---

license: cc-by-4.0

base_model: nvidia/parakeet-tdt-0.6b-v2

base_model_relation: quantized

library_name: transcribe.cpp

pipeline_tag: automatic-speech-recognition

language:

- en

tags:

- gguf

- transcribe.cpp

- asr

- speech-to-text

- parakeet

- conformer

- rnnt

- tdt

transcribe_cpp:

wer_librispeech_test_clean:

f32: 1.68

f16: 1.68

q8_0: 1.69

q6_k: 1.7

q5_k_m: 1.7

q4_k_m: 1.72

rtf_ryzen_4750u:

cpu: 8

vulkan: 15

rtf_m4_max:

cpu: 29

metal: 175

streaming: false

translate: false

lang_detect: false

timestamps: token

---

parakeet-tdt-0.6b-v2: transcribe.cpp GGUF

GGUF conversions of nvidia/parakeet-tdt-0.6b-v2 for use

with transcribe.cpp.

Ported from upstream commit

1b149a3,

pinned 2026-04-15.

Validated against the NeMo reference at transcribe.cpp commit

bf0d0b7

on 2026-04-18.

Offline English speech-to-text. A 0.6B-parameter Conformer encoder with a

TDT/RNNT transducer decoder. Takes a 16 kHz mono WAV and produces a transcript

with optional token-level timestamps. Not a streaming model; no multilingual

capability (see v3 for that).

Downloads

| Quantization | Download | Size | WER (LibriSpeech test-clean) |

| --- | --- | ---: | ---: |

| F32 | parakeet-tdt-0.6b-v2-F32.gguf | 2.47 GB | 1.68% |

| F16 | parakeet-tdt-0.6b-v2-F16.gguf | 1.24 GB | 1.68% |

| Q8_0 | parakeet-tdt-0.6b-v2-Q8_0.gguf | 730 MB | 1.69% |

| Q6_K | parakeet-tdt-0.6b-v2-Q6_K.gguf | 608 MB | 1.70% |

| Q5_K_M | parakeet-tdt-0.6b-v2-Q5_K_M.gguf | 547 MB | 1.70% |

| Q4_K_M | parakeet-tdt-0.6b-v2-Q4_K_M.gguf | 483 MB | 1.72% |

WER measured on the full LibriSpeech test-clean split (2620 utterances) with

greedy transducer decoding and no external LM. F32 reference baseline: 1.68%.

NVIDIA's self-reported number on the same split is 1.69%, so the F32 and Q8_0

ports match the upstream reference within rounding.

Usage

Build transcribe.cpp from source:

git clone git@github.com:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build

Run on a 16 kHz mono WAV:

build/bin/transcribe-cli \
  -m parakeet-tdt-0.6b-v2-Q8_0.gguf \
  input.wav

If your audio isn't already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

See the transcribe.cpp model page for performance

numbers, numerical validation, and reproduction steps.

License

Inherited from the base model: CC-BY-4.0. See the

upstream model card for full terms.

---

Original Model Card

> The section below is reproduced from

> nvidia/parakeet-tdt-0.6b-v2 at commit

> 1b149a3 for offline reference. The upstream card is the

> authoritative source.

🦜 Parakeet TDT 0.6B V2 (En)

<style>

img {

display: inline;

}

</style>


| 

| 

> 🎉 NEW: Multilingual Parakeet TDT 0.6B V3 is now available!

> 🌍 25 European Languages | 🚀 Enhanced Performance | 🔗 Try it here: nvidia/parakeet-tdt-0.6b-v3

<span style="color:#466f00;">Description:</span>

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 [1] architecture integrates the TDT [2] 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.

<span style="color:#466f00;">License/Terms of Use:</span>

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

<span style="color:#466f00;">Discover more from NVIDIA:</span>

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.<br>

<span style="color:#466f00;">Explore more from NVIDIA:</span><br>

What is Nemotron?<br>

NVIDIA Developer Nemotron<br>

NVIDIA Riva Speech<br>

NeMo Documentation<br>

<span style="color:#466f00;">Deployment Geography:</span>

Global

<span style="color:#466f00;">Use Case:</span>

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.

<span style="color:#466f00;">Release Date:</span>

05/01/2025

<span style="color:#466f00;">Model Architecture:</span>

Architecture Type:

FastConformer-TDT

Network Architecture:

• This model was developed based on FastConformer encoder architecture[1] and TDT decoder[2]
• This model has 600 million model parameters.

<span style="color:#466f00;">Input:</span>

• 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

<span style="color:#466f00;">Output:</span>

• 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.

<span style="color:#466f00;">How to Use this Model:</span>

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 [3], 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-v2")

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']}")

<span style="color:#466f00;">Try via API — No Setup Required</span>

Transcribe audio instantly using the free hosted API on build.nvidia.com — no GPU, no Docker, no model download needed.

