{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Justmalhar/csm-google-collab/blob/main/Sesame_AI_CSM_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ww36-N-bgcnA"
      },
      "source": [
        "# Sesame AI - Google Collab CSM notebook\n",
        "\n",
        "Author: justmalhar\n",
        "\n",
        "http://x.com/justmalhar\n",
        "\n",
        "http://github.com/justmalhar\n",
        "\n",
        "HuggingFace Model - https://huggingface.co/sesame/csm-1b\n",
        "\n",
        "## Instructions\n",
        "\n",
        "Run each step below till you see the Gradio UI running\n",
        "\n",
        "\n",
        "## Steps:\n",
        "1. Setup Gradio\n",
        "2. Clone the repo and install the requirments\n",
        "3. Login with HuggingFace account to access the model\n",
        "4. Run all steps till you see the Gradio UI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6S72oH6hTd5",
        "outputId": "cd15a8fb-2033-49a5-8243-96ca208a7385"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "Downloading safehttpx-0.1.6-py3-none-any.whl (8.7 kB)\n",
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            "Installing collected packages: pydub, mpmath, typing-extensions, tomlkit, sympy, shellingham, semantic-version, safetensors, ruff, python-multipart, orjson, networkx, groovy, fsspec, filelock, ffmpy, annotated-types, aiofiles, uvicorn, torch, starlette, pydantic-core, huggingface_hub, typer, torchaudio, tokenizers, safehttpx, pydantic, gradio-client, transformers, fastapi, gradio\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.10.0\n",
            "    Uninstalling typing_extensions-4.10.0:\n",
            "      Successfully uninstalled typing_extensions-4.10.0\n",
            "Successfully installed aiofiles-23.2.1 annotated-types-0.7.0 fastapi-0.115.11 ffmpy-0.5.0 filelock-3.18.0 fsspec-2025.3.0 gradio-5.22.0 gradio-client-1.8.0 groovy-0.1.2 huggingface_hub-0.29.3 mpmath-1.3.0 networkx-3.4.2 orjson-3.10.15 pydantic-2.10.6 pydantic-core-2.27.2 pydub-0.25.1 python-multipart-0.0.20 ruff-0.11.2 safehttpx-0.1.6 safetensors-0.5.3 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.46.1 sympy-1.13.1 tokenizers-0.21.1 tomlkit-0.13.2 torch-2.6.0 torchaudio-2.6.0 transformers-4.50.0 typer-0.15.2 typing-extensions-4.12.2 uvicorn-0.34.0\n"
          ]
        }
      ],
      "source": [
        "# Reinstall required dependencies\n",
        "!pip install torch torchaudio transformers huggingface_hub gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssphqLJugbrI"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ObK_fhW7iCAn",
        "outputId": "a483389f-82f3-4d07-e1b5-6cc4c73bae21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "c:\\Users\\PALLAVI\\csm-google-collab\\csm-google-collab\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning into 'csm-google-collab'...\n",
            "c:\\Users\\PALLAVI\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\magics\\osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
            "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
          ]
        }
      ],
      "source": [
        "# Clone the Sesame CSM repository\n",
        "!git clone https://github.com/Justmalhar/csm-google-collab.git\n",
        "%cd csm-google-collab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "cr1XnB10Zf4z",
        "outputId": "d5d175fe-6c10-4b6d-9ffb-d40b29adcd04"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ERROR: Could not open requirements file: [Errno 2] No such file or directory: '/content/csm/requirements.txt'\n"
          ]
        }
      ],
      "source": [
        "# Install dependencies from requirements.txt\n",
        "!pip install -r /content/csm/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "YV8hf1Y7iEl0"
      },
      "outputs": [],
      "source": [
        "#Add the repository to Python's path\n",
        "import sys\n",
        "sys.path.append(\"/content/csm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "id": "8AvFx6f4iMSW"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\PALLAVI\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\huggingface_hub\\utils\\_deprecation.py:38: FutureWarning: Deprecated positional argument(s) used in 'notebook_login': pass new_session='' as keyword args. From version 1.0 passing these as positional arguments will result in an error,\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Authenticate with Hugging Face\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login(\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "collapsed": true,
        "id": "3CsTqgqZiUls",
        "outputId": "8966902a-5d4d-4ba4-f8f7-12e6830cf676"
      },
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "sesame/csm-1b does not appear to have a file named pytorch_model.bin, model.safetensors, tf_model.h5, model.ckpt or flax_model.msgpack.",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[8], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m config \u001b[38;5;241m=\u001b[39m AutoConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msesame/csm-1b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# Load the model with the configuration\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msesame/csm-1b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m model\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mbfloat16)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel loaded successfully! 🎉\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[1;32mc:\\Users\\PALLAVI\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:573\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    571\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m    572\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[1;32m--> 573\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    574\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    575\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    576\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    577\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    578\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    579\u001b[0m )\n",
