README.md

metadata

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:

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]}')