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VoiceScribe/voicescribe-parakeet

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Model Card

Parakeet TDT 1.1B V3 - OpenVINO

![Discord](https://discord.gg/WNsvaCtmDe) ![GitHub Repo stars](https://github.com/FluidInference/eddy)

OpenVINO-optimized version of NVIDIA's Parakeet TDT 1.1B V3 model for high-performance multilingual automatic speech recognition on Intel NPUs and CPUs.

Benchmark Results

Hardware: Intel Core Ultra 7 155H (Meteor Lake) with Intel AI Boost NPU Software: OpenVINO 2025.x

LibriSpeech test-clean (English)

Metric	Value
Average WER	3.7%
Median WER	0.0%
Average CER	1.9%
RTFx (NPU)	25.7×
RTFx (CPU)	5-8×
Files processed	2,620 (5.4 hours)

FLEURS Multilingual (24 Languages)

Metric	Value
Average WER	17.0%
Average CER	5.4%
Average RTFx	41.1×
Total samples	~15,000+

Best performing languages (WER): Italian 4.3%, Spanish 5.4%, English 6.1%, German 7.4%, French 7.7%

See BENCHMARK_RESULTS.md for complete per-language results.

Performance Comparison

Implementation	Device	RTFx (Avg)	WER (LibriSpeech)
eddy (OpenVINO)	Intel Core Ultra 7 155H NPU	25.7×	3.7%
Parakeet (PyTorch)	Intel Arc 140V GPU	~20×*	~2.5%*
eddy (OpenVINO)	Intel Core Ultra 7 155H CPU	5-8×	3.7%

Note: Benchmarked on HP EliteBook Ultra G1i. eddy NPU is ~1.3× faster than PyTorch on Intel Arc GPU, with lower power consumption. *V3 estimated from V2 benchmark.

Supported Languages

24 European languages: English, Spanish, Italian, French, German, Dutch, Russian, Polish, Ukrainian, Slovak, Bulgarian, Finnish, Romanian, Croatian, Czech, Swedish, Estonian, Hungarian, Lithuanian, Danish, Maltese, Slovenian, Latvian, Greek

Usage

Python usage via ctypes available - see eddy repository for details.

Model Details

—Parameters: 1.1B
—Architecture: FastConformer-RNNT (4-model pipeline)
—Languages: 24 European languages
—Blank token ID: 8192
—Context window: 10s chunks with 3s overlap
—Features: LSTM state continuity, token deduplication, per-token timestamps

License

CC-BY-4.0 - See LICENSE for details.

Acknowledgments

Based on NVIDIA's Parakeet TDT model. OpenVINO conversion and optimization by the FluidInference team.