Model Overview

This collection contains the Persian FastConformer Hybrid (Transducer and CTC) Large model (around 114M parameters) trained on Mozilla CommonVoice Persian with around 335 hours of Persian speech.

It utilizes a Google SentencePiece [1] tokenizer with a vocabulary size of 1024. It transcribes text in Persian alphabet without punctuation.

Model Architecture

FastConformer is an optimized version of the Conformer model [2] with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with joint Transducer and CTC decoder loss. You may find more information on the details of FastConformer here: Fast-Conformer Model and about Hybrid Transducer-CTC training here: Hybrid Transducer-CTC.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

This model was initialized with the weights of English FastConformer Hybrid (Transducer and CTC) Large P&C model and fine-tuned to Persian data.

Datasets

All the models in this collection are trained on Mozilla CommonVoice Persian Corpus 15.0.

In order to leverage the entire validated data portion, the standard train/dev/test splits were discarded and replaced with custom splits. The custom splits may be reproduced by:

grouping utterances with identical transcript and sorting utterances (ascendingly) by the (transcript occupancy, transcript) pairs;
selecting the first 10540 utterances for the test set (to maintain the original size);
selecting the second 10540 utterances for the dev set;
selecting the remaining data for the training set.

The transcripts were additionally normalized according to the following script (empty results were discarded):

str | None:\n\n # Skip selected with banned characters\n if set(text) & SKIP:\n return None # skip this\n\n # Remove hashtags - they are not being read in Farsi CV\n text = " ".join(w for w in text.split() if not w.startswith("#"))\n\n # Replace selected characters with others\n for lhs, rhs in REPLACEMENTS.items():\n text = text.replace(lhs, rhs)\n\n # Replace selected characters with empty strings\n for tok in DISCARD:\n text = text.replace(tok, "")\n\n # Unify the symbols that have the same meaning but different Unicode representation.\n text = unicodedata.normalize("NFKC", text)\n\n # Remove hamza's that were not merged with any letter by NFKC.\n text = text.replace("ء", "")\n\n # Remove double whitespace etc.\n return " ".join(t for t in text.split() if t)\n\n\">

import unicodedata\nimport string\n\nSKIP = set(\n    list(string.ascii_letters)\n    + [\n        \"=\",  # occurs only 2x in utterance (transl.): \"twenty = xx\"\n        \"ā\",  # occurs only 4x together with \"š\"\n        \"š\",\n        # Arabic letters\n        \"ة\",  # TEH MARBUTA\n    ]\n)\n\nDISCARD = [\n    # \"(laughter)\" in Farsi\n    \"(خنده)\",\n    # ASCII\n    \"!\",\n    '\"',\n    \"#\",\n    \"&\",\n    \"'\",\n    \"(\",\n    \")\",\n    \",\",\n    \"-\",\n    \".\",\n    \":\",\n    \";\",\n    # Unicode punctuation?\n    \"–\",\n    \"“\",\n    \"”\",\n    \"…\",\n    \"؟\",\n    \"،\",\n    \"؛\",\n    \"ـ\",\n    # Unicode whitespace?\n    \"ً\",\n    \"ٌ\",\n    \"َ\",\n    \"ُ\",\n    \"ِ\",\n    \"ّ\",\n    \"ْ\",\n    \"ٔ\",\n    # Other\n    \"«\",\n    \"»\",\n]\n\nREPLACEMENTS = {\n    \"أ\": \"ا\",\n    \"ۀ\": \"ە\",\n    \"ك\": \"ک\",\n    \"ي\": \"ی\",\n    \"ى\": \"ی\",\n    \"ﯽ\": \"ی\",\n    \"ﻮ\": \"و\",\n    \"ے\": \"ی\",\n    \"ﺒ\": \"ب\",\n    \"ﻢ\": \"ﻡ\",\n    \"٬\": \" \",\n    \"ە\": \"ه\",\n}\n\n\ndef maybe_normalize(text: str) -> str | None:\n\n    # Skip selected with banned characters\n    if set(text) & SKIP:\n        return None  # skip this\n\n    # Remove hashtags - they are not being read in Farsi CV\n    text = \" \".join(w for w in text.split() if not w.startswith(\"#\"))\n\n    # Replace selected characters with others\n    for lhs, rhs in REPLACEMENTS.items():\n        text = text.replace(lhs, rhs)\n\n    # Replace selected characters with empty strings\n    for tok in DISCARD:\n        text = text.replace(tok, \"\")\n\n    # Unify the symbols that have the same meaning but different Unicode representation.\n    text = unicodedata.normalize(\"NFKC\", text)\n\n    # Remove hamza's that were not merged with any letter by NFKC.\n    text = text.replace(\"ء\", \"\")\n\n    # Remove double whitespace etc.\n    return \" \".join(t for t in text.split() if t)\n\n

