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Model Overview

Speaker Recognition is a broad research area that solves two major tasks: speaker identification (who is speaking?) and speaker verification (is the speaker who they claim to be?). In this work, we focus on far-field, text-independent speaker recognition when the identity of the speaker is based on how the speech is spoken, not necessarily on what is being said. Typically such SR systems operate on unconstrained speech utterances, which are converted into vectors of fixed length, called speaker embeddings. Speaker embeddings are also used in automatic speech recognition (ASR) and speech synthesis.

This model with contextnet based encoder[1] is trained end-to-end using angular softmax loss for speaker verification and diarization purposes and for extracting speaker embeddings

Model Architecture

TitaNet-L (large) models employ 1D depth-wise separable convolutions with Squeezeand-Excitation (SE) layers with global context followed by channel attention based statistics pooling layer to map variable-length utterances to a fixed-length embedding (tvector).[1]

Training

These models were trained on a composite dataset comprising of several thousand hours of speech, compiled from various publicly available sources. The NeMo toolkit [2] was used for training this model over few hundred epochs on multiple GPUs.

Datasets

The following datasets are used for training

Voxceleb 1 Dev data (1211 speakers)
Voxceleb 2 Dev data (5994 speakers)
RIR noise
Fisher
Switch board
Librispeech

Performance

This is TitaNet-L model which is based on 1D convolutions, residual connections and SEs structure with 25.3M parameters achieves 0.68% EER on voxceleb clean test trial file and\nAlso achieves the following diarization results on common evaluation datasets (without finetuning on any dev set):

\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\">

EVALUATIONTYPE	NIST_SRE_2000	AMI(Lapel)	AMI(MixHeadset)	CH109
ORACLEKNOWN #SPEAKERS	6.73	2.03	1.73	1.19
ORACLEUNKNOWN #SPEAKERS	5.38	2.03	1.89	1.63

How to use this model

For training and extracting embeddings detailed step by step, procedure has provided in Speaker Verification notebook. and Embeddings extraction script

Embedding Extraction

For a single audio file, one can also extract embeddings inline using

import nemo.collections.asr as nemo_asr\nspeaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')\nembs = speaker_model.get_embedding('audio_path')\n

Speaker Verification

Speaker Verification is a task of verifying if two utterances are from the same speaker or not.\nWe provide a helper function to verify the audio files and return True if two provided audio files are from the same speaker, False otherwise.\nThe audio files should be 16KHz mono channel wav files.

import nemo.collections.asr as nemo_asr\nspeaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')\ndecision = speaker_model.verify_speakers('path/to/one/audio_file','path/to/other/audio_file')\n

Input

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

Output

This model provides embeddings of size 192 from a speaker for a given audio sample.

Limitations

This model is trained on both telephonic and non-telephonic speech from voxceleb datasets, Fisher and switch board. If your domain of data differs from trained data or doesnot show relatively good performance consider finetuning for that speech domain.

References

[1] TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context\n[2] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the license of the NeMo Toolkit [2]. By downloading the public and release version of the model, you accept the terms and conditions of this license.

