---
license: cc-by-4.0
base_model: nvidia/parakeet-tdt-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: 1.39
    f16: 1.39
    q8_0: 1.38
    q6_k: 1.4
    q5_k_m: 1.39
    q4_k_m: 1.42
  rtf_m4_max:
    metal: 125.5
    cpu: 17.5
  rtf_ryzen_4750u:
    vulkan: 11.5
    cpu: 5.5
  streaming: false
  translate: false
  lang_detect: false
  timestamps: token
---

# parakeet-tdt-1.1b: transcribe.cpp GGUF

GGUF conversions of [nvidia/parakeet-tdt-1.1b](https://huggingface.co/nvidia/parakeet-tdt-1.1b) for use
with [transcribe.cpp](https://github.com/handy-computer/transcribe.cpp).

Ported from upstream commit
[53276c6](https://huggingface.co/nvidia/parakeet-tdt-1.1b/commit/53276c6),
pinned 2026-05-10.
Validated against the NeMo reference at transcribe.cpp commit
[42528dd](https://github.com/handy-computer/transcribe.cpp/tree/42528dd)
on 2026-05-10.

Offline English speech-to-text. A 1.1B-parameter FastConformer-XL 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 and does not translate.


## Downloads

| Quantization | Download | Size | WER (LibriSpeech test-clean) |
| --- | --- | ---: | ---: |
| F32 | [parakeet-tdt-1.1b-F32.gguf](https://huggingface.co/handy-computer/parakeet-tdt-1.1b-gguf/resolve/main/parakeet-tdt-1.1b-F32.gguf) | 4.28 GB | 1.39% |
| F16 | [parakeet-tdt-1.1b-F16.gguf](https://huggingface.co/handy-computer/parakeet-tdt-1.1b-gguf/resolve/main/parakeet-tdt-1.1b-F16.gguf) | 2.15 GB | 1.39% |
| Q8_0 | [parakeet-tdt-1.1b-Q8_0.gguf](https://huggingface.co/handy-computer/parakeet-tdt-1.1b-gguf/resolve/main/parakeet-tdt-1.1b-Q8_0.gguf) | 1.27 GB | 1.38% |
| Q6_K | [parakeet-tdt-1.1b-Q6_K.gguf](https://huggingface.co/handy-computer/parakeet-tdt-1.1b-gguf/resolve/main/parakeet-tdt-1.1b-Q6_K.gguf) | 1.04 GB | 1.40% |
| Q5_K_M | [parakeet-tdt-1.1b-Q5_K_M.gguf](https://huggingface.co/handy-computer/parakeet-tdt-1.1b-gguf/resolve/main/parakeet-tdt-1.1b-Q5_K_M.gguf) | 936 MB | 1.39% |
| Q4_K_M | [parakeet-tdt-1.1b-Q4_K_M.gguf](https://huggingface.co/handy-computer/parakeet-tdt-1.1b-gguf/resolve/main/parakeet-tdt-1.1b-Q4_K_M.gguf) | 825 MB | 1.42% |

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: 1.39%. NVIDIA's self-reported number on the same split is 1.39%.


## Usage

Build transcribe.cpp from source:

```bash
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:

```bash
build/bin/transcribe-cli \
  -m parakeet-tdt-1.1b-Q8_0.gguf \
  input.wav
```

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

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

See the [transcribe.cpp model page](https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/parakeet-tdt-1.1b.md) for performance
numbers, numerical validation, and reproduction steps.

## License

Inherited from the base model: **CC-BY-4.0**. See the
[upstream model card](https://huggingface.co/nvidia/parakeet-tdt-1.1b) for full terms.

---

## Original Model Card

> The section below is reproduced from
> [nvidia/parakeet-tdt-1.1b](https://huggingface.co/nvidia/parakeet-tdt-1.1b) at commit
> `53276c6` for offline reference. The upstream card is the
> authoritative source.

# Parakeet TDT 1.1B (en)

<style>
img {
 display: inline;
}
</style>

[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--TDT-lightgrey#model-badge)](#model-architecture)
| [![Model size](https://img.shields.io/badge/Params-1.1B-lightgrey#model-badge)](#model-architecture)
| [![Language](https://img.shields.io/badge/Language-en-lightgrey#model-badge)](#datasets)


`parakeet-tdt-1.1b` is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) and [Suno.ai](https://www.suno.ai/) teams.
It is an XXL version of FastConformer [1] TDT [2] (around 1.1B parameters) model.
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) for complete architecture details.

## 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](https://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>

### Explore more from NVIDIA:  <br>
What is [Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/)?<br>
NVIDIA Developer [Nemotron](https://developer.nvidia.com/nemotron)<br>
[NVIDIA Riva Speech](https://developer.nvidia.com/riva?sortBy=developer_learning_library%2Fsort%2Ffeatured_in.riva%3Adesc%2Ctitle%3Aasc#demos)<br>
[NeMo Documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html)<br>

## NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/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

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-tdt-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

```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
 pretrained_name="nvidia/parakeet-tdt-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 FastConformer-TDT 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](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).

TDT (Token-and-Duration Transducer) [2] is a generalization of conventional Transducers by decoupling token and duration predictions. Unlike conventional Transducers which produces a lot of blanks during inference, a TDT model can skip majority of blank predictions by using the duration output (up to 4 frames for this parakeet-tdt-1.1b model), thus brings significant inference speed-up. The detail of TDT can be found here: [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795).

## Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml).

The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).

### Datasets

The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:

- Librispeech 960 hours of English speech
- Fisher Corpus
- Switchboard-1 Dataset
- WSJ-0 and WSJ-1
- National Speech Corpus (Part 1, Part 6)
- VCTK
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hour subset
- Mozilla Common Voice (v7.0)
- People's Speech  - 12,000 hour subset

## 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**|**SPGI Speech**|**TEDLIUM-v3**|**Vox Populi**|**Common Voice**|
|---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-----|-------|------|------|
| 1.22.0  | SentencePiece Unigram | 1024 | 15.90 | 14.65 | 9.55 | 1.39 | 2.62 | 3.42 | 3.56 | 5.48 | 5.97 |

These are greedy WER numbers without external LM. More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)


## Model Fairness Evaluation

As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset", we assessed the parakeet-tdt-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 | 17.18 | 14.61 | 19.06 | 37.57 |

### Age Bias:

| Age Group | $(18-30)$ | $(31-45)$ | $(46-85)$ | $(1-100)$ |
| :--- | :--- | :--- | :--- | :--- |
| Num utterances | 15956 | 14585 | 13349 | 43890 |
| % WER | 15.83 | 15.89 | 15.46 | 15.74 |

(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](https://developer.nvidia.com/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](https://huggingface.co/models?other=Riva).  
Check out [Riva live demo](https://developer.nvidia.com/riva#demos). 

## References
[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)

[2] [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795)

[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)

[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

[5] [Suno.ai](https://suno.ai/)

[6] [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)

[7] [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://arxiv.org/abs/2104.02821)


## Licence

License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/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](https://creativecommons.org/licenses/by/4.0/) license.