README.md

---
license: cc-by-4.0
language:
- en
- es
- fr
- de
- bg
- hr
- cs
- da
- nl
- et
- fi
- el
- hu
- it
- lv
- lt
- mt
- pl
- pt
- ro
- sk
- sl
- sv
- ru
- uk

pipeline_tag: automatic-speech-recognition
library_name: nemo
datasets:
- granary
- librispeech_asr
- fisher_corpus
- mozilla-foundation/common_voice_8_0
- National-Singapore-Corpus-Part-1
- vctk
- voxpopuli
- europarl
- multilingual_librispeech
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- Transducer
- TDT
- FastConformer
- Conformer
- pytorch
- NeMo
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: parakeet-tdt-0.6b-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: AMI (Meetings test)
      type: edinburghcstr/ami
      config: ihm
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 11.16
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Earnings-22
      type: revdotcom/earnings22
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 11.15
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: GigaSpeech
      type: speechcolab/gigaspeech
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 9.74
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech (clean)
      type: librispeech_asr
      config: other
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 1.69
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech (other)
      type: librispeech_asr
      config: other
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 3.19
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: SPGI Speech
      type: kensho/spgispeech
      config: test
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 2.17
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: tedlium-v3
      type: LIUM/tedlium
      config: release1
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 3.38
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Vox Populi
      type: facebook/voxpopuli
      config: en
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 5.95
metrics:
- wer
---

# **Parakeet TDT 0.6B V3 (En)**

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

[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--TDT-blue#model-badge)](#model-architecture)
| [![Model size](https://img.shields.io/badge/Params-0.6B-green#model-badge)](#model-architecture)
| [![Language](https://img.shields.io/badge/Language-en-orange#model-badge)](#datasets)

## <span style="color:#76b900;">🦜 parakeet-tdt-0.6b-v3: Multilingual Speech-to-Text Model</span>

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

`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](https://huggingface.co/nvidia/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](https://huggingface.co/datasets/nvidia/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**)


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

`parakeet-tdt-0.6b-v3`'s key features are built on the foundation of its predecessor, [parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/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**

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](https://creativecommons.org/licenses/by/4.0/legalcode.en) license.


### <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>

08/14/2025

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

**Architecture Type**: 

FastConformer-TDT

**Network Architecture**:

* This model was developed based on [FastConformer encoder](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) architecture[3] and TDT decoder[4]
* 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. 

For more information, refer to the [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).

## <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](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
```bash
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

```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
```bash
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)
```




## <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-v3`. Previous versions can be [accessed](https://huggingface.co/collections/nvidia/parakeet-659711f49d1469e51546e021) here. 

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

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

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](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and [TDT configuration](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_tdt_ctc_bpe.yaml).

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](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py) and was optimized across all 25 supported languages.

### <span style="color:#466f00;">Training Dataset</span>
The model was trained on the combination of [Granary dataset's ASR subset](https://huggingface.co/datasets/nvidia/Granary) 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](https://huggingface.co/datasets/FBK-MT/mosel) \[7]
  - [MOSEL](https://huggingface.co/datasets/FBK-MT/mosel) \[8]
  - [YODAS](https://huggingface.co/datasets/espnet/yodas-granary) \[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](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard).

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

#### 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 ↓** | *11.94%* | *7.84%* | *11.98%* |
| **bg** | 12.62% | - | - |
| **cs** | 10.92% | - | - |
| **da** | 18.37% | - | - |
| **de** | 5.07% | - | 4.84% |
| **el** | 20.76% | - | - |
| **en** | 4.85% | - | 6.80% |
| **es** | 3.39% | 4.39% | 3.40% |
| **et** | 17.74% | - | 22.06% |
| **fi** | 13.19% | - | - |
| **fr** | 5.12% | 4.96% | 6.05% |
| **hr** | 12.42% | - | - |
| **hu** | 15.64% | - | - |
| **it** | 2.93% | 10.05% | 3.68% |
| **lt** | 20.34% | - | - |
| **lv** | 22.74% | - | 38.33% |
| **mt** | 20.33% | - | - |
| **nl** | 7.44% | 12.82% | 6.50% |
| **pl** | 7.30% | 7.28% | - |
| **pt** | 4.68% | 7.52% | 4.03% |
| **ro** | 12.40% | - | - |
| **ru** | 5.47% | - | 3.00% |
| **sk** | 8.76% | - | - |
| **sl** | 24.06% | - | 31.80% |
| **sv** | 15.11% | - | 20.16% |
| **uk** | 6.86% | - | 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` | 6.35 | 11.36 | 11.42 | 9.57 | 1.93 | 3.59 | 3.98 | 2.80 | 6.13 |

Additional evaluation details are available on the [Hugging Face ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_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 | 6.35 | 11.36 | 11.42 | 9.57 | 1.93 | 3.59 | 3.98 | 2.80 | 6.13 | - |
| SNR 50 | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD |
| SNR 25 | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD |
| SNR 5 | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD  |



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

[1] [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://arxiv.org/abs/2505.13404)

[2] [NVIDIA Granary Dataset Card](https://huggingface.co/datasets/nvidia/Granary)

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

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

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

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

[7] [Youtube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons)

[8] [MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages](https://arxiv.org/abs/2410.01036)

[9] [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://arxiv.org/pdf/2406.00899)

[10] [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://arxiv.org/abs/2205.12446)

[11] [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)

[12] [CoVoST 2 and Massively Multilingual Speech-to-Text Translation](https://arxiv.org/abs/2007.10310)

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

[14] [MUSAN: A Music, Speech, and Noise Corpus](https://arxiv.org/abs/1510.08484)

## <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](https://developer.nvidia.com/blog/enhancing-ai-transparency-and-ethical-considerations-with-model-card/).

Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).