Llama3_1_S_v0_2_Inference.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/jonbaer/googlecolab/blob/master/Llama3_1_S_v0_2_Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "oUqUkptW4SIA"
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      "source": [
        "# A Sample Inference Code for LLAMA3.1-S-v0.2: A Speech Multimodal Model That Natively Understanding Audio and Text Input\n",
        "<div class=\"align-center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/janhq/llama3-s/main/images/llama-listen.jpg\" width=\"200\"></a>\n",
        "  <p><small>Image source: <a href=\"https://www.amazon.co.uk/When-Llama-Learns-Listen-Feelings/dp/1839237988\">\"When Llama Learns to Listen\"</a></small></p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KvesFci4stp"
      },
      "source": [
        "## Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hk6_z2lipub6",
        "outputId": "ac00a890-8efc-4cf6-f373-4f3264d7ec35"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
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          "execution_count": 1
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      "source": [
        "%%shell\n",
        "pip install -q openai-whisper==20231117 IPython matplotlib vector_quantize_pytorch webdataset\n",
        "pip install -q git+https://github.com/homebrewltd/WhisperSpeech.git\n",
        "pip install -q -U transformers bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HnXzI1k3pVEU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchaudio\n",
        "from whisperspeech.vq_stoks import RQBottleneckTransformer\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline\n",
        "from huggingface_hub import hf_hub_download\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QlS4FXO3wr9"
      },
      "source": [
        "## Download a sound requesting our model to code a random python script"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ptShpidIrILl",
        "colab": {
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        "outputId": "e6375325-6a1b-48eb-cb57-2661814acc24"
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      "outputs": [
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          "text": [
            "--2024-08-23 11:41:30--  https://docs.google.com/uc?export=download&id=1xwVCMtfDb_eRhuSSSP-_6SAiClQNZ9xp\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.197.100, 74.125.197.101, 74.125.197.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.197.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://drive.usercontent.google.com/download?id=1xwVCMtfDb_eRhuSSSP-_6SAiClQNZ9xp&export=download [following]\n",
            "--2024-08-23 11:41:30--  https://drive.usercontent.google.com/download?id=1xwVCMtfDb_eRhuSSSP-_6SAiClQNZ9xp&export=download\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.117.132, 2607:f8b0:400e:c03::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.117.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 60972 (60K) [audio/wav]\n",
            "Saving to: ‘codeapythonscript.wav’\n",
            "\n",
            "codeapythonscript.w 100%[===================>]  59.54K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2024-08-23 11:41:33 (98.1 MB/s) - ‘codeapythonscript.wav’ saved [60972/60972]\n",
            "\n",
            "--2024-08-23 11:41:33--  https://drive.google.com/uc?export=download&id=1IShlXCiNrY0QBs7TeKxOH2zoh3IzXRrF\n",
            "Resolving drive.google.com (drive.google.com)... 172.253.117.113, 172.253.117.138, 172.253.117.100, ...\n",
            "Connecting to drive.google.com (drive.google.com)|172.253.117.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://drive.usercontent.google.com/download?id=1IShlXCiNrY0QBs7TeKxOH2zoh3IzXRrF&export=download [following]\n",
            "--2024-08-23 11:41:33--  https://drive.usercontent.google.com/download?id=1IShlXCiNrY0QBs7TeKxOH2zoh3IzXRrF&export=download\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.117.132, 2607:f8b0:400e:c03::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.117.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 41516 (41K) [audio/wav]\n",
            "Saving to: ‘writeastory.wav’\n",
            "\n",
            "writeastory.wav     100%[===================>]  40.54K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-08-23 11:41:34 (101 MB/s) - ‘writeastory.wav’ saved [41516/41516]\n",
            "\n"
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      "source": [
        "%%shell\n",
        "wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1xwVCMtfDb_eRhuSSSP-_6SAiClQNZ9xp' -O codeapythonscript.wav\n",
        "wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1IShlXCiNrY0QBs7TeKxOH2zoh3IzXRrF' -O writeastory.wav"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UEXAlp6N4CjV"
      },
      "source": [
        "## First, we need to convert the audio file to sound tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yUzExSUQpOdB"
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      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "if not os.path.exists(\"whisper-vq-stoks-medium-en+pl-fixed.model\"):\n",
        "    hf_hub_download(\n",
        "        repo_id=\"jan-hq/WhisperVQ\",\n",
        "        filename=\"whisper-vq-stoks-medium-en+pl-fixed.model\",\n",
        "        local_dir=\".\",\n",
        "    )\n",
        "vq_model = RQBottleneckTransformer.load_model(\n",
        "        \"whisper-vq-stoks-medium-en+pl-fixed.model\"\n",
        "    ).to(device)\n",
        "def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device=device):\n",
        "    vq_model.ensure_whisper(device)\n",
        "\n",
        "    wav, sr = torchaudio.load(audio_path)\n",
        "    if sr != 16000:\n",
        "        wav = torchaudio.functional.resample(wav, sr, 16000)\n",
        "    with torch.no_grad():\n",
        "        codes = vq_model.encode_audio(wav.to(device))\n",
        "        codes = codes[0].cpu().tolist()\n",
        "\n",
        "    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)\n",
        "    return f'<|sound_start|>{result}<|sound_end|>'\n",
        "\n",
        "def audio_to_sound_tokens_transcript(audio_path, target_bandwidth=1.5, device=device):\n",
        "    vq_model.ensure_whisper(device)\n",
        "\n",
        "    wav, sr = torchaudio.load(audio_path)\n",
        "    if sr != 16000:\n",
        "        wav = torchaudio.functional.resample(wav, sr, 16000)\n",
        "    with torch.no_grad():\n",
        "        codes = vq_model.encode_audio(wav.to(device))\n",
        "        codes = codes[0].cpu().tolist()\n",
        "\n",
        "    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)\n",
        "    return f'<|reserved_special_token_69|><|sound_start|>{result}<|sound_end|>'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INNrP9vz4JBZ"
      },
      "source": [
        "## Then, we can inference the model the same as any other LLM."
