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:
pip install nemo_toolkit
How to run
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]}')