---
language: en
license: cc-by-4.0
tags:
- automatic-speech-recognition
- nemo
- conformer
- entity_tagging
- intent
datasets:
- slurp
metrics:
- wer
- cer
model-index:
- name: 1step ASR-NL for Slurp dataset
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Slurp dataset
      type: slurp
    metrics:
    - name: Word Error Rate
      type: wer
      value:
      - Insert WER Value
    - name: Character Error Rate
      type: cer
      value:
      - Insert CER Value
---


# This speech tagger performs transcription, annotates entities, predict intent for SLURP dataset

Model is suitable for voiceAI applications.

## Model Details

- **Model type**: NeMo ASR
- **Architecture**: Conformer CTC
- **Language**: English
- **Training data**: Slurp dataset
- **Performance metrics**: [Metrics]

## Usage

To use this model, you need to install the NeMo library:

```bash
pip install nemo_toolkit
```

### How to run

```python
import nemo.collections.asr as nemo_asr

# Step 1: Load the ASR model from Hugging Face
model_name = 'WhissleAI/speech-tagger_en_slurp-iot'
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name)

# Step 2: Provide the path to your audio file
audio_file_path = '/path/to/your/audio_file.wav'

# Step 3: Transcribe the audio
transcription = asr_model.transcribe(paths2audio_files=[audio_file_path])
print(f'Transcription: {transcription[0]}')
```