      ]
    },
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "Unused kwargs: ['bnb_8bit_compute_dtype', 'bnb_8bit_use_double_quant']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
          ]
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          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
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      "source": [
        "def setup_pipeline(model_path, use_4bit=False, use_8bit=False):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "\n",
        "    model_kwargs = {\"device_map\": \"auto\"}\n",
        "\n",
        "    if use_4bit:\n",
        "        model_kwargs[\"quantization_config\"] = BitsAndBytesConfig(\n",
        "            load_in_4bit=True,\n",
        "            bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "            bnb_4bit_use_double_quant=True,\n",
        "            bnb_4bit_quant_type=\"nf4\",\n",
        "        )\n",
        "    elif use_8bit:\n",
        "        model_kwargs[\"quantization_config\"] = BitsAndBytesConfig(\n",
        "            load_in_8bit=True,\n",
        "            bnb_8bit_compute_dtype=torch.bfloat16,\n",
        "            bnb_8bit_use_double_quant=True,\n",
        "        )\n",
        "    else:\n",
        "        model_kwargs[\"torch_dtype\"] = torch.bfloat16\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)\n",
        "\n",
        "    return pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
        "\n",
        "def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False):\n",
        "    generation_args = {\n",
        "        \"max_new_tokens\": max_new_tokens,\n",
        "        \"return_full_text\": False,\n",
        "        \"temperature\": temperature,\n",
        "        \"do_sample\": do_sample,\n",
        "    }\n",
        "\n",
        "    output = pipe(messages, **generation_args)\n",
        "    return output[0]['generated_text']\n",
        "\n",
        "# Usage\n",
        "llm_path = \"homebrewltd/llama3.1-s-instruct-v0.2\"\n",
        "pipe = setup_pipeline(llm_path, use_8bit=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CWl_BmnhFUun"
      },
      "source": [
        "### Code generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tihXrDivD9Bk",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "abbab960-60da-43dd-86d0-a7d5f95ba0c8"
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      "outputs": [
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "# Model Output:  Sure, here is a simple Python script that uses the `pandas` library to read a CSV file and then writes it to a SQL database using `sqlalchemy`.\n",
            "\n",
            "```python\n",
            "import pandas as pd\n",
            "from sqlalchemy import create_engine\n",
            "\n",
            "# Read the CSV file\n",
            "df = pd.read_csv('your_file.csv')\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Usage\n",
        "sound_tokens = audio_to_sound_tokens(\"codeapythonscript.wav\")\n",
        "\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": sound_tokens},\n",
        "]\n",
        "generated_text = generate_text(pipe, messages)\n",
        "\n",
        "print(\"-\"*50)\n",
        "print(\"# Model Output: \", generated_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNFerZYZFYuI"
      },
      "source": [
        "### Story creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rjo0rItwF3BT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e452851-1e70-4220-d045-7f0538b9d5d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "# Model Output:  Once upon a time, in a small village nestled between the mountains and the sea, lived a young girl named Lily. She was known for her radiant smile and her kind heart. Lily had a unique gift; she could communicate with animals. This gift was a secret she kept hidden from the villagers, fearing they would not\n"
          ]
        }
      ],
      "source": [
        "# Usage\n",
        "sound_tokens = audio_to_sound_tokens(\"writeastory.wav\")\n",
        "\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": sound_tokens},\n",
        "]\n",
        "generated_text = generate_text(pipe, messages)\n",
        "\n",
        "print(\"-\"*50)\n",
        "print(\"# Model Output: \", generated_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We can also make model transcripe the audio by adding the \"<|reserved_special_token_69|>\" token\n"
      ],
      "metadata": {
        "id": "rpIz8489Y8aE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sound_tokens = audio_to_sound_tokens_transcript(\"writeastory.wav\")\n",
        "\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": sound_tokens},\n",
        "]\n",
        "generated_text = generate_text(pipe, messages)\n",
        "\n",
        "print(\"-\"*50)\n",
        "print(\"# Model Output: \", generated_text)"
      ],
      "metadata": {
        "id": "NP7mFHmLZHXl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "# Model Output:  Write a story\n"
          ]
        }
      ]
    }
  ],
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