---

license: cc-by-4.0

base_model: nvidia/parakeet-tdt_ctc-1.1b

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: 1.87

f16: 1.87

q8_0: 1.87

q6_k: 1.87

q5_k_m: 1.87

q4_k_m: 1.91

rtf_m4_max:

metal: 125.5

cpu: 18.5

rtf_ryzen_4750u:

vulkan: 11

cpu: 5.5

streaming: false

translate: false

lang_detect: false

timestamps: token

---

parakeet-tdt_ctc-1.1b: transcribe.cpp GGUF

GGUF conversions of nvidia/parakeet-tdt_ctc-1.1b for use

with transcribe.cpp.

Ported from upstream commit

675e786,

pinned 2026-05-10.

Validated against the NeMo reference at transcribe.cpp commit

42528dd

on 2026-05-10.

Offline English speech-to-text with punctuation and capitalization. A 1.1B-parameter FastConformer-XL encoder with a TDT/RNNT transducer decoder (the auxiliary CTC head from the upstream hybrid checkpoint is dropped at convert time). Not a streaming model and does not translate.

Downloads

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

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

| F32 | parakeet-tdt_ctc-1.1b-F32.gguf | 4.28 GB | 1.87% |

| F16 | parakeet-tdt_ctc-1.1b-F16.gguf | 2.15 GB | 1.87% |

| Q8_0 | parakeet-tdt_ctc-1.1b-Q8_0.gguf | 1.27 GB | 1.87% |

| Q6_K | parakeet-tdt_ctc-1.1b-Q6_K.gguf | 1.04 GB | 1.87% |

| Q5_K_M | parakeet-tdt_ctc-1.1b-Q5_K_M.gguf | 936 MB | 1.87% |

| Q4_K_M | parakeet-tdt_ctc-1.1b-Q4_K_M.gguf | 825 MB | 1.91% |

WER measured on the full LibriSpeech test-clean split (2620 utterances) with greedy TDT/RNN-T transducer decoding and no external LM. F32 reference baseline: 1.87%. NVIDIA's self-reported number on the same split is 1.82%.

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_ctc-1.1b-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_ctc-1.1b at commit

> 675e786 for offline reference. The upstream card is the

> authoritative source.

Parakeet TDT-CTC 1.1B PnC(en)

<style>

img {

display: inline;

}

</style>


| 

| 

parakeet-tdt_ctc-1.1b is an ASR model that transcribes speech with Punctuations and Capitalizations of English alphabet. This model is jointly developed by NVIDIA NeMo and Suno.ai teams.

It is an XXL version of Hybrid FastConformer [1] TDT-CTC [2] (around 1.1B parameters) model. This model has been trained with Local Attention and Global token hence this model can transcribe 11 hrs of audio in one single pass. And for reference this model can transcibe 90mins of audio in <16 sec on A100.

See the model architecture section and NeMo documentation for complete architecture details.

NVIDIA NeMo: Training

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']

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.

Automatically instantiate the model

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/parakeet-tdt_ctc-1.1b")

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

By default model uses TDT to transcribe the audio files, to switch decoder to use CTC, use decoding_type='ctc'

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
 pretrained_name="nvidia/parakeet-tdt_ctc-1.1b" 
 audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

Input

This model accepts 16000 Hz mono-channel audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Model Architecture

This model uses a Hybrid FastConformer-TDT-CTC architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: Fast-Conformer Model.

Training

The NeMo toolkit [3] was used for finetuning this model for 20,000 steps over parakeet-tdt-1.1 model. This model is trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

The model was trained on 36K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 27K hours of English speech plus 9k hours from the following public PnC datasets:

• Librispeech 960 hours of English speech
• Fisher Corpus
• National Speech Corpus Part 1
• VCTK
• VoxPopuli (EN)
• Europarl-ASR (EN)
• Multilingual Librispeech (MLS EN) - 2,000 hour subset
• Mozilla Common Voice (v7.0)

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The following tables summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

|Version|Tokenizer|Vocabulary Size|AMI|Earnings-22|Giga Speech|LS test-clean| LS test-other | SPGI Speech|TEDLIUM-v3|Vox Populi|Common Voice|

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

| 1.23.0 | SentencePiece Unigram | 1024 | 15.94 | 11.86 | 10.19 | 1.82 | 3.67 | 2.24 | 3.87 | 6.19 | 8.69 |

These are greedy WER numbers without external LM. More details on evaluation can be found at HuggingFace ASR Leaderboard

Model Fairness Evaluation

As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset", we assessed the parakeet-tdt_ctc-1.1b model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows:

Gender Bias:

| Gender | Male | Female | N/A | Other |

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

| Num utterances | 19325 | 24532 | 926 | 33 |

| % WER | 12.81 | 10.49 | 13.88 | 23.12 |

Age Bias:

| Age Group | (18-30) | (31-45) | (46-85) | (1-100) |

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

| Num utterances | 15956 | 14585 | 13349 | 43890 |

| % WER | 11.50 | 11.63 | 11.38 | 11.51 |

(Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)

NVIDIA Riva: Deployment

NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.

Additionally, Riva provides:

• World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
• Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
• Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support

Although this model isn’t supported yet by Riva, the list of supported models is here.

Check out Riva live demo.

References

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

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

[3] Google Sentencepiece Tokenizer

[4] NVIDIA NeMo Toolkit

[5] Suno.ai

[6] HuggingFace ASR Leaderboard

[7] Towards Measuring Fairness in AI: the Casual Conversations Dataset

Licence

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.