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Model Overview

This model is based on the QuartzNet architecture [1]. The pre-trained models here can be used immediately for fine-tuning or dataset evaluation.

It utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the German portion of Common Voice from Mozilla (MCV) [2].

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 fine-tuned from English language to German. We took an encoder from the English version of QuartzNet network trained on ~3,000 hours of public English data. Then we changed the model's decoder to output characters from German alphabet and fine-tuned this model using German portion of Common Voice from Mozilla (MCV) [2]. We trained it on non-dev and non-test validated clips from Mozilla Common Voice version 6.0.

See details of the training procedure here [3].

Datasets

While training this model, we used the following datasets:

Librispeech 960 hours of English speech (pre-training)
Fisher Corpus (pre-training)
Switchboard-1 Dataset (pre-training)
WSJ-0 and WSJ-1 (pre-training)
Mozilla Common Voice (German) (fine-tuning)

Performance

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

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

11.8 % on the dev set from German MCV dataset.

Note that these scores on this evaluation sets 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_de_quartznet15x5\")\n

Transcribing text with this model

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

This model was trained on relatively small amount of speech data. It's performance will vary greatly based on your application and audio. We recommend using it as a starting point for fine-tuning your own models.

References

[1] Kriman, Samuel, et al. \"Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions.\" ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.

[2] Mozilla Common Voice

[3] Huang, Jocelyn, et al. \"Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition.\" arXiv preprint arXiv:2005.04290 (2020).

[4] 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 pre-trained models here can be used immediately for fine-tuning or dataset evaluation.\n\nIt utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the German portion of Common Voice from Mozilla (MCV) [2].\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 fine-tuned from English language to German. We took an encoder from the English version of QuartzNet network trained on ~3,000 hours of public English data. Then we changed the model's decoder to output characters from German alphabet and fine-tuned this model using German portion of Common Voice from Mozilla (MCV) [2]. We trained it on non-dev and non-test validated clips from Mozilla Common Voice version 6.0.\n\nSee details of the training procedure here [3].\n\n### Datasets\n\nWhile training this model, we used the following datasets:\n\n- Librispeech 960 hours of English speech (pre-training)\n- Fisher Corpus (pre-training)\n- Switchboard-1 Dataset (pre-training)\n- WSJ-0 and WSJ-1 (pre-training)\n- Mozilla Common Voice (German) (fine-tuning)\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate.\n\nThe model obtains the following scores on the following evaluation datasets -\n\n- 11.8 % on the dev set from German MCV dataset.\n\nNote that these scores on this evaluation sets 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_de_quartznet15x5\")\n```\n### Transcribing text with this model\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_de_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\nThis model was trained on relatively small amount of speech data. It's performance will vary greatly based on your application and audio. We recommend using it as a starting point for fine-tuning your own models.\n\n## References\n[1] [Kriman, Samuel, et al. \"Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions.\" ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.](https://arxiv.org/abs/1910.10261)\n\n[2] [Mozilla Common Voice](https://commonvoice.mozilla.org/en)\n\n[3] [Huang, Jocelyn, et al. \"Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition.\" arXiv preprint arXiv:2005.04290 (2020).](https://arxiv.org/abs/2005.04290)\n\n[4] [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. 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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.",[561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585],[532,533,534,535,536],{"_371":446,"_204":446,"_165":166,"_344":556},{"_371":420,"_204":420,"_165":166,"_344":551},{"_371":546,"_204":546,"_165":166,"_344":547},{"_371":541,"_204":541,"_165":166,"_344":542},{"_371":537,"_204":537,"_165":166,"_344":538},"nvidia.partners",[539],{"_77":540,"_377":537,"_378":31,"_165":414},"edge","app.hushly.com",[543],{"_77":544,"_377":541,"_378":31,"_165":545},"*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",[548],{"_77":549,"_377":546,"_378":31,"_165":550},"_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.",[644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672],[624,625,626,627],{"_371":639,"_204":639,"_165":166,"_344":640},{"_371":634,"_204":634,"_165":166,"_344":635},{"_371":433,"_204":433,"_165":166,"_344":631},{"_371":546,"_204":546,"_165":166,"_344":628},[629],{"_77":630,"_377":546,"_378":85,"_165":550},"_gh_sess",[632],{"_77":633,"_377":433,"_378":85,"_165":440},"YSC","bizible.com",[636],{"_77":637,"_377":634,"_378":31,"_165":638},"_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",[641],{"_77":642,"_377":639,"_378":31,"_165":643},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":714,"_377":492,"_378":31,"_165":715},{"_77":699,"_377":713,"_378":85,"_165":700},{"_77":682,"_377":495,"_378":31,"_165":607},{"_77":682,"_377":595,"_378":31,"_165":607},{"_77":711,"_377":492,"_378":31,"_165":712},{"_77":699,"_377":498,"_378":85,"_165":700},{"_77":709,"_377":492,"_378":31,"_165":710},{"_77":699,"_377":495,"_378":85,"_165":700},{"_77":707,"_377":594,"_378":31,"_165":708},{"_77":699,"_377":616,"_378":85,"_165":700},{"_77":679,"_377":495,"_378":31,"_165":681},{"_77":705,"_377":492,"_378":85,"_165":706},{"_77":703,"_377":492,"_378":31,"_165":704},{"_77":701,"_377":492,"_378":85,"_165":702},{"_77":699,"_377":590,"_378":85,"_165":700},{"_77":697,"_377":492,"_378":31,"_165":698},{"_77":695,"_377":492,"_378":31,"_165":696},{"_77":693,"_377":492,"_378":85,"_165":694},{"_77":691,"_377":599,"_378":31,"_165":692},{"_77":679,"_377":590,"_378":31,"_165":681},{"_77":689,"_377":492,"_378":31,"_165":690},{"_77":687,"_377":492,"_378":85,"_165":688},{"_77":685,"_377":492,"_378":31,"_165":686},{"_77":683,"_377":492,"_378":31,"_165":684},{"_77":682,"_377":492,"_378":31,"_165":607},{"_77":679,"_377":680,"_378":31,"_165":681},{"_77":677,"_377":492,"_378":31,"_165":678},{"_77":675,"_377":492,"_378":85,"_165":676},{"_77":673,"_377":492,"_378":31,"_165":674},"_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. 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