---
language:
- en
license: cc-by-nc-nd-4.0
library_name: nemo
datasets:
- commonvoice
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- CTC
- named-entity-recognition
- emotion-classification
- Transformer
- NeMo
- pytorch
model-index:
- name: 1step_ctc_ner_emotion_commonvoice500hrs
  results: []

---
# This speech tagger performs transcription, annotates entities, predict speaker emotion 

Model is suitable for voiceAI applications, real-time and offline.

## Model Details

- **Model type**: NeMo ASR
- **Architecture**: Conformer CTC
- **Language**: English
- **Training data**: CommonVoice, Gigaspeech
- **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_ner_emotion'
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]}')
```