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---
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]}')
``` |