bin/train/easy_colab.ipynb

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        "# Neural Amp Modeler (\"Easy Mode\" Trainer)\n",
        "**Note**:\n",
        "This notebook is meant to be used on [Google Colab](https://colab.research.google.com/github/sdatkinson/neural-amp-modeler/blob/main/bin/train/easy_colab.ipynb).\n",
        "\n",
        "🔶**Before you run**🔶\n",
        "\n",
        "Make sure to get a GPU! (From the upper-left menu, click Runtime->Change runtime type->Select \"GPU\" from the \"Hardware accelerator dropdown menu)"
      ]
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        "## Step 1: Get data\n",
        "* **Download the reamp signal.** Here: [v3_0_0.wav](https://drive.google.com/file/d/1Pgf8PdE0rKB1TD4TRPKbpNo1ByR3IOm9/view?usp=drive_link).\n",
        "* **Reamp your gear.** Then reamp the gear you want to model using it. Save that reamp as \"output.wav\". *Note: Use 48kHz, 24-bit, mono.* For other sample rates, use [the CLI trainer](https://github.com/sdatkinson/neural-amp-modeler).\n",
        "* **Upload your files.** Upload the input (DI) and output (amped) files you want to use by clicking the Folder icon on the left ⬅ and then clicking the upload icon or by dragging the files into the panel."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Step 2: Train!\n",
        "Configure your training run below, then hit the Play button to start training!\n",
        "\n",
        "🕙NOTE: At default settings, training will take about 10 minutes.🕙"
      ]
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      "source": [
        "try:\n",
        "    import nam\n",
        "except ImportError as e:\n",
        "    print(\"Installing NAM into Colab. This should take under 2 minutes.\")\n",
        "    # Check what we're starting with (Issue 399)\n",
        "    !if [ ! -d logs ]; then mkdir logs; fi\n",
        "    !pip list > logs/packages.log\n",
        "    !pip install neural-amp-modeler > logs/install.log\n",
        "    # Hint: use the next line instead for the very latest!\n",
        "    # !pip install git+https://github.com/sdatkinson/neural-amp-modeler.git@main\n",
        "\n",
        "from nam.train.colab import run\n",
        "from nam.models.metadata import GearType, ToneType, UserMetadata\n",
        "\n",
        "%load_ext tensorboard\n",
        "\n",
        "from functools import partial\n",
        "\n",
        "import ipywidgets as widgets\n",
        "\n",
        "# NOTE: Enums need to be handled carefully since the values need to be supplied literally here!\n",
        "\n",
        "#@markdown # Training parameters\n",
        "epochs = 100 #@param {type: \"number\"}\n",
        "architecture = \"standard\"  #@param [\"standard\", \"lite\", \"feather\", \"nano\"] {type: \"string\"}\n",
        "latency_samples = \"auto\"  #@param {type: \"string\"}\n",
        "fit_cab = False  #@param {type: \"boolean\"}\n",
        "ignore_checks = False #@param {type: \"boolean\"}\n",
        "\n",
        "#@markdown # Metadata\n",
        "use_metadata = False #@param {type: \"boolean\"}\n",
        "name = \"My model\" #@param {type:\"string\"}\n",
        "modeled_by = \"Your name\" #@param {type:\"string\"}\n",
        "gear_make = \"GearCo\" #@param {type:\"string\"}\n",
        "gear_model = \"GearName\" #@param {type:\"string\"}\n",
        "gear_type = \"amp\" #@param [\"amp\", \"pedal\", \"pedal_amp\", \"amp_cab\", \"amp_pedal_cab\", \"preamp\", \"studio\"] {type:\"string\"}\n",
        "tone_type = \"clean\" #@param [\"clean\", \"overdrive\", \"crunch\", \"hi_gain\", \"fuzz\"] {type:\"string\"}\n",
        "\n",
        "def _verbose_enum(E, val):\n",
        "    try:\n",
        "        return E(val)\n",
        "    except ValueError as e:\n",
        "        raise ValueError(\n",
        "            str(e)\n",
        "            + \"\\nValid choices are: \"\n",
        "            + \", \".join(list(x.value for x in E))\n",
        "        )\n",
        "\n",
        "def _parse_latency(ls: str):\n",
        "    if ls.lower() == \"auto\":\n",
        "        return None\n",
        "    try:\n",
        "        return int(ls)\n",
        "    except ValueError as e:\n",
        "        raise ValueError(\n",
        "            f\"Invalid value for latency {ls} was given. Either use 'auto' or provide \"\n",
        "            f\"the reamp latency, in samples.\\nOriginal error:\\n\\n{e}\"\n",
        "        )\n",
        "\n",
        "user_metadata = None if not use_metadata else UserMetadata(\n",
        "    name=name,\n",
        "    modeled_by=modeled_by,\n",
        "    gear_make=gear_make,\n",
        "    gear_model=gear_model,\n",
        "    gear_type=_verbose_enum(GearType, gear_type.lower()),\n",
        "    tone_type=_verbose_enum(ToneType, tone_type.lower())\n",
        ")\n",
        "run_partial = partial(run, user_metadata=user_metadata)\n",
        "\n",
        "%tensorboard --logdir /content/lightning_logs\n",
        "run(\n",
        "    epochs=epochs,\n",
        "    architecture=architecture,\n",
        "    fit_cab=fit_cab,  # Change me to True for full-rig modeling!\n",
        "    ignore_checks=ignore_checks,  # Change to True to train anyways with potentially bad data\n",
        "    user_metadata=user_metadata,\n",
        "    delay=_parse_latency(latency_samples)\n",
        ")"
      ]
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        "## Step 3: Check the results and download your model\n",
        "We're done!\n",
        "\n",
        "Have a look at the plot above to see how your model compares to the real gear you're modeling.\n",
        "Hopefully it looks good!\n",
        "Go to the file browser on the left panel ⬅ and download `model.nam` from the `exported_model` directory (you may need to hit the refresh button).\n",
        "\n",
        "Additionally, if you want to continue to train this model later you can download the lightning model artifacts from `lightning_logs`. If not, that's fine too.\n",
        "\n",
        "# 🎸 **ENJOY!** 🎸"
      ]
    }
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