Update README.md

README.md

@@ -50,7 +50,40 @@ For more details please check [our paper](https://arxiv.org/abs/2409.12117).
 The model is available for use in the [NVIDIA NeMo](https://github.com/NVIDIA/NeMo), and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. 
 
 ### Inference
-For inference please follow our [Audio Codec Inference Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Audio_Codec_Inference.ipynb). Note that you will need to set the ```model_name``` parameter to "audio_codec_low_frame_rate_22khz".
+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 "audio_codec_low_frame_rate_22khz".
+
+Alternatively, you can manually download the [checkpoint]() and use the code below to do an inference on the model:
+
+
+```
+import librosa
+import torch
+import soundfile as sf
+from nemo.collections.tts.models import AudioCodecModel
+
+codec_path = ??? # set here the model .nemo checkpoint path
+path_to_input_audio = ??? # path of the input audio
+path_to_output_audio = ??? # path of the reconstructed output audio
+
+nemo_codec_model = AudioCodecModel.restore_from(restore_path=codec_path, map_location="cpu").eval()
+
+# get discrete tokens from audio
+audio, _ = librosa.load(path_to_input_audio, sr=nemo_codec_model.sample_rate)
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+audio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)
+audio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)
+
+encoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)
+
+# Reconstruct audio from tokens
+reconstructed_audio, _ = nemo_codec_model.decode(tokens=encoded_tokens, tokens_len=encoded_len)
+
+# save reconstructed audio
+output_audio = reconstructed_audio.cpu().numpy().squeeze()
+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_low_frame_rate_22050.yaml" config. You also will need to set ```pretrained_model_name``` to "audio_codec_low_frame_rate_22khz".