455},{"_78":453,"_388":445,"_389":31,"_167":452},{"_78":451,"_388":445,"_389":31,"_167":452},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[459,460,461,462],{"_78":468,"_388":457,"_389":31,"_167":466},{"_78":467,"_388":457,"_389":31,"_167":466},{"_78":465,"_388":457,"_389":31,"_167":466},{"_78":463,"_388":457,"_389":31,"_167":464},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[471],{"_78":472,"_388":469,"_389":31,"_167":473},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[476],{"_78":477,"_388":474,"_389":31,"_167":478},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[481],{"_78":482,"_388":479,"_389":31,"_167":423},"R","6sc.co",[485],{"_78":486,"_388":483,"_389":31,"_167":487},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635],[580,581,582,583,584],{"_381":474,"_208":474,"_167":383,"_354":606},{"_381":440,"_208":440,"_167":383,"_354":601},{"_381":594,"_208":594,"_167":383,"_354":595},{"_381":590,"_208":590,"_167":383,"_354":591},{"_381":585,"_208":585,"_167":383,"_354":586},"nvcrm.my.site.com",[587],{"_78":588,"_388":585,"_389":31,"_167":589},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[592],{"_78":593,"_388":590,"_389":31,"_167":423},"edge","github.com",[596,597],{"_78":600,"_388":594,"_389":31,"_167":599},{"_78":598,"_388":594,"_389":31,"_167":599},"logged_in","This domain is owned by Github. 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