print seed

notebooks/test_model.ipynb

@@ -74,7 +74,7 @@
     "\n",
     "#@markdown teticio/audio-diffusion-instrumental-hiphop-256 - trained on instrumental hiphop\n",
     "\n",
-    "model_id = \"teticio/audio-diffusion-256\"  #@param [\"teticio/audio-diffusion-256\", \"teticio/audio-diffusion-breaks-256\", \"audio-diffusion-instrumenal-hiphop-256\"]"
+    "model_id = \"teticio/audio-diffusion-instrumental-hiphop-256\"  #@param [\"teticio/audio-diffusion-256\", \"teticio/audio-diffusion-breaks-256\", \"audio-diffusion-instrumenal-hiphop-256\"]"
    ]
   },
   {
@@ -252,7 +252,7 @@
    },
    "outputs": [],
    "source": [
-    "start_step = 600  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
+    "start_step = 100  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
     "overlap_secs = 2  #@param {type:\"integer\"}\n",
     "mel.load_audio(audio_file)\n",
     "overlap_samples = overlap_secs * mel.get_sample_rate()\n",
@@ -260,6 +260,7 @@
     "stride = slice_size - overlap_samples\n",
     "generator = torch.Generator()\n",
     "seed = generator.seed()\n",
+    "print(f'Seed = {seed}')\n",
     "track = np.array([])\n",
     "not_first = 0\n",
     "for sample in range(len(mel.audio) // stride):\n",
@@ -300,7 +301,6 @@
     "_, (sample_rate,\n",
     "    audio2) = audio_diffusion.generate_spectrogram_and_audio_from_audio(\n",
     "        raw_audio=mel.get_audio_slice(slice),\n",
-    "        generator=generator,\n",
     "        mask_start_secs=1,\n",
     "        mask_end_secs=1)\n",
     "display(Audio(audio, rate=sample_rate))\n",