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-S (small) 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 6.4M parameters achieves 1.08% 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	6.37	2.00	2.22	1.11
ORACLEUNKNOWN #SPEAKERS	5.49	2.30	1.97	1.42

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_small')\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_small')\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-S (small) 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 6.4M parameters achieves 1.08% 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 | 6.37 | 2.00 | 2.22 | 1.11 |\n| ORACLEUNKNOWN #SPEAKERS | 5.49 | 2.30 | 1.97 | 1.42 |\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_small')\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_small')\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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by Google Analytics to identify and track an individual session with your device.","__sec__token","__sec__cid","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","_gd_visitor","_biz_dfsA","This cookie is associated with CloudFlare, used to speed up page load times. 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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).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","__sec__ghost","_gd_svisitor","info.nvidia.com","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.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","nv.referrerHref","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","__sec_crid","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","s_tp","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","This _gd_session cookie is used to collect visitor data related to the user's visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. 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Key	Value
AMI MixHeadset	2.22
CH109	1.11
AMI Lapel	2.00
NIST-SRE 2000	6.37

Key	Value
AMI MixHeadset	1.97
CH109	1.42
AMI Lapel	2.3
NIST-SRE 2000	5.49