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nvidia/
parakeet-tdt-0.6b-v3

Name: parakeet-tdt-0.6b-v3
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nemo

automatic-speech-recognition

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🦜 parakeet-tdt-0.6b-v3: Multilingual Speech-to-Text Model

Description:

parakeet-tdt-0.6b-v3 is a 600-million-parameter multilingual automatic speech recognition (ASR) model designed for high-throughput speech-to-text transcription. It extends the parakeet-tdt-0.6b-v2 model by expanding language support from English to 25 European languages. The model automatically detects the language of the audio and transcribes it without requiring additional prompting. It is part of a series of models that leverage the Granary [1, 2] multilingual corpus as their primary training dataset.

🗣️ Try Demo here: https://huggingface.co/spaces/nvidia/parakeet-tdt-0.6b-v3

Supported Languages:
Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hungarian (hu), Italian (it), Latvian (lv), Lithuanian (lt), Maltese (mt), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv), Russian (ru), Ukrainian (uk)

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

Key Features:

parakeet-tdt-0.6b-v3's key features are built on the foundation of its predecessor, parakeet-tdt-0.6b-v2, and include:

Automatic punctuation and capitalization
Accurate word-level and segment-level timestamps
Long audio transcription, supporting audio up to 24 minutes long with full attention (on A100 80GB) or up to 3 hours with local attention.
Released under a permissive CC BY 4.0 license

For full details on the model architecture, training methodology, datasets, and evaluation results, check out the Technical Report.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by the CC-BY-4.0 license.

Discover more from NVIDIA:

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.

Explore more from NVIDIA:

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

Automatic Speech Recognition (ASR) Performance

Figure 1: ASR WER comparison across different models. This does not include Punctuation and Capitalisation errors.

Evaluation Notes

Note 1: The above evaluations are conducted for 24 supported languages, excluding Latvian since seamless-m4t-v2-large and seamless-m4t-medium do not support it.

Note 2: Performance differences may be partly attributed to Portuguese variant differences - our training data uses European Portuguese while most benchmarks use Brazilian Portuguese.

Deployment Geography:

Global

Use Case:

This model serves developers, researchers, academics, and industries building applications that require speech-to-text capabilities, including but not limited to: conversational AI, voice assistants, transcription services, subtitle generation, and voice analytics platforms.

Release Date:

Huggingface 08/14/2025

Model Architecture:

Architecture Type:

FastConformer-TDT

Network Architecture:

This model was developed based on FastConformer encoder architecture[3] and TDT decoder[4]
This model has 600 million model parameters.

Input:

Input Type(s): 16kHz Audio Input Format(s): .wav and .flac audio formats Input Parameters: 1D (audio signal) Other Properties Related to Input: Monochannel audio

Output:

Output Type(s): Text Output Format: String Output Parameters: 1D (text) Other Properties Related to Output: Punctuations and Capitalizations included.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

For more information, refer to the NeMo documentation.

How to Use this Model:

There are several ways to use this model. Choose the one that fits your needs.

Run locally with NeMo-Speech.cpp

NeMo-Speech.cpp provides a lightweight native C++ runtime for local inference with this model. After installing the runtime:

bash

hf download nvidia/parakeet-tdt-0.6b-v3 \
  parakeet-tdt-0.6b-v3.q8_0.gguf \
  --local-dir models

nemo-speech transcribe audio.wav \
  --model models/parakeet-tdt-0.6b-v3.q8_0.gguf

See the NeMo-Speech.cpp documentation for more details.

NVIDIA NeMo

To train, fine-tune, or run Python inference with this model, install NVIDIA NeMo after installing a recent PyTorch version.

pip install -U nemo_toolkit['asr']

The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

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

Automatically instantiate the model

python

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/parakeet-tdt-0.6b-v3")

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:

python

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

Transcribing with timestamps

To transcribe with timestamps:

python

output = asr_model.transcribe(['2086-149220-0033.wav'], timestamps=True)
# by default, timestamps are enabled for char, word and segment level
word_timestamps = output[0].timestamp['word'] # word level timestamps for first sample
segment_timestamps = output[0].timestamp['segment'] # segment level timestamps
char_timestamps = output[0].timestamp['char'] # char level timestamps

for stamp in segment_timestamps:
    print(f"{stamp['start']}s - {stamp['end']}s : {stamp['segment']}")

Transcribing long-form audio

python

#updating self-attention model of fast-conformer encoder
#setting attention left and right context sizes to 256
asr_model.change_attention_model(self_attention_model="rel_pos_local_attn", att_context_size=[256, 256])

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

print(output[0].text)

Streaming with Parakeet models

To use parakeet models in streaming mode use this script as shown below:

bash

python NeMo/main/examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py \
    pretrained_name="nvidia/parakeet-tdt-0.6b-v3" \
    model_path=null \
    audio_dir="<optional path to folder of audio files>" \
    dataset_manifest="<optional path to manifest>" \
    output_filename="<optional output filename>" \
    right_context_secs=2.0 \
    chunk_secs=2 \
    left_context_secs=10.0 \
    batch_size=32 \
    clean_groundtruth_text=False

NVIDIA NIM for v2 parakeet model is available at https://build.nvidia.com/nvidia/parakeet-tdt-0_6b-v2.

