Update README.md

README.md

@@ -16,14 +16,27 @@ pip install nemo_toolkit['all']
 
 The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
 
-### Automatically load the model from NGC
+### Automatically instantiate the model
 
 ```python
 import nemo.collections.asr as nemo_asr
-asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_large")
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(repo_id="nvidia/stt_en_conformer_ctc_large",filename="stt_en_conformer_large.nemo")
+asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from(path)
+```
+
+### Transcribing using Python
+First, let's get a sample
+```
+wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
+```
+Then simply do:
+```
+asr_model.transcribe(['2086-149220-0033.wav'])
 ```
 
-### Transcribing text with this model
+### Transcribing many audio files
 
 ```shell
 python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \
@@ -105,11 +118,4 @@ Since this model was trained on publically available speech datasets, the perfor
 
 [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
 
-[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
-
-
-## Licence
-
-License to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).
-
-
+[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)