Tokenizer Construction

The tokenizer for this model was built using text corpus provided with the train dataset.

We build a Google Sentencepiece Tokenizer [1] with the following script :

" \\\n --vocab_size=1024 \\\n --tokenizer="spe" \\\n --spe_type="bpe" \\\n --spe_character_coverage=1.0 \\\n --spe_max_sentencepiece_length=4 \\\n --log\n\">

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n  --manifest=\"train_manifest.json\" \\\n  --data_root=\"<OUTPUT DIRECTORY FOR TOKENIZER>\" \\\n  --vocab_size=1024 \\\n  --tokenizer=\"spe\" \\\n  --spe_type=\"bpe\" \\\n  --spe_character_coverage=1.0 \\\n  --spe_max_sentencepiece_length=4 \\\n  --log\n

Performance

The performance of Automatic Speech Recognition models is measuring using Character Error Rate (CER) and Word Error Rate (WER).

The model obtains the following scores on our custom dev and test splits of Mozilla CommonVoice Persian 15.0:

\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\">

Model	%WER/CER dev	%WER/CER test
RNNT head	15.44 / 3.89	15.48 / 4.63
CTC head	13.18 / 3.38	13.16 / 3.85

How to Use this Model

The model is available for use in the NeMo toolkit [3], 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\nasr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name=\"stt_fa_fastconformer_hybrid_large\")\n

Transcribing text with this model

Using Transducer mode inference:

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_fa_fastconformer_hybrid_large\" \\\n  audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\"\n

Using CTC mode inference:

" \\\n decoder_type="ctc"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_fa_fastconformer_hybrid_large\" \\\n  audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\" \\\n  decoder_type=\"ctc\"\n

Input

This model accepts 16 kHz mono-channel audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Limitations

Since this model was trained on publicly 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] Google Sentencepiece Tokenizer

