README.md

Automatic Speech Recognition (ASR)

Key Features

• HuggingFace Space for Audio Transcription (File, Microphone and YouTube)
• Pretrained models available in 14+ languages
• Automatic Speech Recognition (ASR)
  • Supported ASR models:
    • Jasper, QuartzNet, CitriNet, ContextNet
    • Conformer-CTC, Conformer-Transducer, FastConformer-CTC, FastConformer-Transducer
    • Squeezeformer-CTC and Squeezeformer-Transducer
    • LSTM-Transducer (RNNT) and LSTM-CTC
  • Supports the following decoders/losses:
    • CTC
    • Transducer/RNNT
    • Hybrid Transducer/CTC
    • NeMo Original Multi-blank Transducers and Token-and-Duration Transducers (TDT)
  • Streaming/Buffered ASR (CTC/Transducer) - Chunked Inference Examples
  • Cache-aware Streaming Conformer with multiple lookaheads (including microphone streaming tutorial.
  • Beam Search decoding
  • Language Modelling for ASR (CTC and RNNT): N-gram LM in fusion with Beam Search decoding, Neural Rescoring with Transformer
  • Support of long audios for Conformer with memory efficient local attention
• Speech Classification, Speech Command Recognition and Language Identification: MatchboxNet (Command Recognition), AmberNet (LangID)
• Voice activity Detection (VAD): MarbleNet
  • ASR with VAD Inference - Example
• Speaker Recognition: TitaNet, ECAPA_TDNN, SpeakerNet
• Speaker Diarization
  • Clustering Diarizer: TitaNet, ECAPA_TDNN, SpeakerNet
  • Neural Diarizer: MSDD (Multi-scale Diarization Decoder)
• Speech Intent Detection and Slot Filling: Conformer-Transformer

You can also get a high-level overview of NeMo ASR by watching the talk NVIDIA NeMo: Toolkit for Conversational AI, presented at PyData Yerevan 2022: