Parakeet Realtime Eou 120m Coreml

--- license: other license_name: nvidia-open-model-license license_link: >- https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ language: - en metrics: - wer library_name: nemo tags: - speech-recognition - FastConformer - end-of-utterance - voice agent pipeline_tag: automatic-speech-recognition base_model: - nvidia/parakeet_realtime_eou_120m-v1 base_model_relation: finetune --- # Parakeet Realtime EOU 120M — CoreML CoreML conversion of [nvidia/parakeet-realtime-eou-120m-v1](https://huggingface.co/nvidia/parakee t-realtime-eou-120m-v1) for streaming speech recognition with end-of-utterance detection on Apple Silicon. Used by [FluidAudio](https://github.com/FluidInference/FluidAudio) for real-time transcription. ## Models The RNNT pipeline is split into three CoreML models, exported at two chunk sizes: | Model | Description | |-------|-------------| | `streaming_encoder.mlmodelc` | FastConformer encoder with loopback state caching | | `decoder.mlmodelc` | 1-layer LSTM decoder (640 hidden units) | | `joint_decision.mlmodelc` | Joint network for token prediction + EOU detection | ### Chunk Size Variants | Variant | Latency | WER (test-clean) | RTFx (M2) | |---------|---------|-------------------|------------| | `160ms/` | 160ms | 8.29% | 4.78x | | `320ms/` | 320ms | 4.87% | 12.48x | Benchmarked on LibriSpeech test-clean (2620 files, 5.40h audio) on Apple M2. ## Usage with FluidAudio ```swift import FluidAudio let manager = StreamingEouAsrManager() await manager.initialize() // Transcribe with EOU detection await manager.startStreaming( eouCallback: { transcript in print("Utterance complete: \(transcript)") }, partialCallback: { partial in print("Partial: \(partial)") } ) // Feed audio chunks as they arrive await manager.feedAudio(samples) ``` CLI # Transcribe a file swift run fluidaudio parakeet-eou --input audio.wav # Benchmark swift run -c release fluidaudio parakeet-eou --benchmark --chunk-size 320 Architecture 120M parameter RNNT (Recurrent Neural Network Transducer) with: - Encoder: 17-layer FastConformer with cache-aware streaming - Decoder: 1-layer LSTM, 640 hidden size - Joint: Linear projection with 1027 output classes (1024 tokens + EOU token + SOS + blank) - EOU token: ID 1024 signals end-of-utterance Streaming State The encoder maintains loopback state between chunks: ┌─────────────────────┬──────────────────┬───────────────────────┐ │ State │ Shape │ Description │ ├─────────────────────┼──────────────────┼───────────────────────┤ │ preCache │ [1, 128, N] │ Mel-level context │ ├─────────────────────┼──────────────────┼───────────────────────┤ │ cacheLastChannel │ [17, 1, 70, 512] │ Conformer layer cache │ ├─────────────────────┼──────────────────┼───────────────────────┤ │ cacheLastTime │ [17, 1, 512, 8] │ Temporal cache │ ├─────────────────────┼──────────────────┼───────────────────────┤ │ cacheLastChannelLen │ [1] │ Cache length tracking │ └─────────────────────┴──────────────────┴───────────────────────┘ Export Converted from PyTorch using coremltools. To re-export: python3 Scripts/ParakeetEOU/Conversion/convert_split_encoder.py \ --output-dir Models/ParakeetEOU \ --model-id nvidia/parakeet-realtime-eou-120m-v1 License NVIDIA Open Model License — see https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/. Original model: https://huggingface.co/nvidia/parakeet-realtime-eou-120m-v1 ---