[2] ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

[3] NVIDIA NeMo Toolkit

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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The model has a vocab size of 1024.","## Model Overview\n\nThis collection contains the Persian FastConformer Hybrid (Transducer and CTC) Large model (around 114M parameters) trained on Mozilla CommonVoice Persian with around 335 hours of Persian speech.\n\nIt utilizes a Google SentencePiece [1] tokenizer with a vocabulary size of 1024. It transcribes text in Persian alphabet without punctuation.\n\n## Model Architecture\n\nFastConformer is an optimized version of the Conformer model [2] with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with joint Transducer and CTC decoder loss. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) and about Hybrid Transducer-CTC training here: [Hybrid Transducer-CTC](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#hybrid-transducer-ctc).\n\n## Training\n\nThe NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_finetune.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/asr_finetune/speech_to_text_finetune.yaml).\n\nThe tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).\n\nThis model was initialized with the weights of [English FastConformer Hybrid (Transducer and CTC) Large P&C model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_pc) and fine-tuned to Persian data.\n\n### Datasets\n\nAll the models in this collection are trained on Mozilla CommonVoice Persian Corpus 15.0.\n\nIn order to leverage the entire validated data portion, the standard train/dev/test splits were discarded and replaced with custom splits. The custom splits may be reproduced by:\n- grouping utterances with identical transcript and sorting utterances (ascendingly) by the (transcript occupancy, transcript) pairs;\n- selecting the first 10540 utterances for the test set (to maintain the original size);\n- selecting the second 10540 utterances for the dev set;\n- selecting the remaining data for the training set.\n\nThe transcripts were additionally normalized according to the following script (empty results were discarded):\n\n```python\nimport unicodedata\nimport string\n\nSKIP = set(\n list(string.ascii_letters)\n + [\n \"=\", # occurs only 2x in utterance (transl.): \"twenty = xx\"\n \"ā\", # occurs only 4x together with \"š\"\n \"š\",\n # Arabic letters\n \"ة\", # TEH MARBUTA\n ]\n)\n\nDISCARD = [\n # \"(laughter)\" in Farsi\n \"(خنده)\",\n # ASCII\n \"!\",\n '\"',\n \"#\",\n \"&\",\n \"'\",\n \"(\",\n \")\",\n \",\",\n \"-\",\n \".\",\n \":\",\n \";\",\n # Unicode punctuation?\n \"–\",\n \"“\",\n \"”\",\n \"…\",\n \"؟\",\n \"،\",\n \"؛\",\n \"ـ\",\n # Unicode whitespace?\n \"ً\",\n \"ٌ\",\n \"َ\",\n \"ُ\",\n \"ِ\",\n \"ّ\",\n \"ْ\",\n \"ٔ\",\n # Other\n \"«\",\n \"»\",\n]\n\nREPLACEMENTS = {\n \"أ\": \"ا\",\n \"ۀ\": \"ە\",\n \"ك\": \"ک\",\n \"ي\": \"ی\",\n \"ى\": \"ی\",\n \"ﯽ\": \"ی\",\n \"ﻮ\": \"و\",\n \"ے\": \"ی\",\n \"ﺒ\": \"ب\",\n \"ﻢ\": \"ﻡ\",\n \"٬\": \" \",\n \"ە\": \"ه\",\n}\n\n\ndef maybe_normalize(text: str) -> str | None:\n\n # Skip selected with banned characters\n if set(text) & SKIP:\n return None # skip this\n\n # Remove hashtags - they are not being read in Farsi CV\n text = \" \".join(w for w in text.split() if not w.startswith(\"#\"))\n\n # Replace selected characters with others\n for lhs, rhs in REPLACEMENTS.items():\n text = text.replace(lhs, rhs)\n\n # Replace selected characters with empty strings\n for tok in DISCARD:\n text = text.replace(tok, \"\")\n\n # Unify the symbols that have the same meaning but different Unicode representation.\n text = unicodedata.normalize(\"NFKC\", text)\n\n # Remove hamza's that were not merged with any letter by NFKC.\n text = text.replace(\"ء\", \"\")\n\n # Remove double whitespace etc.\n return \" \".join(t for t in text.split() if t)\n\n```\n\n### Tokenizer Construction\n\nThe tokenizer for this model was built using text corpus provided with the train dataset.\n\nWe build a Google Sentencepiece Tokenizer [1] with the following script :\n\n```bash\npython [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n --manifest=\"train_manifest.json\" \\\n --data_root=\"\" \\\n --vocab_size=1024 \\\n --tokenizer=\"spe\" \\\n --spe_type=\"bpe\" \\\n --spe_character_coverage=1.0 \\\n --spe_max_sentencepiece_length=4 \\\n --log\n```\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measuring using Character Error Rate (CER) and Word Error Rate (WER).\n\nThe model obtains the following scores on our custom dev and test splits of Mozilla CommonVoice Persian 15.0:\n\n| Model | %WER/CER dev | %WER/CER test |\n|-----------|--------------|---------------|\n| RNNT head | 15.44 / 3.89 | 15.48 / 4.63 |\n| CTC head | 13.18 / 3.38 | 13.16 / 3.85 |\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [3], 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\nasr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name=\"stt_fa_fastconformer_hybrid_large\")\n```\n\n### Transcribing text with this model\n\nUsing Transducer mode inference:\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_fa_fastconformer_hybrid_large\" \\\n audio_dir=\"\"\n```\n\nUsing CTC mode inference:\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_fa_fastconformer_hybrid_large\" \\\n audio_dir=\"\" \\\n decoder_type=\"ctc\"\n```\n\n### Input\n\nThis model accepts 16 kHz mono-channel audio (wav files) as input.