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Model
Base Multilingual Code Switch Mandarin-English 4-gram LM
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| Field | Response |
|---|---|
| Intended Applications & Domains: | Automatic Speech Recognition |
| Model Type: | Language Model |
| Intended Users: | This model is intended for developers and data scientists building interactive call centers, virtual assistants, and language learning assistants |
| Output: | Likelihood of word sequence |
| Describe how the model works: | Automatic Speech Recognition (ASR) engine transcribes speech based on probability of the next word |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | None |
| Technical Limitations: | Transcripts may be not 100% accurate. Accuracy varies based on language and characteristics of input audio (Domain, Use Case, Accent, Noise, Speech Type, Context of speech, etc.) |
| Verified to have met prescribed NVIDIA quality standards: | Yes |
| Performance Metrics: | Word Error Rate (WER) on the ASR transcription conditioned on n-gram language models |
| Potential Known Risks: | If a word is not trained in the language model and not presented in vocabulary, the word is not likely to be recognized. Not recommended for word-for-word/incomplete sentences as accuracy varies based on the context of input text |
| Recommended Training: | https://github.com/nvidia-riva/tutorials/blob/main/asr-python-advanced-nemo-ngram-training-and-finetuning.ipynb |
| Licensing: | https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/ |