🧃 Parakeet Unified EN 0.6B - CoreML
A CoreML-optimized Parakeet Unified 0.6B English ASR model for Apple platforms. A single FastConformer-RNNT checkpoint serves both offline batch transcription and low-latency streaming, with punctuation and capitalization. The encoder is multi-context trained, so each streaming latency tier is the same weights re-exported with a different chunked-attention mask.
This model is consumed by the FluidAudio Swift SDK.
Model Description
- Architecture: FastConformer encoder + RNNT (Transducer) decoder/joint
- Parameters: ~0.6B
- Language: English
- Sample rate: 16 kHz mono
- Vocabulary: 1024 SentencePiece tokens (blank index 1024)
- Subsampling factor: 8 (one encoder frame ≈ 80 ms)
- Precision: FLOAT16; an INT8 encoder variant is included for higher throughput
- Punctuation & capitalization: yes
The same checkpoint exposes two paths:
- Offline batch — full-attention 15 s windows (240,000 samples), long files transcribed with overlapping windows merged on a 2 s overlap.
- Streaming — a
[left, chunk, right]chunked-attention window baked into the encoder at conversion time. Latency = (chunk + right) × 80 ms.
Streaming latency tiers
| Variant | [L,C,R] | Latency | Aggregate WER* | RTFx | Notes |
|---|---|---|---|---|---|
parakeet-unified-320ms | 70, 2, 2 | 0.32 s | 2.37% | 10x | lowest latency |
parakeet-unified-640ms | 70, 7, 1 | 0.64 s | 2.40% | 27x | efficiency — same WER as 320 ms, big chunk re-encodes less often |
parakeet-unified-1120ms | 70, 7, 7 | 1.12 s | 2.25% | 33x | best streaming WER |
parakeet-unified-2080ms (default) | 70, 13, 13 | 2.08 s | 2.47% | 54x | default |
*Aggregate WER on a 150-file LibriSpeech test-clean sweep (identical files across tiers, so the
relative ordering is what matters). Rules of thumb: look-ahead (right context) drives WER, chunk
size drives RTFx, and look-ahead saturates around right ≈ 2 frames.
Benchmarks
LibriSpeech test-clean, all 2620 files, INT8 encoder, scored with FluidAudio's TextNormalizer:
| Mode | Avg WER | Aggregate WER | Median RTFx | Overall RTFx |
|---|---|---|---|---|
| batch | 2.16% | 1.68% | 130.0x | 143.6x |
| streaming (2080 ms) | 2.21% | 1.79% | 66.2x | 65.9x |
vs Parakeet TDT v3 (same harness)
| Model | Mode | Avg WER | Overall RTFx | Punctuation/caps | Languages |
|---|---|---|---|---|---|
| Parakeet TDT v3 | batch (sliding window) | 2.6% | 110 | no | 25 + Japanese |
| Parakeet Unified | batch | 2.16% | 144 | yes | English |
| Parakeet Unified | streaming | 2.21% | 66 | yes | English |
For English transcription, Unified batch beats TDT v3 on both WER and throughput and adds punctuation/capitalization. TDT v3 remains the choice for non-English audio.
Usage in Swift
import FluidAudio
// Offline batch transcription
let manager = try await UnifiedAsrManager.fromHub()
let result = try await manager.transcribe(samples) // 16 kHz mono Float
print(result.text)
// Streaming (choose a latency tier)
let streaming = try await StreamingUnifiedAsrManager.fromHub(variant: .parakeetUnified2080ms)
The CLI selects a tier with --parakeet-variant, e.g.
parakeet-unified-320ms, parakeet-unified-1120ms, parakeet-unified-2080ms (default), or
parakeet-unified-offline-15s. See the
FluidAudio repository for full instructions.
Repository contents
CoreML pipeline (.mlmodelc), driven from metadata.json / vocab.json:
| Stage | Bundle |
|---|---|
| Preprocessor | parakeet_unified_preprocessor.mlmodelc |
| Mel encoder (offline) | parakeet_unified_mel_encoder.mlmodelc |
| Mel encoder (streaming) | parakeet_unified_mel_encoder_streaming_70_13_13.mlmodelc |
| Encoder (offline) | parakeet_unified_encoder.mlmodelc, _int8 |
| Encoder (streaming) | parakeet_unified_encoder_streaming_{70_2_2,70_7_1,70_7_7,70_13_13}.mlmodelc (+ _int8) |
| Decoder | parakeet_unified_decoder.mlmodelc |
| Joint | parakeet_unified_joint.mlmodelc |
| Joint decision (single step) | parakeet_unified_joint_decision_single_step.mlmodelc |
metadata.json holds the runtime shapes (mel/encoder/decoder/state) and streaming window geometry;
vocab.json is the 1024-token SentencePiece vocabulary.
License
Released under the CC-BY-4.0 license.
Acknowledgments
Based on NVIDIA's Parakeet (FastConformer-RNNT) model. CoreML conversion and Swift integration by the FluidInference team.