\n\n### Output\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n## Limitations\n\nSince this model was trained on publicly 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] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[2] [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://arxiv.org/abs/2005.03191)\n\n[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\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. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","STT Fa FastConformer Hybrid Transducer-CTC Large",["D",1699382056734],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_fa_fastconformer_hybrid_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",425060453,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241,242,243,244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_204":279,"_250":280,"_251":281,"_253":254},{"_204":275,"_250":276,"_251":277,"_253":254},{"_204":271,"_250":272,"_251":273,"_253":254},{"_204":269,"_250":269,"_251":270,"_253":254},{"_204":267,"_250":267,"_251":268,"_253":254},{"_204":265,"_250":265,"_251":266,"_253":254},{"_204":263,"_250":263,"_251":264,"_253":254},{"_204":261,"_250":261,"_251":262,"_253":254},{"_204":259,"_250":259,"_251":260,"_253":254},{"_204":256,"_250":256,"_251":257,"_253":254},{"_204":249,"_250":249,"_251":252,"_253":254},"PyTorchLightning","value","filter",{"_255":254,"_250":249},"category","label","field","PyTorch with NeMo",{"_255":258,"_250":256},"framework","PyTorch",{"_255":258,"_250":259},"Persian",{"_255":254,"_250":261},"NeMo",{"_255":258,"_250":263},"Hybrid Transducer-CTC",{"_255":254,"_250":265},"FastConformer",{"_255":254,"_250":267},"Fa",{"_255":254,"_250":269},"DL","soln_dl",{"_255":274,"_250":272},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_255":278,"_250":276},"application","AI","soln_ai",{"_255":274,"_250":280},{"_106":1260,"_108":1261},"ngcApps",[1213],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1194":1195,"_1196":1197,"_1198":1199,"_1200":1201,"_1202":293,"_137":317,"_1203":1204,"_1205":56,"_1206":85,"_1207":85,"_1208":1209,"_1210":85,"_1211":1212},"amplitude",{"_1191":1192,"_829":-7,"_1193":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1185":1186,"_1187":1188,"_1189":1190},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1069":223,"_1070":1071,"_1072":1073,"_1074":1075},"announcements",[],"consentState",{"_354":1059,"_1060":85,"_1061":31,"_1062":31,"_1063":1064},"consentConfig",{"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355},"showContactAdmin",1785765795644,[],[],[331,332,333,334,335],{"_45":59,"_47":69,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":67,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":101,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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,[493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514],[378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394],{"_395":488,"_204":488,"_166":223,"_368":489},{"_395":484,"_204":484,"_166":223,"_368":485},{"_395":479,"_204":479,"_166":223,"_368":480},{"_395":475,"_204":475,"_166":223,"_368":476},{"_395":470,"_204":470,"_166":223,"_368":471},{"_395":465,"_204":465,"_166":223,"_368":466},{"_395":457,"_204":457,"_166":223,"_368":458},{"_395":449,"_204":449,"_166":223,"_368":450},{"_395":444,"_204":444,"_166":223,"_368":445},{"_395":439,"_204":439,"_166":223,"_368":440},{"_395":434,"_204":434,"_166":223,"_368":435},{"_395":429,"_204":429,"_166":223,"_368":430},{"_395":424,"_204":424,"_166":223,"_368":425},{"_395":417,"_204":417,"_166":223,"_368":418},{"_395":412,"_204":412,"_166":223,"_368":413},{"_395":405,"_204":405,"_166":223,"_368":406},{"_395":396,"_204":396,"_166":223,"_368":397},"hostName","amazon-adsystem.com",[398,399],{"_77":404,"_401":396,"_402":31,"_166":403},{"_77":400,"_401":396,"_402":31,"_166":403},"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",[407,408],{"_77":411,"_401":405,"_402":31,"_166":410},{"_77":409,"_401":405,"_402":31,"_166":410},"IDE","This domain is owned by Doubleclick (Google). 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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. 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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.",4,[586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610],[557,558,559,560,561],{"_395":470,"_204":470,"_166":223,"_368":581},{"_395":444,"_204":444,"_166":223,"_368":576},{"_395":571,"_204":571,"_166":223,"_368":572},{"_395":566,"_204":566,"_166":223,"_368":567},{"_395":562,"_204":562,"_166":223,"_368":563},"nvidia.partners",[564],{"_77":565,"_401":562,"_402":31,"_166":438},"edge","app.hushly.com",[568],{"_77":569,"_401":566,"_402":31,"_166":570},"*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",[573],{"_77":574,"_401":571,"_402":31,"_166":575},"_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. 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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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