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

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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

License/Terms of Use:

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

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:

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 -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.

Automatically instantiate the model

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:

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

Transcribing with timestamps

To transcribe with timestamps:

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

#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)

Software Integration:

Runtime Engine(s):

• NeMo 2.2

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Hopper
• NVIDIA Volta

[Preferred/Supported] Operating System(s):

• Linux

Hardware Specific Requirements:

Atleast 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):

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

Huggingface Open-ASR-Leaderboard

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]:

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

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License

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