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In this work, we focus on far-field, text-independent speaker recognition when the identity of the speaker is based on how the speech is spoken, not necessarily on what is being said. Typically such SR systems operate on unconstrained speech utterances, which are converted into vectors of fixed length, called speaker embeddings. Speaker embeddings are also used in automatic speech recognition (ASR) and speech synthesis.\n\nThis model with contextnet based encoder[1] is trained end-to-end using angular softmax loss for speaker verification and diarization purposes and for extracting speaker embeddings\n\n## Model Architecture\n\nTitaNet-L (large) models employ 1D depth-wise separable convolutions with Squeezeand-Excitation (SE) layers with global context followed by channel attention based statistics pooling layer to map variable-length utterances to a fixed-length embedding (tvector).[1] \n\n## Training\nThese models were trained on a composite dataset comprising of several thousand hours of speech, compiled from various publicly available sources. The NeMo toolkit [2] was used for training this model over few hundred epochs on multiple GPUs. \n\n### Datasets\nThe following datasets are used for training\n\n- [Voxceleb 1 Dev data](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) (1211 speakers) \n- [Voxceleb 2 Dev data](http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html) (5994 speakers) \n- [RIR noise](http://www.openslr.org/28/) \n- Fisher\n- Switch board \n- Librispeech\n\n\n## Performance\n\nThis is TitaNet-L model which is based on 1D convolutions, residual connections and SEs structure with 25.3M parameters achieves 0.68% EER on voxceleb clean test trial file and \nAlso achieves the following diarization results on common evaluation datasets (without finetuning on any dev set):\n\n| EVALUATIONTYPE | NIST_SRE_2000 | AMI(Lapel) | AMI(MixHeadset) | CH109 |\n|:---------------------------:|:------------:|:--------------:|:-------------------:|:-----:|\n| ORACLEKNOWN #SPEAKERS | 6.73 | 2.03 | 1.73 | 1.19 |\n| ORACLEUNKNOWN #SPEAKERS | 5.38 | 2.03 | 1.89 | 1.63 |\n\n## How to use this model\n\nFor training and extracting embeddings detailed step by step, procedure has provided in [Speaker Verification notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Recognition_Verification.ipynb). and [Embeddings extraction script](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_recognition/extract_speaker_embeddings.py)\n\n### Embedding Extraction\nFor a single audio file, one can also extract embeddings inline using \n```python\nimport nemo.collections.asr as nemo_asr\nspeaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')\nembs = speaker_model.get_embedding('audio_path')\n```\n### Speaker Verification\nSpeaker Verification is a task of verifying if two utterances are from the same speaker or not.\nWe provide a helper function to verify the audio files and return True if two provided audio files are from the same speaker, False otherwise.\nThe audio files should be 16KHz mono channel wav files.\n```python\nimport nemo.collections.asr as nemo_asr\nspeaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')\ndecision = speaker_model.verify_speakers('path/to/one/audio_file','path/to/other/audio_file')\n```\n\n### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides embeddings of size 192 from a speaker for a given audio sample.\n\n## Limitations\nThis model is trained on both telephonic and non-telephonic speech from voxceleb datasets, Fisher and switch board. If your domain of data differs from trained data or doesnot show relatively good performance consider finetuning for that speech domain. \n\n\n\n## References\n[1] [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://arxiv.org/pdf/2110.04410.pdf)\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n\n\n## Licence\n\nLicense to use this model is covered by the license of the NeMo Toolkit [2]. 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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[440],{"_78":441,"_395":438,"_396":31,"_168":442},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.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.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","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.",[579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603],[550,551,552,553,554],{"_388":464,"_209":464,"_168":390,"_361":574},{"_388":438,"_209":438,"_168":390,"_361":569},{"_388":564,"_209":564,"_168":390,"_361":565},{"_388":559,"_209":559,"_168":390,"_361":560},{"_388":555,"_209":555,"_168":390,"_361":556},"nvidia.partners",[557],{"_78":558,"_395":555,"_396":31,"_168":432},"edge","app.hushly.com",[561],{"_78":562,"_395":559,"_396":31,"_168":563},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[566],{"_78":567,"_395":564,"_396":31,"_168":568},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691],[643,644,645,646],{"_388":658,"_209":658,"_168":390,"_361":659},{"_388":653,"_209":653,"_168":390,"_361":654},{"_388":451,"_209":451,"_168":390,"_361":650},{"_388":564,"_209":564,"_168":390,"_361":647},[648],{"_78":649,"_395":564,"_396":86,"_168":568},"_gh_sess",[651],{"_78":652,"_395":451,"_396":86,"_168":458},"YSC","bizible.com",[655],{"_78":656,"_395":653,"_396":31,"_168":657},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[660],{"_78":661,"_395":658,"_396":31,"_168":662},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":733,"_395":510,"_396":31,"_168":734},{"_78":718,"_395":732,"_396":86,"_168":719},{"_78":701,"_395":513,"_396":31,"_168":625},{"_78":701,"_395":613,"_396":31,"_168":625},{"_78":730,"_395":510,"_396":31,"_168":731},{"_78":718,"_395":516,"_396":86,"_168":719},{"_78":728,"_395":510,"_396":31,"_168":729},{"_78":718,"_395":513,"_396":86,"_168":719},{"_78":726,"_395":612,"_396":31,"_168":727},{"_78":718,"_395":634,"_396":86,"_168":719},{"_78":698,"_395":513,"_396":31,"_168":700},{"_78":724,"_395":510,"_396":86,"_168":725},{"_78":722,"_395":510,"_396":31,"_168":723},{"_78":720,"_395":510,"_396":86,"_168":721},{"_78":718,"_395":608,"_396":86,"_168":719},{"_78":716,"_395":510,"_396":31,"_168":717},{"_78":714,"_395":510,"_396":31,"_168":715},{"_78":712,"_395":510,"_396":86,"_168":713},{"_78":710,"_395":617,"_396":31,"_168":711},{"_78":698,"_395":608,"_396":31,"_168":700},{"_78":708,"_395":510,"_396":31,"_168":709},{"_78":706,"_395":510,"_396":86,"_168":707},{"_78":704,"_395":510,"_396":31,"_168":705},{"_78":702,"_395":510,"_396":31,"_168":703},{"_78":701,"_395":510,"_396":31,"_168":625},{"_78":698,"_395":699,"_396":31,"_168":700},{"_78":696,"_395":510,"_396":31,"_168":697},{"_78":694,"_395":510,"_396":86,"_168":695},{"_78":692,"_395":510,"_396":31,"_168":693},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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