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Model Overview

Tacotron2 is an encoder-attention-decoder. The encoder is made of three parts in sequence: 1) a word embedding, 2) a convolutional network, and 3) a bi-directional LSTM. The encoded represented is connected to the decoder via a Location Sensitive Attention module. The decoder is comprised of a 2 layer LSTM network, a convolutional postnet, and a fully connected prenet.

During training, the ground frame is fed through the prenet and passed as input to the decoder LSTM layers. During inference, the model's predictions at the previous time step is used. In addition, an attention context is computed by the attention layer at each step and concatenated with the prenet output. The output of the LSTM network concatenated with the attention is sent through two projection layers. The first projects the information to a spectrogram while the other projects it to a stop token. The spectrogram is then sent through the convolutional postnet to compute a residual to add to the generated spectrogram.

Trained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models and then deployed. Here is a pre-trained Tacotron2 Speech Synthesis Riva model. Note that the Tacotron2 model at that link is not contained in a .riva file. Rather, it is used directly in the Riva build phase as a .nemo file.

Training

This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.

Performance

No performance information available at this time.

How to Use this Model

You can download NeMo docker container that includes NeMo release 1.10.0, and run inference on that docker container,

$ docker pull nvcr.io/nvidia/nemo:22.05\n$ docker run --gpus all -it --rm --shm-size=8g -p 8080:8080 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:22.05 bash\n

This model can be automatically loaded from NGC.

NOTE: In order to generate audio, you also need a 22050Hz vocoder from NeMo. This example uses the HiFi-GAN model.

# Load Tacotron2\nfrom nemo.collections.tts.models import Tacotron2Model\nspec_generator = Tacotron2Model.from_pretrained(\"tts_en_tacotron2\")\n\n# Load vocoder\nfrom nemo.collections.tts.models import HifiGanModel\nvocoder = HifiGanModel.from_pretrained(model_name=\"tts_hifigan\")\n\n# Generate audio\nimport soundfile as sf\nimport torch\nwith torch.no_grad():\n\tparsed = spec_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\n\tspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\n\taudio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)\n\n# Save the audio to disk in a file called speech.wav\nif isinstance(audio, torch.Tensor):\n\taudio = audio.to('cpu').numpy()\nsf.write(\"speech.wav\", audio.T, 22050, format=\"WAV\")\n

Input

This model accepts batches of text.

Output

This model generates mel spectrograms.

Limitations

This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.

Versions

1.10.0 (current): Refactored Tacotron2 to use new TTSDataset class, and made minor bugfixes for file paths.

1.0.0: An updated version of tacotron2 that standardizes mel spectrogram generation across NeMo models.

1.0.0rc1: The original version that was released with NeMo 1.0.0.rc1

References

Tacotron2 paper: https://arxiv.org/abs/1712.05884\nLocation Sensitive Attention paper: https://arxiv.org/abs/1506.07503

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 encoder is made of three parts in sequence: 1) a word embedding, 2) a convolutional network, and 3) a bi-directional LSTM. The encoded represented is connected to the decoder via a Location Sensitive Attention module. The decoder is comprised of a 2 layer LSTM network, a convolutional postnet, and a fully connected prenet.\n\nDuring training, the ground frame is fed through the prenet and passed as input to the decoder LSTM layers. During inference, the model's predictions at the previous time step is used. In addition, an attention context is computed by the attention layer at each step and concatenated with the prenet output. The output of the LSTM network concatenated with the attention is sent through two projection layers. The first projects the information to a spectrogram while the other projects it to a stop token. The spectrogram is then sent through the convolutional postnet to compute a residual to add to the generated spectrogram.\n\nTrained or fine-tuned NeMo models (with the file extenstion `.nemo`) can be converted to Riva models and then deployed. [Here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/speechsynthesis_english_tacotron2) is a pre-trained Tacotron2 Speech Synthesis Riva model. Note that the Tacotron2 model at that link is not contained in a `.riva` file. Rather, it is used directly in the Riva build phase as a `.nemo` file.\n\n## Training\n\nThis model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.\n\n## Performance\nNo performance information available at this time.\n\n## How to Use this Model\n\nYou can download NeMo docker container that includes NeMo release 1.10.0, and run inference on that docker container,\n\n``` bash\n$ docker pull nvcr.io/nvidia/nemo:22.05\n$ docker run --gpus all -it --rm --shm-size=8g -p 8080:8080 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:22.05 bash\n```\n\nThis model can be automatically loaded from NGC.\n\nNOTE: In order to generate audio, you also need a 22050Hz vocoder from NeMo. This example uses the HiFi-GAN model.\n\n```python\n# Load Tacotron2\nfrom nemo.collections.tts.models import Tacotron2Model\nspec_generator = Tacotron2Model.from_pretrained(\"tts_en_tacotron2\")\n\n# Load vocoder\nfrom nemo.collections.tts.models import HifiGanModel\nvocoder = HifiGanModel.from_pretrained(model_name=\"tts_hifigan\")\n\n# Generate audio\nimport soundfile as sf\nimport torch\nwith torch.no_grad():\n\tparsed = spec_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\n\tspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\n\taudio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)\n\n# Save the audio to disk in a file called speech.wav\nif isinstance(audio, torch.Tensor):\n\taudio = audio.to('cpu').numpy()\nsf.write(\"speech.wav\", audio.T, 22050, format=\"WAV\")\n```\n\n### Input\nThis model accepts batches of text.\n\n### Output\nThis model generates mel spectrograms.\n\n## Limitations\nThis checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.\n\n## Versions\n1.10.0 (current): Refactored Tacotron2 to use new TTSDataset class, and made minor bugfixes for file paths.\n\n1.0.0: An updated version of tacotron2 that standardizes mel spectrogram generation across NeMo models.\n\n1.0.0rc1: The original version that was released with NeMo 1.0.0.rc1\n\n## References\nTacotron2 paper: https://arxiv.org/abs/1712.05884\nLocation Sensitive Attention paper: https://arxiv.org/abs/1506.07503\n\n## Licence\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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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. 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