---
license: apache-2.0
datasets:
- ai4bharat/IndicVoices-ST
language:
- hi
metrics:
- wer
base_model:
- nvidia/stt_en_fastconformer_hybrid_large_streaming_multi
---

# Model Card for Aibharath-FastConformer-Hindi-ASR

This model is a fine-tuned version of **STT En FastConformer Hybrid Large Streaming** from NVIDIA's NeMo framework, specifically optimized for **Hindi automatic speech recognition (ASR)**. The model has been fine-tuned on the **Aibharath Hindi dataset** to enhance transcription accuracy for Hindi speech, including real-time streaming applications.

## Model Details

### Model Description

- **Model type:** Hybrid ASR model (CTC + Attention)
- **Model Sample Rate:** 16000 hz
- **Language(s) (NLP):** Hindi (`hi`)  
- **License:** Apache-2.0  
- **Finetuned from model [optional]:** `nvidia/stt_en_fastconformer_hybrid_large_streaming_multi`  



## **How to Get Started with the Model**

You can load and use the model with the following code:

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="salesken/Hindi-FastConformer-Streaming-ASR")

# Optional: change the default latency. Default latency is 1040ms. Supported latencies: {0: 0ms, 1: 80ms, 16: 480ms, 33: 1040ms}.
# Note: These are the worst latency and average latency would be half of these numbers.
asr_model.encoder.set_default_att_context_size([70,13]) 

#Optional: change the default decoder. Default decoder is Transducer (RNNT). Supported decoders: {ctc, rnnt}.
asr_model.change_decoding_strategy(decoder_type='rnnt')

asr_model.transcribe(['2086-149220-0033.wav'])