Update README.md

README.md

@@ -59,11 +59,11 @@ import torch
 import soundfile as sf
 from nemo.collections.tts.models import AudioCodecModel
 
-codec_path = ??? # set here the model .nemo checkpoint path
+model_name = "nvidia/audio-codec-44khz"
 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()
+nemo_codec_model = AudioCodecModel.from_pretrained(model_name).eval()
 
 # get discrete tokens from audio
 audio, _ = librosa.load(path_to_input_audio, sr=nemo_codec_model.sample_rate)
@@ -72,10 +72,11 @@ 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)
+with torch.no_grad():
+  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()