# This speech tagger performs transcription for Hindi, annotates key entities, predict speaker age, dialiect and intent.

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/stt_hi_conformer_ctc_entities_age_dialiect_intent'
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]}')
```

Dataset is from AI4Bharat IndicVoices Hindi V1 and V2 dataset.

https://indicvoices.ai4bharat.org/