---
language: en
license: cc-by-4.0
tags:
  - automatic-speech-recognition
  - nemo
  - conformer
datasets:
  - slurp
metrics:
  - wer  # Word Error Rate
  - cer  # Character Error Rate
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]}')
```