Model Overview

Language Identification model as detailed in [1]. This model can be used for Spoken Language Identification (LangID / LID) and serves as the first step for Automatic Speech Recognition (ASR).

Model Architecture

This model uses Conformer architecture [2]. For pretraining, it uses 18 conformer layers with 8 attention heads and a hidden depth of 512. For finetuning, only the bottom (closest to audio input) 9 layers are selected from the pretrained model.

Training

Training occurs over separate pretraining and finetuning stages.

Pretraining

Pretraining solves the contrastive loss task detailed in [3], using AdamW optimization over 400k updates with batchsize 2048. Training uses Noam scheduling with a peak learning rate of 1.4e-3 after 25k warmup steps.

Finetuning

Finetuning statistically pools outputs before projection through a bottleneck vector of size 256. The model is then trained on a cross-entropy loss task over 20 epochs. Training uses AdamW optimization with Noam scheduling and a warm-up ratio of 0.2. Training data is augmented by masking 50% of time steps using SpecAugment patches of size 24 and applying 4 frequency masks of size 10. Further, data is augmented with the Room Impulse Response (RIR) and Noise [4] corpora and speed perturbation of 0.95x and 1.05x speeds.

The final model parameters are averaged over five best checkpoints in respect to validation loss.

Datasets

Pretraining uses the 400k unlabeled hours of the VoxPopuli dataset [5], a multilingual dataset comprised of European Parliament discussions across 23 of the 24 official languages of the European Union (Maltese is unincluded).

Finetuning training and evaluation uses the Voxlingua107 dataset [6]. It contains YouTube data for 107 languages in the official training set. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. For training, we segment the training set into segments of 4, 5, and 6 seconds.

To preserve integrity of the evaluation set, we stratified shuffle split 10% of training data to use as a validation set during training.

Performance

The model achieves 5.34% error rate on official evaluation set which contains 1609 verified utterances of 33 languages.

How to Use this Model

The model is available for use in the NeMo toolkit 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\nimport nemo.collections.asr as nemo_asr\nvad_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name=\"langid_pearlnet\")\n

Input

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

Output

This model provides spoken language identification of the given utterance.

Limitations

Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

References

[1] Bartley, Travis M., et al. \"Accidental learners: Spoken language identification in multilingual self-supervised models.\" ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.

[2] Gulati, Anmol, et al. \"Conformer: Convolution-augmented Transformer for Speech Recognition.\" Proc. Interspeech 2020 (2020): 5036-5040. ISCA, 2020.

[3] Baevski, Alexei, et al. \"wav2vec 2.0: A framework for self-supervised learning of speech representations.\" Advances in neural information processing systems 33 (2020): 12449-12460. NeurIPS, 2020.

[4] Ko, Tom, et al. \"A study on data augmentation of reverberant speech for robust speech recognition.\" 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017.

[5] Wang, Changhan, et al. \"VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation.\" ACL 2021-59th Annual Meeting of the Association for Computational Linguistics. 2021. ACL, 2021.

[6] Valk, Jörgen, and Tanel Alumäe. \"VoxLingua107: a dataset for spoken language recognition.\" 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021.

Licence

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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This model can be used for Spoken Language Identification (LangID / LID) and serves as the first step for Automatic Speech Recognition (ASR).\n\n## Model Architecture\n\nThis model uses Conformer architecture [2]. For pretraining, it uses 18 conformer layers with 8 attention heads and a hidden depth of 512. For finetuning, only the bottom (closest to audio input) 9 layers are selected from the pretrained model.\n\n## Training\n\nTraining occurs over separate pretraining and finetuning stages.\n\n### Pretraining\n\nPretraining solves the contrastive loss task detailed in [3], using AdamW optimization over 400k updates with batchsize 2048. Training uses Noam scheduling with a peak learning rate of 1.4e-3 after 25k warmup steps.\n\n### Finetuning\n\nFinetuning statistically pools outputs before projection through a bottleneck vector of size 256. The model is then trained on a cross-entropy loss task over 20 epochs. Training uses AdamW optimization with Noam scheduling and a warm-up ratio of 0.2. Training data is augmented by masking 50% of time steps using SpecAugment patches of size 24 and applying 4 frequency masks of size 10. Further, data is augmented with the Room Impulse Response (RIR) and Noise [4] corpora and speed perturbation of 0.95x and 1.05x speeds.\n\nThe final model parameters are averaged over five best checkpoints in respect to validation loss.\n\n### Datasets\n\nPretraining uses the 400k unlabeled hours of the VoxPopuli dataset [5], a multilingual dataset comprised of European Parliament discussions across 23 of the 24 official languages of the European Union (Maltese is unincluded).\n\nFinetuning training and evaluation uses the Voxlingua107 dataset [6]. It contains YouTube data for 107 languages in the official training set. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. For training, we segment the training set into segments of 4, 5, and 6 seconds.\n\nTo preserve integrity of the evaluation set, we stratified shuffle split 10% of training data to use as a validation set during training.\n\n## Performance\n\nThe model achieves 5.34% error rate on official evaluation set which contains 1609 verified utterances of 33 languages.\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit 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```\nimport nemo\nimport nemo.collections.asr as nemo_asr\nvad_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name=\"langid_pearlnet\")\n```\n\n### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides spoken language identification of the given utterance.\n\n## Limitations\n\nSince this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.\n\n## References\n\n[1] Bartley, Travis M., *et al.* \"Accidental learners: Spoken language identification in multilingual self-supervised models.\" ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.\n\n[2] Gulati, Anmol, *et al.* \"Conformer: Convolution-augmented Transformer for Speech Recognition.\" Proc. Interspeech 2020 (2020): 5036-5040. ISCA, 2020.\n\n[3] Baevski, Alexei, *et al.* \"wav2vec 2.0: A framework for self-supervised learning of speech representations.\" Advances in neural information processing systems 33 (2020): 12449-12460. NeurIPS, 2020.\n\n[4] Ko, Tom, *et al.* \"A study on data augmentation of reverberant speech for robust speech recognition.\" 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017.\n\n[5] Wang, Changhan, *et al.* \"VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation.\" ACL 2021-59th Annual Meeting of the Association for Computational Linguistics. 2021. ACL, 2021.\n\n[6] Valk, Jörgen, and Tanel Alumäe. \"VoxLingua107: a dataset for spoken language recognition.\" 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021.\n\n### Licence\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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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. 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The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[983],{"_78":958,"_426":981,"_427":34,"_209":984},"The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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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. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","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.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. 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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Key	Value
Architecture	Conformer
Outputs	predicted language of given utterance
Inputs	16000 KHZ MONO-CHANNEL AUDIO (WAV FILES)