Model Overview

QuartzNet15x5 checkpoints available here are trained using NeMo on the Ai-shell 2 Mandarin Chinese dataset.

It utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the Aishell-2 Mandard Corpus.

Model Architecture

The Quartznet model is composed of multiple blocks with residual connections between them, trained with CTC loss. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers.

The Quartznet 15x5 model consists of 79 layers and has a total of 18.9 million parameters, with five blocks that repeat fifteen times plus four additional convolutional layers [1].

Training

This model was trained on the open source Aishell-2 [2] corpus consisting of about 1000 hours transcribed Mandarin speech. The NeMo toolkit [3] was used for training this model over several hundred epochs on multiple GPUs.

The model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it more robust to noise and is also augmented with narrowband speech codecs to be more accurate with 8 kHz telephony data.

Datasets

While training this model, we used the following datasets:

Aishell-2 corpus

Performance

The performance of Automatic Speech Recognition models is measuring using Character Error Rate.

The model obtains the following scores on the following evaluation datasets -

7.2 % on Aishell-2 dev_ios
7.2 % on Aishell-2 test_ios

Note that these scores on Aishell-2 are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.

How to Use this Model

The model is available for use in the NeMo toolkit [4], 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.EncDecCTCModel.from_pretrained(model_name=\"stt_zh_quartznet15x5\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_zh_quartznet15x5\" \\\n  audio_dir=\"\"\n

Input

This model accepts 16000 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 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] QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions

[2] AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale

[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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It has comparable accuracy to Jasper while having much fewer parameters. This particular model has 15 blocks each repeated 5 times.","## Model Overview\n\nQuartzNet15x5 checkpoints available here are trained using NeMo on the Ai-shell 2 Mandarin Chinese dataset.\n\nIt utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the Aishell-2 Mandard Corpus.\n\n## Model Architecture\n\nThe Quartznet model is composed of multiple blocks with residual connections between them, trained with CTC loss. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers.\n\n\nThe Quartznet 15x5 model consists of 79 layers and has a total of 18.9 million parameters, with five blocks that repeat fifteen times plus four additional convolutional layers [1].\n\n## Training\n\nThis model was trained on the open source Aishell-2 [2] corpus consisting of about 1000 hours transcribed Mandarin speech. The NeMo toolkit [3] was used for training this model over several hundred epochs on multiple GPUs.\n\nThe model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it more robust to noise and is also augmented with narrowband speech codecs to be more accurate with 8 kHz telephony data.\n\n### Datasets\n\nWhile training this model, we used the following datasets:\n\n- Aishell-2 corpus\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measuring using Character Error Rate. \n\nThe model obtains the following scores on the following evaluation datasets -\n\n- 7.2 % on `Aishell-2 dev_ios`\n- 7.2 % on `Aishell-2 test_ios`\n\nNote that these scores on Aishell-2 are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [4], 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.EncDecCTCModel.from_pretrained(model_name=\"stt_zh_quartznet15x5\")\n```\n### Transcribing text with this model\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_zh_quartznet15x5\" \\\n audio_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 transcribed speech as a string for a given audio sample.\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] [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://arxiv.org/abs/1910.10261)\n\n[2] [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://arxiv.org/abs/1808.10583)\n\n[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\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).","STT Zh Quartznet15x5",["D",1680637597899],"/api/image/nvidia/nemo/models/stt_zh_quartznet15x5","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[305,306,307,308,309],[],[],[],{"_90":318,"_137":318,"_138":319,"_140":141},{"_90":316,"_137":316,"_138":317,"_140":141},{"_90":314,"_137":314,"_138":315,"_140":141},{"_90":312,"_137":312,"_138":313,"_140":141},{"_90":310,"_137":310,"_138":311,"_140":141},"Zh",{"_142":141,"_137":310},"STT",{"_142":141,"_137":312},"QuartzNet",{"_142":141,"_137":314},"PytorchLightning",{"_142":141,"_137":316},"NeMo",{"_142":320,"_137":318},"framework",{"_199":1491,"_201":1492},"ngcApps",[1444],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1425":1426,"_1427":1428,"_1429":1430,"_1431":1432,"_1433":332,"_230":356,"_1434":1435,"_1436":51,"_1437":178,"_1438":178,"_1439":1440,"_1441":178,"_1442":1443},"amplitude",{"_1422":1423,"_1028":-7,"_1424":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1416":1417,"_1418":1419,"_1420":1421},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1303":258,"_1304":1305,"_1306":1307,"_1308":1309},"announcements",[],"consentState",{"_394":1293,"_1294":178,"_1295":26,"_1296":26,"_1297":1298},"consentConfig",{"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395},"showContactAdmin",1784336640820,[],[],[370,371,372,373,374],{"_40":55,"_42":65,"_44":194,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":194,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":194,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":194,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":375,"_50":51},4,"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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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.",[667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687],[632,633,634,635,636],{"_435":527,"_90":527,"_89":258,"_408":658},{"_435":493,"_90":493,"_89":258,"_408":653},{"_435":646,"_90":646,"_89":258,"_408":647},{"_435":642,"_90":642,"_89":258,"_408":643},{"_435":637,"_90":637,"_89":258,"_408":638},"nvcrm.my.site.com",[639],{"_284":640,"_441":637,"_442":26,"_89":641},"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",[644],{"_284":645,"_441":642,"_442":26,"_89":476},"edge","github.com",[648,649],{"_284":652,"_441":646,"_442":26,"_89":651},{"_284":650,"_441":646,"_442":26,"_89":651},"logged_in","This domain is owned by Github. 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