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---
license: cc-by-4.0
language:
- hi
base_model:
- parthiv11/stt_hi_conformer_ctc_large_v2
tags:
- speech_recognition
- entity_tagging
- dialect_prediction
- gender
- age
- intent
---
# 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/ |