            "File \u001b[1;32mc:\\Users\\PALLAVI\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\transformers\\modeling_utils.py:272\u001b[0m, in \u001b[0;36mrestore_default_torch_dtype.<locals>._wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    270\u001b[0m old_dtype \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mget_default_dtype()\n\u001b[0;32m    271\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 272\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    273\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    274\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_default_dtype(old_dtype)\n",
            "File \u001b[1;32mc:\\Users\\PALLAVI\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\transformers\\modeling_utils.py:4317\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m   4312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gguf_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   4313\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   4314\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou cannot combine Quantization and loading a model from a GGUF file, try again by making sure you did not passed a `quantization_config` or that you did not load a quantized model from the Hub.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   4315\u001b[0m     )\n\u001b[1;32m-> 4317\u001b[0m checkpoint_files, sharded_metadata \u001b[38;5;241m=\u001b[39m \u001b[43m_get_resolved_checkpoint_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   4318\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4319\u001b[0m \u001b[43m    \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvariant\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariant\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgguf_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgguf_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4322\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrom_tf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_tf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_flax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4324\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_safetensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_safetensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4326\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4327\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4329\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4330\u001b[0m \u001b[43m    \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4331\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4332\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4333\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4335\u001b[0m is_sharded \u001b[38;5;241m=\u001b[39m sharded_metadata \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   4336\u001b[0m is_quantized \u001b[38;5;241m=\u001b[39m hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "File \u001b[1;32mc:\\Users\\PALLAVI\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\transformers\\modeling_utils.py:1130\u001b[0m, in \u001b[0;36m_get_resolved_checkpoint_files\u001b[1;34m(pretrained_model_name_or_path, subfolder, variant, gguf_file, from_tf, from_flax, use_safetensors, cache_dir, force_download, proxies, local_files_only, token, user_agent, revision, commit_hash)\u001b[0m\n\u001b[0;32m   1124\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[0;32m   1125\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpretrained_model_name_or_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not appear to have a file named\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1126\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_add_variant(WEIGHTS_NAME,\u001b[38;5;250m \u001b[39mvariant)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but there is a file without the variant\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1127\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvariant\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Use `variant=None` to load this model from those weights.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1128\u001b[0m                 )\n\u001b[0;32m   1129\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1130\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[0;32m   1131\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpretrained_model_name_or_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not appear to have a file named\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1132\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_add_variant(WEIGHTS_NAME,\u001b[38;5;250m \u001b[39mvariant)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_add_variant(SAFE_WEIGHTS_NAME,\u001b[38;5;250m \u001b[39mvariant)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1133\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mTF2_WEIGHTS_NAME\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mTF_WEIGHTS_NAME\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mFLAX_WEIGHTS_NAME\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1134\u001b[0m                 )\n\u001b[0;32m   1136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m:\n\u001b[0;32m   1137\u001b[0m     \u001b[38;5;66;03m# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted\u001b[39;00m\n\u001b[0;32m   1138\u001b[0m     \u001b[38;5;66;03m# to the original exception.\u001b[39;00m\n\u001b[0;32m   1139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
            "\u001b[1;31mOSError\u001b[0m: sesame/csm-1b does not appear to have a file named pytorch_model.bin, model.safetensors, tf_model.h5, model.ckpt or flax_model.msgpack."
