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README.md

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+# 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/
+