parakeet-unified-en

Parakeet-unified-en-0.6b is an English automatic speech recognition (ASR) model based on transducer architecture (RNN-T) combining both offline and streaming inference (with a minimum latency of 160ms) in one model [1]. It is trained mostly on the English part of the Granary dataset [4], which contains approximately 250,000 hours of US English (en-US) speech across diverse acoustic conditions. The model transcribes speech to English alphabet, spaces, and apostrophes with punctuation and captalization support.

Average WER comparison on the HF ASR Leaderboard datasets including offline and streaming inference with different latency values.

Why Choose nvidia/parakeet-unified-en-0.6b?

• One model for both tasks: You need to utilize only one unified model for both offline and streaming inference with a minimum latency of 160ms.
• Better accuracy performance: The unified model achieves better accuracy performance on the HF ASR Leaderboard datasets compared to the previous transducer-based offline and streaming only models.
• Streaming chunk size flexibilty: Enables you to choose the optimal streaming latency (chunk + right context) from 2080ms to 160ms with step of 80ms.
• Punctuation & Capitalization: Built-in support for punctuation and capitalization in output text

This model consists of a 🦜 Parakeet (FastConformer) encoder (jointly trained in offline and streaming modes) with an RNN-T decoder. It is designed for offline and streaming speech-to-text applications where latency can be as low as 160ms, such as voice assistants, live captioning, and conversational AI systems. The current inference pipeline supports only buffered streaming (left context is recomputed for each chunk) that can be longer than cache-aware streaming.

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

License/Terms of Use:

Governing Terms: Use of the model is governed by the NVIDIA Open Model License Agreement.

Deployment Geography:

Global

Use Case:

This model is for transcription of English audio in offline and streaming modes.

Release Date:

• Hugging Face [04/07/2026] via https://huggingface.co/nvidia/parakeet-unified-en-0.6b

Model Architecture

Architecture Type: Unified-FastConformer-RNNT

The unified model architecture is presented in [1]. The model is based on the FastConformer encoder architecture [2] with 24 encoder layers and an RNNT (Recurrent Neural Network Transducer) decoder. The model was trained jointly in offline and streaming modes. In the offline mode we used standard offline training with full-context self-attention and non-causal convolutions. In the streaming mode we applied chunked self-attention masks (incluing left, middle/chunk and right context) together with Dynamic Chunked Convolutions inside each FastConformer layer [3] to adapt the model to both decoding scenarios. We also introduced a novel mode-consistency regularization loss to further reduce the gap between offline and streaming performance. All the model parameters are shared between offline and streaming modes (encoder, predictor, and joint networks), including initial x8 subsampling with non-causal convolutions.

Network Architecture:

• Encoder: Unified FastConformer with 24 layers
• Decoder: RNNT (Recurrent Neural Network Transducer)
• Parameters: 600M

NVIDIA NeMo

How to Use this Model

For now, we provide only inference support for the unified model. We will release the unified training pipeline soon.

Loading the Model

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/parakeet-unified-en-0.6b")

Offline Inference

output = asr_model.transcribe([wav_file_path])
print(output[0].text)

Streaming Inference

For streaming inference you can use statfull chunked RNN-T decoding script from NeMo - /NeMo/blob/main/examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py

cd NeMo
python examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py \
    model_path= \
    dataset_manifest= \
    output_filename= \
    left_context_secs= \   # left context in seconds, 5.6s by default
    chunk_secs= \                 # chunk size in seconds, 0.56s by default
    right_context_secs= \ # right context in seconds, 0.56s by default
    att_context_size_as_chunk=true \          # set to true to use chunked self-attention masks
    batch_size=

You can also run streaming inference through the pipeline method, which uses NeMo/examples/asr/conf/asr_streaming_inference/buffered_rnnt.yaml configuration file to build end‑to‑end workflows with punctuation and capitalization (PnC), inverse text normalization (ITN), and translation support.

from nemo.collections.asr.inference.factory.pipeline_builder import PipelineBuilder
from omegaconf import OmegaConf

# Path to the buffered rnnt config file downloaded from above link
cfg_path = 'buffered_rnnt.yaml'
cfg = OmegaConf.load(cfg_path)

# Pass the paths of all the audio files for inferencing
audios = ['/path/to/your/audio.wav']

# Create the pipeline object and run inference
pipeline = PipelineBuilder.build_pipeline(cfg)
output = pipeline.run(audios)

# Print the output
for entry in output:
  print(entry['text'])

Setting up Streaming Configuration

Latency is defined as the sum of the chunk size (middle part) and the right context. For the left context we use 5.6s by