parakeet realtime eou 120m coreml EU AI Act Compliance Profile

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 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

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: ┌──────────

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Library

Base Model