Speech Recognition
Speech Recognition#
- How do I use Riva ASR APIs with out-of-the-box models?
- Creating Grammars for Speech Hints
- How to Customize Riva ASR Vocabulary and Pronunciation with Lexicon Mapping
- How to Deploy a Custom Language Model (n-gram) Trained with NeMo on Riva
- How to Deploy a Custom Acoustic Model (Citrinet) Trained with NeMo on Riva
- How to Deploy a Custom Acoustic Model (Conformer-CTC) Trained with NeMo on Riva
- How to Deploy a Conformer-CTC Acoustic Model with WFST Decoders
- How to Customize a Riva ASR Acoustic Model (Conformer-CTC) with Adapters
- ASR with Adapters
- What are Adapters?
- Advantages and Limitations of Adapter Training
- Preparing the Acoustic Encoder for Adapter Training
- Preparing the Model and Dataset for Adaptation
- Creating and Training an Adapter
- Evaluating the Model
- Export the Model to Riva
- What’s Next?
- How to Fine-Tune a Riva ASR Acoustic Model with NVIDIA NeMo
- How to Improve Recognition of Specific Words
- Conclusion
- How to Synthesize a Noisy Dataset that can be used to Train a Noise Robust ASR Model
- How to Improve the Accuracy on Noisy Speech by Fine-Tuning the Acoustic Model (Conformer-CTC) in the Riva ASR Pipeline
- How To Train, Evaluate, and Fine-Tune an n-gram Language Model
- How do I Use Speaker Diarization with Riva ASR?
- Requirements and Setup
- How do I boost specific words at runtime with word boosting?
- Support for Class Based n-gram Language Models in Riva (WFST Decoder)
- WFST Decoding