nexaml/parakeet-tdt-0.6b-v2-MLX

Quickstart

Run them directly with nexa-sdk installed In nexa-sdk CLI:

nexaml/parakeet-tdt-0.6b-v2-MLX

Overview

parakeet-tdt-0.6b-v2 is a 600-million-parameter automatic speech recognition (ASR) model designed for high-quality English transcription, featuring support for punctuation, capitalization, and accurate timestamp prediction. Try Demo here: https://huggingface.co/spaces/nvidia/parakeet-tdt-0.6b-v2

This XL variant of the FastConformer architecture integrates the TDT decoder and is trained with full attention, enabling efficient transcription of audio segments up to 24 minutes in a single pass. The model achieves an RTFx of 3380 on the HF-Open-ASR leaderboard with a batch size of 128. Note: RTFx Performance may vary depending on dataset audio duration and batch size.

Key Features

• Accurate word-level timestamp predictions
• Automatic punctuation and capitalization
• Robust performance on spoken numbers, and song lyrics transcription

For more information, refer to the Model Architecture section and the NeMo documentation.

This model is ready for commercial/non-commercial use.

Benchmark Results

Huggingface Open-ASR-Leaderboard Performance

The performance of Automatic Speech Recognition (ASR) models is measured using Word Error Rate (WER). Given that this model is trained on a large and diverse dataset spanning multiple domains, it is generally more robust and accurate across various types of audio.

Base Performance

The table below summarizes the WER (%) using a Transducer decoder with greedy decoding (without an external language model):

Model	Avg WER	AMI	Earnings-22	GigaSpeech	LS test-clean	LS test-other	SPGI Speech	TEDLIUM-v3	VoxPopuli
parakeet-tdt-0.6b-v2	6.05	11.16	11.15	9.74	1.69	3.19	2.17	3.38	5.95

Noise Robustness

Performance across different Signal-to-Noise Ratios (SNR) using MUSAN music and noise samples:

SNR Level	Avg WER	AMI	Earnings	GigaSpeech	LS test-clean	LS test-other	SPGI	Tedlium	VoxPopuli	Relative Change
Clean	6.05	11.16	11.15	9.74	1.69	3.19	2.17	3.38	5.95	-
SNR 50	6.04	11.11	11.12	9.74	1.70	3.18	2.18	3.34	5.98	+0.25%
SNR 25	6.50	12.76	11.50	9.98	1.78	3.63	2.54	3.46	6.34	-7.04%
SNR 5	8.39	19.33	13.83	11.28	2.36	5.50	3.91	3.91	6.96	-38.11%

Telephony Audio Performance

Performance comparison between standard 16kHz audio and telephony-style audio (using μ-law encoding with 16kHz→8kHz→16kHz conversion):

Audio Format	Avg WER	AMI	Earnings	GigaSpeech	LS test-clean	LS test-other	SPGI	Tedlium	VoxPopuli	Relative Change
Standard 16kHz	6.05	11.16	11.15	9.74	1.69	3.19	2.17	3.38	5.95	-
μ-law 8kHz	6.32	11.98	11.16	10.02	1.78	3.52	2.20	3.38	6.52	-4.10%

These WER scores were obtained using greedy decoding without an external language model. Additional evaluation details are available on the Hugging Face ASR Leaderboard.

Reference

• Original model card: nvidia/parakeet-tdt-0.6b-v2
• Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
• Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
• NVIDIA NeMo Toolkit
• Youtube-commons: A massive open corpus for conversational and multimodal data
• Yodas: Youtube-oriented dataset for audio and speech
• HuggingFace ASR Leaderboard
• MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
• Granary: Speech Recognition and Translation Dataset in 25 European Languages