Speech Recognition: Conformer

Model Overview

Conformer-CTC (around 120M parameters) is trained on ASRSet with over 3500 hours of German (de-DE) speech. The model transcribes speech in lower case German alphabet along with spaces and apostrophes.

Model Architecture

Conformer-CTC model is a non-autoregressive variant of Conformer model for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. For more information, refer to the Conformer-CTC Model documentation.

Intended Use

Primary use case intended for these models is automatic speech recognition.

Input

Single-channel audio files (WAV) with a 16kHz sample rate

Output

Transcripts, which are sequences of valid vocabulary labels as given by the specification file

How to Use this Model

Conformer is an end-to-end architecture that is trained using CTC loss. These model checkpoints are intended to be used with NVIDIA Riva.

The models are encrypted with the key tlt_encode.

Training

The model was trained on various proprietary and open-source datasets. These datasets include domain specific data for various domains, spontaneous speech and dialog, all of which contribute to the model’s accuracy.

This model delivers WER that is better than or comparable to popular alternate Speech to Text solutions for a range of domains and use cases.

Limitations

Currently, Conformer models only support training and inference on .wav audio files. All models included here were trained and evaluated on audio files with a sample rate of 16kHz, so for best performance you may need to upsample or downsample audio files to 16kHz.

In addition, the model will perform best on audio samples that are longer than 0.1 seconds long. For training and fine-tuning Conformer models, it is recommended that samples are capped at a maximum length of around 15 seconds, depending on the amount of memory available to you. You do not need to place a maximum length limitation for evaluation.

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva license.

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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The model transcribes speech in lower case German alphabet along with spaces and apostrophes.\n\n### Model Architecture\n\nConformer-CTC model is a non-autoregressive variant of Conformer model for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. For more information, refer to the [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html) documentation.\n\n### Intended Use\n\nPrimary use case intended for these models is automatic speech recognition.\n\n### Input\n\nSingle-channel audio files (WAV) with a 16kHz sample rate\n\n### Output\n\nTranscripts, which are sequences of valid vocabulary labels as given by the specification file\n\n### How to Use this Model\n\nConformer is an end-to-end architecture that is trained using CTC loss. These model checkpoints are intended to be used with NVIDIA Riva.\n\nThe models are encrypted with the key `tlt_encode`.\n\n### Training\n\nThe model was trained on various proprietary and open-source datasets. These datasets include domain specific data for various domains, spontaneous speech and dialog, all of which contribute to the model’s accuracy.\n\nThis model delivers WER that is better than or comparable to popular alternate Speech to Text solutions for a range of domains and use cases.\n\n### Limitations\n\nCurrently, Conformer models only support training and inference on .wav audio files. All models included here were trained and evaluated on audio files with a sample rate of 16kHz, so for best performance you may need to upsample or downsample audio files to 16kHz.\n\nIn addition, the model will perform best on audio samples that are longer than 0.1 seconds long. For training and fine-tuning Conformer models, it is recommended that samples are capped at a maximum length of around 15 seconds, depending on the amount of memory available to you. You do not need to place a maximum length limitation for evaluation.\n\n### Suggested Reading\n\nRefer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n### License\n\nBy downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva [license](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n### Ethical AI\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","RIVA Conformer ASR German",["D",1717170667765],"deployable_v2.1_export_v2","/api/image/nvidia/riva/models/speechtotext_de_de_conformer","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",363815323,"bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Age, Gender, Linguistic Background\nMeasures taken to mitigate against unwanted bias: | Used custom dataset to validate model performance across gender, age, and linguistic demographics","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications & Domains: | Speech Transcription\nTypes: | Speech Transcription\nIntended Users: | Data Scientists in Contact Center Transcription, Video Conferencing Transcription, Virtual Assistants, etc\nOutput: | Transcribed text with timestamps and confidence scores\nDescribe how the model works: | Model transcribes audio input into text for the input language\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Gender, National Origin\nTechnical Limitations: | Transcripts may not be 100% accurate. Accuracy varies based on the characteristics of input audio (Domain, Use Case, Accent, Noise, Speech Type, Context of speech, etc.)\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Word Error Rate (WER), Silence Robustness (Characters/mins of silent audio), Latency (in milliseconds), Throughput (Total audio processed per unit of time)\nPotential Known Risks: | Not recommended for word-for-word transcription as accuracy varies based on the characteristics of input audio (domain, use case, accent, noise, speech type, and context of speech)\nLicensing: | https://developer.nvidia.com/riva/ga/license","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any PII used? | Yes\nProtected class data used to create this model? | Age, Gender, Linguistic Background, National Origin\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | No\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | PII not collected by NVIDIA for development of model\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Yes\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | The data is compliant where applicable, but is not applicable for all data.","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Speech Transcription\nDescribe the life-critical impacts (if present). | Not Applicable for Licensed Use Cases per [NVIDIA RIVA License Agreement](https://developer.nvidia.com/riva/ga/license)\nUse Case Restriction(s): | Abide by https://developer.nvidia.com/riva/ga/license\nModel and Dataset Restriction(s): | Dataset access restrictions\nDescribe access restrictions (if any): | The Principle of Least Privilege (PoLP) is applied limiting access for dataset generation and model development. 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