Transformers 🤗 usage

Until Parakeet TDT is part of an official Transformers release, you can use it by installing from source.

pip install git+https://github.com/huggingface/transformers

➡️ Pipeline usage

python

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-tdt-0.6b-v3")
out = pipe("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")
print(out)

➡️ AutoModel

python

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

device = "cuda" if torch.cuda.is_available() else "cpu"
num_samples = 3

model_id = "nvidia/parakeet-tdt-0.6b-v3"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForTDT.from_pretrained(model_id, dtype="auto", device_map=device)

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))

➡️ Timestamping

python

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

device = "cuda" if torch.cuda.is_available() else "cpu"
num_samples = 3

model_id = "nvidia/parakeet-tdt-0.6b-v3"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForTDT.from_pretrained(model_id, dtype="auto", device_map=device)

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)

➡️ Training

python

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

device = "cuda" if torch.cuda.is_available() else "cpu"

model_id = "nvidia/parakeet-tdt-0.6b-v3"
NUM_SAMPLES = 4

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForTDT.from_pretrained(model_id, dtype=torch.bfloat16, device_map=device)
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_extractor.sampling_rate)
inputs.to(device=model.device, dtype=model.dtype)

outputs = model(**inputs)
print("Loss:", outputs.loss.item())
outputs.loss.backward()

For more details about usage, please refer to the Transformers' documentation.

Software Integration:

Runtime Engine(s):

NeMo 2.4

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Volta

[Preferred/Supported] Operating System(s):

Linux

Hardware Specific Requirements:

At least 2GB RAM for model to load. The bigger the RAM, the larger audio input it supports.

Model Version

Current version: parakeet-tdt-0.6b-v3. Previous versions can be accessed here.

Training and Evaluation Datasets:

Training

This model was trained using the NeMo toolkit [5], following the strategies below:

Initialized from a CTC multilingual checkpoint pretrained on the Granary dataset [1] [2].
Trained for 150,000 steps on 128 A100 GPUs.
Dataset corpora and languages were balanced using a temperature sampling value of 0.5.
Stage 2 fine-tuning was performed for 5,000 steps on 4 A100 GPUs using approximately 7,500 hours of high-quality, human-transcribed data of NeMo ASR Set 3.0.

Training was conducted using this example script and TDT configuration.

During the training, a unified SentencePiece Tokenizer [6] with a vocabulary of 8,192 tokens was used. The unified tokenizer was constructed from the training set transcripts using this script and was optimized across all 25 supported languages.

Training Dataset

The model was trained on the combination of Granary dataset's ASR subset and in-house dataset NeMo ASR Set 3.0:

10,000 hours from human-transcribed NeMo ASR Set 3.0, including:
- LibriSpeech (960 hours)
- Fisher Corpus
- National Speech Corpus Part 1
- VCTK
- Europarl-ASR
- Multilingual LibriSpeech
- Mozilla Common Voice (v7.0)
- AMI
660,000 hours of pseudo-labeled data from Granary [1] [2], including:
- YTC [7]
- MOSEL [8]
- YODAS [9]

All transcriptions preserve punctuation and capitalization. The Granary dataset will be made publicly available after presentation at Interspeech 2025.

Data Collection Method by dataset

Hybrid: Automated, Human

Labeling Method by dataset

Hybrid: Synthetic, Human

Properties:

Noise robust data from various sources
Single channel, 16kHz sampled data

Evaluation Datasets

For multilingual ASR performance evaluation:

Fleurs [10]
MLS [11]
CoVoST [12]

For English ASR performance evaluation:

Hugging Face Open ASR Leaderboard [13] datasets

Data Collection Method by dataset

Human

Labeling Method by dataset

Human

Properties:

All are commonly used for benchmarking English ASR systems.
Audio data is typically processed into a 16kHz mono channel format for ASR evaluation, consistent with benchmarks like the Open ASR Leaderboard.

