📝 update songstarter post

posts/training_musicgen_songstarter.ipynb

@@ -78,7 +78,7 @@
     "\n",
     "#### Initial experiments with fine-tuning on artists\n",
     "\n",
-    "After some exploration, I figured out how to get training running using their codebase, and prepared a dataset of samples from a few artists and started training for the text-to-music task, starting from the [musicgen-small](https://huggingface.co/facebook/musicgen-small) checkpoint as that was the largest model I could fit on a Google Colab A100 40gb GPU. For the dataset, since I didn't have text captions for the music, I set the captions to be the same every time: \"an electronic instrumental in the style of <artist>\".\n",
+    "After some exploration, I figured out how to get training running using their codebase, and prepared a dataset of samples from a few artists and started training for the text-to-music task, starting from the [musicgen-small](https://huggingface.co/facebook/musicgen-small) checkpoint as that was the largest model I could fit on a Google Colab A100 40gb GPU. For the dataset, since I didn't have text captions for the music, I set the captions to be the same every time: \"an electronic instrumental in the style of \\<artist\\>\".\n",
     "\n",
     "The results were...okay. I had to generate a lot of samples to get some good ones out...most were riddled with artifacts/noise/strange sounds/silence. I also was plagued by a sneaking suspicion that the model was overfitting and just regenerating existing tracks. Here are some clips from the first model I trained, where I used a dataset of songs by [Monte Booker](https://soundcloud.com/montebooker):\n",
     "\n",