Update README.md

README.md

@@ -51,7 +51,7 @@ The model is available for use in the [NVIDIA NeMo](https://github.com/NVIDIA/Ne
 ### Inference
 For inference, you can follow our [Audio Codec Inference Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Audio_Codec_Inference.ipynb) which automatically downloads the model checkpoint. Note that you will need to set the ```model_name``` parameter to "nvidia/audio-codec-22khz".
 
-Alternatively, you can download the ```.nemo``` checkpoint from the "Files and versions" tab and use the code below to make an inference on the model:
+Alternatively, you can use the code below, which also handles the automatic checkpoint download:
 
 ```
 import librosa
@@ -86,7 +86,7 @@ sf.write(path_to_output_audio, output_audio, nemo_codec_model.sample_rate)
 
 ### Training
 
-For fine-tuning on another dataset please follow the steps available at our [Audio Codec Training Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Audio_Codec_Training.ipynb). Note that you will need to set the ```CONFIG_FILENAME``` parameter to the "audio_codec_22050.yaml" config. You also will need to set ```pretrained_model_name``` to "audio-codec-22khz".
+For fine-tuning on another dataset please follow the steps available at our [Audio Codec Training Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Audio_Codec_Training.ipynb). Note that you will need to set the ```CONFIG_FILENAME``` parameter to the "audio_codec_22050.yaml" config. You also will need to set ```pretrained_model_name``` to "nvidia/audio-codec-22khz".
 
 ## Training, Testing, and Evaluation Datasets: