
## Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>

## 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 [1], 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("ypluit/stt_kr_citrinet1024_PublicCallCenter_1000H_0.22")
```


### Transcribing using Python
First, let's get a sample
```
get any korean telephone voice wave file
```
Then simply do:
```
asr_model.transcribe(['sample-kr.wav'])
```

### Transcribing many audio files

```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="model"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```

### Input

This model accepts 16000Hz Mono-channel Audio (wav files) as input.

### Output

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


## Model Architecture

See nemo toolkit and reference papers.
## Training

Learned about 30 days on 2 A6000

### Datasets

Private call center real data (1100hour)

## Performance

< 0.13 CER

## Limitations

This model was trained with 650 hours of Korean telephone voice data for customer service in a call center. might be Poor performance for general-purpose dialogue and specific accents.

## References


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

{"language": ["kr"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "Citrinet1024", "NeMo", "pytorch"], "datasets": ["RealCallData"], "model-index": [{"name": "stt_kr_citrinet1024_PublicCallCenter_1000H_0.22", "results": []}]}
nemo automatic-speech-recognition speech audio Citrinet1024 NeMo pytorch kr dataset:RealCallData license:cc-by-4.0 region:us
TAGS
#nemo #automatic-speech-recognition #speech #audio #Citrinet1024 #NeMo #pytorch #kr #dataset-RealCallData #license-cc-by-4.0 #region-us 


## Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>

## 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.
 

## How to Use this Model

The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.


### Automatically instantiate the model




### Transcribing using Python
First, let's get a sample

Then simply do:


### Transcribing many audio files



### Input

This model accepts 16000Hz Mono-channel Audio (wav files) as input.

### Output

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


## Model Architecture

See nemo toolkit and reference papers.
## Training

Learned about 30 days on 2 A6000

### Datasets

Private call center real data (1100hour)

## Performance

< 0.13 CER

## Limitations

This model was trained with 650 hours of Korean telephone voice data for customer service in a call center. might be Poor performance for general-purpose dialogue and specific accents.

## References


[1] NVIDIA NeMo Toolkit

## Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## 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. ## How to Use this Model

The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ### Transcribing using Python
First, let's get a sample

Then simply do: ### Transcribing many audio files ### Input

This model accepts 16000Hz Mono-channel Audio (wav files) as input. ### Output

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

See nemo toolkit and reference papers. ## Training

Learned about 30 days on 2 A6000 ### Datasets

Private call center real data (1100hour) ## Performance

< 0.13 CER ## Limitations

This model was trained with 650 hours of Korean telephone voice data for customer service in a call center. might be Poor performance for general-purpose dialogue and specific accents. ## References


[1] NVIDIA NeMo Toolkit
TAGS
#nemo #automatic-speech-recognition #speech #audio #Citrinet1024 #NeMo #pytorch #kr #dataset-RealCallData #license-cc-by-4.0 #region-us 
 ## Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## 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. ## How to Use this Model

The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ### Transcribing using Python
First, let's get a sample

Then simply do: ### Transcribing many audio files ### Input

This model accepts 16000Hz Mono-channel Audio (wav files) as input. ### Output

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

See nemo toolkit and reference papers. ## Training

Learned about 30 days on 2 A6000 ### Datasets

Private call center real data (1100hour) ## Performance

< 0.13 CER ## Limitations

This model was trained with 650 hours of Korean telephone voice data for customer service in a call center. might be Poor performance for general-purpose dialogue and specific accents. ## References


[1] NVIDIA NeMo Toolkit
TAGS
#nemo #automatic-speech-recognition #speech #audio #Citrinet1024 #NeMo #pytorch #kr #dataset-RealCallData #license-cc-by-4.0 #region-us 
## Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>## 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.## How to Use this Model

The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.### Automatically instantiate the model### Transcribing using Python
First, let's get a sample

Then simply do:### Transcribing many audio files### Input

This model accepts 16000Hz Mono-channel Audio (wav files) as input.### Output

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

See nemo toolkit and reference papers.## Training

Learned about 30 days on 2 A6000### Datasets

Private call center real data (1100hour)## Performance

< 0.13 CER## Limitations

This model was trained with 650 hours of Korean telephone voice data for customer service in a call center. might be Poor performance for general-purpose dialogue and specific accents.## References


[1] NVIDIA NeMo Toolkit