Performance

Multilingual ASR

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

Language	Fleurs	MLS	CoVoST
Average WER ↓	11.97%	7.83%	11.98%
bg	12.64%	-	-
cs	11.01%	-	-
da	18.41%	-	-
de	5.04%	-	4.84%
el	20.70%	-	-
en	4.85%	-	6.80%
es	3.45%	4.39%	3.41%
et	17.73%	-	22.04%
fi	13.21%	-	-
fr	5.15%	4.97%	6.05%
hr	12.46%	-	-
hu	15.72%	-	-
it	3.00%	10.08%	3.69%
lt	20.35%	-	-
lv	22.84%	-	38.36%
mt	20.46%	-	-
nl	7.48%	12.78%	6.50%
pl	7.31%	7.28%	-
pt	4.76%	7.50%	3.96%
ro	12.44%	-	-
ru	5.51%	-	3.00%
sk	8.82%	-	-
sl	24.03%	-	31.80%
sv	15.08%	-	20.16%
uk	6.79%	-	5.10%

Note: WERs are calculated after removing Punctuation and Capitalization from reference and predicted text.

Huggingface Open-ASR-Leaderboard

Model	Avg WER	AMI	Earnings-22	GigaSpeech	LS test-clean	LS test-other	SPGI Speech	TEDLIUM-v3	VoxPopuli
`parakeet-tdt-0.6b-v3`	6.34%	11.31%	11.42%	9.59%	1.93%	3.59%	3.97%	2.75%	6.14%

Additional evaluation details are available on the Hugging Face ASR Leaderboard.[13]

Noise Robustness

Performance across different Signal-to-Noise Ratios (SNR) using MUSAN music and noise samples [14]:

SNR Level	Avg WER	AMI	Earnings	GigaSpeech	LS test-clean	LS test-other	SPGI	Tedlium	VoxPopuli	Relative Change
Clean	6.34%	11.31%	11.42%	9.59%	1.93%	3.59%	3.97%	2.75%	6.14%	-
SNR 10	7.12%	13.99%	11.79%	9.96%	2.15%	4.55%	4.45%	3.05%	6.99%	-12.28%
SNR 5	8.23%	17.59%	13.01%	10.69%	2.62%	6.05%	5.23%	3.33%	7.31%	-29.81%
SNR 0	11.66%	24.44%	17.34%	13.60%	4.82%	10.38%	8.41%	5.39%	8.91%	-83.97%
SNR -5	19.88%	34.91%	26.92%	21.41%	12.21%	19.98%	16.96%	11.36%	15.30%	-213.64%

References

[1] Granary: Speech Recognition and Translation Dataset in 25 European Languages

[2] NVIDIA Granary Dataset Card

[3] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[4] Efficient Sequence Transduction by Jointly Predicting Tokens and Durations

[5] NVIDIA NeMo Toolkit

[6] Google Sentencepiece Tokenizer

[7] Youtube-Commons

[8] MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages

[9] YODAS: Youtube-Oriented Dataset for Audio and Speech

[10] FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech

[11] MLS: A Large-Scale Multilingual Dataset for Speech Research

[12] CoVoST 2 and Massively Multilingual Speech-to-Text Translation

[13] HuggingFace ASR Leaderboard

[14] MUSAN: A Music, Speech, and Noise Corpus

Inference:

Engine:

NVIDIA NeMo

Test Hardware:

NVIDIA A10
NVIDIA A100
NVIDIA A30
NVIDIA H100
NVIDIA L4
NVIDIA L40
NVIDIA Turing T4
NVIDIA Volta V100

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards here.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Bias:

Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing	None
Measures taken to mitigate against unwanted bias	None

Explainability:

Field	Response
Intended Domain	Speech to Text Transcription
Model Type	FastConformer
Intended Users	This model is intended for developers, researchers, academics, and industries building conversational based applications.
Output	Text
Describe how the model works	Speech input is encoded into embeddings and passed into conformer-based model and output a text response.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of	Not Applicable
Technical Limitations & Mitigation	Transcripts may be not 100% accurate. Accuracy varies based on language and characteristics of input audio (Domain, Use Case, Accent, Noise, Speech Type, Context of speech, etc.)
Verified to have met prescribed NVIDIA quality standards	Yes
Performance Metrics	Word Error Rate
Potential Known Risks	If a word is not trained in the language model and not presented in vocabulary, the word is not likely to be recognized. Not recommended for word-for-word/incomplete sentences as accuracy varies based on the context of input text
Licensing	GOVERNING TERMS: Use of this model is governed by the CC-BY-4.0 license.

Privacy:

Field	Response
Generatable or reverse engineerable personal data?	None
Personal data used to create this model?	None
Is there provenance for all datasets used in training?	Yes
Does data labeling (annotation, metadata) comply with privacy laws?	Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?	No, not possible with externally-sourced data.
Applicable Privacy Policy	https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

Safety:

Field	Response
Model Application(s)	Speech to Text Transcription
Describe the life critical impact	None
Use Case Restrictions	Abide by CC-BY-4.0 License
Model and dataset restrictions	The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.