parakeet-rnnt

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parakeet-rnnt-0.6b is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by NVIDIA NeMo and Suno.ai teams. It is an XL version of FastConformer Transducer [1] (around 600M parameters) model. See the model architecture section and NeMo documentation for complete architecture details.

Licence/Terms of Use

License to use this model is covered by the CC-BY-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4.0 license.

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For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at developer.nvidia.com. Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.

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What is Nemotron? NVIDIA Developer Nemotron NVIDIA Riva Speech NeMo Documentation

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

You can also run Parakeet RNNT with Transformers 🤗 (more below).

1) NeMo usage

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.

pip install nemo_toolkit['all']

Automatically instantiate the model

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

Transcribing using Python

First, let's get a sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

Then simply do:

output = asr_model.transcribe(['2086-149220-0033.wav'])
print(output[0].text)

Transcribing many audio files

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
 pretrained_name="nvidia/parakeet-rnnt-0.6b"
 audio_dir=""

2) Transformers 🤗 usage

Parakeet RNNT is available in 🤗 Transformers starting from v5.13.0.

pip install "transformers>=5.13.0"

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-rnnt-0.6b")
out = pipe("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")
print(out)

from transformers import AutoModelForRNNT, AutoProcessor
from datasets import load_dataset, Audio

num_samples = 3

model_id = "nvidia/parakeet-rnnt-0.6b"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForRNNT.from_pretrained(model_id, dtype="auto", device_map="auto")

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el["array"] for el in ds["audio"][:num_samples]]

inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
inputs.to(model.device, dtype=model.dtype)
output = model.generate(**inputs, return_dict_in_generate=True)
print(processor.decode(output.sequences, skip_special_tokens=True))

from datasets import Audio, load_dataset
from transformers import AutoModelForRNNT, AutoProcessor

num_samples = 3

model_id = "nvidia/parakeet-rnnt-0.6b"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForRNNT.from_pretrained(model_id, dtype="auto", device_map="auto")

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el["array"] for el in ds["audio"][:num_samples]]

inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
inputs.to(model.device, dtype=model.dtype)
output = model.generate(**inputs, return_dict_in_generate=True)
decoded_output, decoded_timestamps = processor.decode(
    output.sequences,
    durations=output.durations,
    skip_special_tokens=True,
)
print("Transcription:", decoded_output)
print("Timestamped tokens:", decoded_timestamps)

from transformers import AutoModelForRNNT, AutoProcessor
from datasets import load_dataset, Audio
import torch

model_id = "nvidia/parakeet-rnnt-0.6b"
NUM_SAMPLES = 4

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForRNNT.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
model.train()

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el["array"] for el in ds["audio"][:NUM_SAMPLES]]
text_samples = ds["text"][:NUM_SAMPLES]

# passing `text` to the processor will prepare inputs' `labels` key
inputs = processor(audio=speech_samples, text=text_samples, sampling_rate=processor.feature_