

## 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 [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.ASRModel.from_pretrained("iqbalc/stt_de_conformer_transducer_large")
```

### Transcribing using Python
```
asr_model.transcribe(['filename.wav'])

```

### Transcribing many audio files

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

### Input

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

### Output

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

## Model Architecture

Conformer-Transducer model is an autoregressive variant of Conformer model for Automatic Speech Recognition which uses Transducer loss/decoding

## Training

The NeMo toolkit was used for training the models. These models are fine-tuned 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

All the models in this collection are trained on a composite dataset comprising of over two thousand hours of cleaned German speech:

1. MCV7.0 567 hours 
2. MLS 1524 hours 
3. VoxPopuli 214 hours

## Performance

Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

MCV7.0 test	= 4.93

## Limitations

The model might perform worse for accented speech


## References
[NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
{"language": ["de"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "NeMo", "pytorch"], "datasets": ["mozilla-foundation/common_voice_7_0", "Multilingual LibriSpeech (2000 hours)"], "model-index": [{"name": "stt_de_conformer_transducer_large", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "mozilla-foundation/common_voice_7_0", "type": "common_voice_7_0", "config": "other", "split": "test", "args": {"lageangu": "de"}}, "metrics": [{"type": "wer", "value": 4.93, "name": "WER"}]}]}]}
nemo automatic-speech-recognition speech audio CTC Conformer Transformer NeMo pytorch de license:cc-by-4.0 model-index region:us
TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #NeMo #pytorch #de #license-cc-by-4.0 #model-index #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 [3], 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


### Transcribing many audio files



### Input

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

### Output

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

## Model Architecture

Conformer-Transducer model is an autoregressive variant of Conformer model for Automatic Speech Recognition which uses Transducer loss/decoding

## Training

The NeMo toolkit was used for training the models. These models are fine-tuned 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

All the models in this collection are trained on a composite dataset comprising of over two thousand hours of cleaned German speech:

1. MCV7.0 567 hours 
2. MLS 1524 hours 
3. VoxPopuli 214 hours

## Performance

Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

MCV7.0 test	= 4.93

## Limitations

The model might perform worse for accented speech


## References
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 [3], 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 ### Transcribing many audio files ### Input

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

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

Conformer-Transducer model is an autoregressive variant of Conformer model for Automatic Speech Recognition which uses Transducer loss/decoding ## Training

The NeMo toolkit was used for training the models. These models are fine-tuned 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

All the models in this collection are trained on a composite dataset comprising of over two thousand hours of cleaned German speech:

1. MCV7.0 567 hours 
2. MLS 1524 hours 
3. VoxPopuli 214 hours ## Performance

Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

MCV7.0 test	= 4.93 ## Limitations

The model might perform worse for accented speech ## References
NVIDIA NeMo Toolkit
TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #NeMo #pytorch #de #license-cc-by-4.0 #model-index #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 [3], 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 ### Transcribing many audio files ### Input

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

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

Conformer-Transducer model is an autoregressive variant of Conformer model for Automatic Speech Recognition which uses Transducer loss/decoding ## Training

The NeMo toolkit was used for training the models. These models are fine-tuned 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

All the models in this collection are trained on a composite dataset comprising of over two thousand hours of cleaned German speech:

1. MCV7.0 567 hours 
2. MLS 1524 hours 
3. VoxPopuli 214 hours ## Performance

Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

MCV7.0 test	= 4.93 ## Limitations

The model might perform worse for accented speech ## References
NVIDIA NeMo Toolkit
passage: TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #NeMo #pytorch #de #license-cc-by-4.0 #model-index #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 [3], 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### Transcribing many audio files### Input

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

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

Conformer-Transducer model is an autoregressive variant of Conformer model for Automatic Speech Recognition which uses Transducer loss/decoding## Training

The NeMo toolkit was used for training the models. These models are fine-tuned 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

All the models in this collection are trained on a composite dataset comprising of over two thousand hours of cleaned German speech:

1. MCV7.0 567 hours 
2. MLS 1524 hours 
3. VoxPopuli 214 hours## Performance

Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

MCV7.0 test	= 4.93## Limitations

The model might perform worse for accented speech## References
NVIDIA NeMo Toolkit