          ]
        }
      ],
      "source": [
        "# Load the Model\n",
        "from transformers import AutoModel, AutoConfig\n",
        "import torch\n",
        "\n",
        "# Load the configuration\n",
        "config = AutoConfig.from_pretrained(\"sesame/csm-1b\")\n",
        "\n",
        "# Load the model with the configuration\n",
        "model = AutoModel.from_pretrained(\"sesame/csm-1b\", config=config)\n",
        "model.to(device=\"cuda\", dtype=torch.bfloat16)\n",
        "print(\"Model loaded successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mdpfkA6qiZaZ"
      },
      "outputs": [],
      "source": [
        "# Define the generate_audio function (replace with your actual implementation)\n",
        "def generate_audio(text, speaker_id):\n",
        "    \"\"\"\n",
        "    Placeholder function for generating audio from text.\n",
        "    Replace this with your actual implementation.\n",
        "    \"\"\"\n",
        "    # Simulate audio generation\n",
        "    print(f\"Generating audio for text: {text} with speaker ID: {speaker_id}\")\n",
        "    # Return a placeholder audio file path\n",
        "    return \"/content/dummy_audio.wav\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Whv2TT-CdFBq"
      },
      "outputs": [],
      "source": [
        "# Define the Gradio interface using Blocks API\n",
        "import gradio as gr\n",
        "\n",
        "with gr.Blocks(title=\"Sesame CSM-1B Text-to-Speech\") as demo:\n",
        "    gr.Markdown(\"# 🎙️ Sesame CSM-1B Text-to-Speech\")\n",
        "    gr.Markdown(\"Generate high-quality audio from text using the Sesame CSM-1B model.\")\n",
        "\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            # Text input\n",
        "            text_input = gr.Textbox(label=\"Enter Text\", placeholder=\"Type your text here...\", lines=5)\n",
        "\n",
        "            # Speaker selection\n",
        "            speaker_dropdown = gr.Dropdown(\n",
        "                choices=[\"Speaker 0\", \"Speaker 1\", \"Speaker 2\", \"Speaker 3\"],  # Add more options if needed\n",
        "                label=\"Select Speaker\",\n",
        "                value=\"Speaker 0\"\n",
        "            )\n",
        "\n",
        "            # File upload for text\n",
        "            file_upload = gr.File(label=\"Or Upload a Text File\", file_types=[\".txt\"])\n",
        "\n",
        "            # Generate and Cancel buttons\n",
        "            with gr.Row():\n",
        "                generate_button = gr.Button(\"Generate Audio 🎵\")\n",
        "                cancel_button = gr.Button(\"Cancel ❌\")\n",
        "\n",
        "        with gr.Column():\n",
        "            # Audio output\n",
        "            audio_output = gr.Audio(label=\"Generated Audio\", interactive=False)\n",
        "\n",
        "            # Playback controls\n",
        "            with gr.Row():\n",
        "                play_button = gr.Button(\"▶️ Play\")\n",
        "                pause_button = gr.Button(\"⏸️ Pause\")\n",
        "                stop_button = gr.Button(\"⏹️ Stop\")\n",
        "\n",
        "            # Volume control\n",
        "            volume_slider = gr.Slider(minimum=0, maximum=100, value=50, label=\"Volume\")\n",
        "\n",
        "    # Define interactions\n",
        "    def process_input(text, file, speaker, cancel_flag):\n",
        "        if cancel_flag:\n",
        "            return \"Process canceled by user.\"\n",
        "\n",
        "        if file is not None:\n",
        "            # Read text from the uploaded file\n",
        "            with open(file.name, \"r\") as f:\n",
        "                text = f.read()\n",
        "\n",
        "        # Get speaker ID from the dropdown\n",
        "        speaker_id = int(speaker.split()[-1])\n",
        "\n",
        "        # Generate audio\n",
        "        audio_file = generate_audio(text, speaker_id)\n",
        "        return audio_file\n",
        "\n",
        "    # Link inputs and outputs\n",
        "    generate_button.click(\n",
        "        fn=process_input,\n",
        "        inputs=[text_input, file_upload, speaker_dropdown, cancel_button],\n",
        "        outputs=audio_output\n",
        "    )\n",
        "\n",
        "    # Cancel button logic\n",
        "    cancel_button.click(\n",
        "        fn=lambda: \"Process canceled by user.\",\n",
        "        inputs=None,\n",
        "        outputs=audio_output\n",
        "    )\n",
        "\n",
        "    # Playback controls (placeholders, as Gradio Audio already has built-in controls)\n",
        "    play_button.click(fn=lambda: None)\n",
        "    pause_button.click(fn=lambda: None)\n",
        "    stop_button.click(fn=lambda: None)\n",
        "\n",
        "# Launch the Gradio app\n",
        "demo.launch(share=True)  # Set `share=True` to get a public link"
      ]
    }
  ],
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