1. Get a free API key: Visit build.nvidia.com/nvidia/parakeet-tdt-0_6b-v2 and click Get API Key

2. Install the Riva client:

pip install nvidia-riva-client

3. Transcribe an audio file:

import riva.client

auth = riva.client.Auth(
    uri="grpc.nvcf.nvidia.com:443",
    use_ssl=True,
    metadata_args=[
        ["function-id", "d3fe9151-442b-4204-a70d-5fcc597fd610"],
        ["authorization", "Bearer nvapi-YOUR_API_KEY"]
    ]
)

asr_service = riva.client.ASRService(auth)

with open("audio.wav", "rb") as f:
    audio = f.read()

config = riva.client.RecognitionConfig(
    language_code="en-US",
    max_alternatives=1,
    enable_automatic_punctuation=True,
    enable_word_time_offsets=True,
)

response = asr_service.offline_recognize(audio, config)
print(response.results[0].alternatives[0].transcript)

Or use the CLI:

git clone https://github.com/nvidia-riva/python-clients.git
export NVIDIA_API_KEY="nvapi-YOUR_API_KEY"

python python-clients/scripts/asr/transcribe_file_offline.py \
    --server grpc.nvcf.nvidia.com:443 --use-ssl \
    --metadata function-id "d3fe9151-442b-4204-a70d-5fcc597fd610" \
    --metadata "authorization" "Bearer $NVIDIA_API_KEY" \
    --language-code en-US \
    --word-time-offsets --automatic-punctuation \
    --input-file audio.wav

> Note: The hosted API accepts 16-bit mono audio in WAV, OGG, or OPUS format. See the API Reference for streaming and advanced options.

<span style="color:#466f00;">Software Integration:</span>

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-v2. Previous versions can be accessed here.

<span style="color:#466f00;">Training and Evaluation Datasets:</span>

<span style="color:#466f00;">Training</span>

This model was trained using the NeMo toolkit [3], following the strategies below:

• Initialized from a FastConformer SSL checkpoint that was pretrained with a wav2vec method on the LibriLight dataset[7].
• Trained for 150,000 steps on 64 A100 GPUs.
• Dataset corpora were balanced using a temperature sampling value of 0.5.
• Stage 2 fine-tuning was performed for 2,500 steps on 4 A100 GPUs using approximately 500 hours of high-quality, human-transcribed data of NeMo ASR Set 3.0.

Training was conducted using this example script and TDT configuration.

The tokenizer was constructed from the training set transcripts using this script.

<span style="color:#466f00;">Training Dataset</span>

The model was trained on the Granary dataset[8], consisting of approximately 120,000 hours of English speech data:

• 10,000 hours from human-transcribed NeMo ASR Set 3.0, including:

- LibriSpeech (960 hours)

- Fisher Corpus

- National Speech Corpus Part 1

- VCTK

- VoxPopuli (English)

- Europarl-ASR (English)

- Multilingual LibriSpeech (MLS English) – 2,000-hour subset

- Mozilla Common Voice (v7.0)

- AMI

• 110,000 hours of pseudo-labeled data from:

- YTC (YouTube-Commons) dataset[4]

- YODAS dataset [5]

- Librilight [7]

All transcriptions preserve punctuation and capitalization. The Granary dataset[8] 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 Dataset

Huggingface Open ASR Leaderboard datasets are used to evaluate the performance of this model.

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.

<span style="color:#466f00;">Performance</span>

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 10 | 6.95 | 14.38 | 12.04 | 10.24 | 1.92 | 4.13 | 2.84 | 3.63 | 6.38 | -14.75% |

| SNR 5 | 8.23 | 18.07 | 13.82 | 11.18 | 2.33 | 5.58 | 3.81 | 4.24 | 6.81 | -35.97% |

| SNR 0 | 11.88 | 25.43 | 18.59 | 14.32 | 4.40 | 10.07 | 7.27 | 6.42 | 8.54 | -96.28% |

| SNR -5 | 20.26 | 36.57 | 28.06 | 22.27 | 11.82 | 19.91 | 16.14 | 13.07 | 14.23 | -234.66% |

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.[6]

<span style="color:#466f00;">References</span>

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

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

[3] NVIDIA NeMo Toolkit

[4] Youtube-commons: A massive open corpus for conversational and multimodal data

[5] Yodas: Youtube-oriented dataset for audio and speech

[6] HuggingFace ASR Leaderboard

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

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

<span style="color:#466f00;">Inference:</span>

Engine:

• NVIDIA NeMo

Test Hardware:

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

<span style="color:#466f00;">Ethical Considerations:</span>

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.

<span style="color:#466f00;">Bias:</span>

Field | Response

---------------------------------------------------------------------------------------------------|---------------

Participation considerations from adversely impacted groups protected classes in model design and testing | None

Measures taken to mitigate against unwanted bias | None

<span style="color:#466f00;">Explainability:</span>

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.

<span style="color:#466f00;">Privacy:</span>

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/

<span style="color:#466f00;">Safety:</span>

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.