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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "xeborcY1drYK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "94861099-b3c6-46c1-ae53-4e5f82e7aa6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nThis is an implementation of Speech to Text, a Large Language Model and a Text to Speech Generator.\\nIt should take a few minutes to download the models. Once loaded inference on GPU should be around 10 seconds.\\n\\nNotes;\\n\\n1. Under Runtime and Change Runtime Type, ensure Runtime is set to T4.\\n\\n2. Under Runtime, select Run all\\n\\n3. In the Gradio box\\n\\n    A) The slider controls the creativity vs precision of the responses.\\n    B) Click record to start and stop audio recording.\\n    C) Press submit to pass to the models.\\n    D) Press play on the right had side when the buttom appears.\\n    E) Enjoy\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "'''\n",
        "This is an implementation of Speech to Text, a Large Language Model and a Text to Speech Generator.\n",
        "It should take a few minutes to download the models. Once loaded inference on GPU should be around 10 seconds.\n",
        "\n",
        "Notes;\n",
        "\n",
        "1. Under Runtime and Change Runtime Type, ensure Runtime is set to T4.\n",
        "\n",
        "2. Under Runtime, select Run all\n",
        "\n",
        "3. In the Gradio box\n",
        "\n",
        "    A) The slider controls the creativity vs precision of the responses.\n",
        "    B) Click record to start and stop audio recording.\n",
        "    C) Press submit to pass to the models.\n",
        "    D) Press play on the right had side when the buttom appears.\n",
        "    E) Enjoy\n",
        "\n",
        "'''\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install libraries\n",
        "\n",
        "!pip install SentencePiece transformers datasets gradio==3.47.1 accelerate\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBsNkrRJgUPO",
        "outputId": "fe6a21e0-aae6-4dc4-ad91-82057b2b6819"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed SentencePiece-0.1.99 accelerate-0.24.1 aiofiles-23.2.1 datasets-2.15.0 dill-0.3.7 fastapi-0.104.1 ffmpy-0.3.1 gradio-3.47.1 gradio-client-0.6.0 h11-0.14.0 httpcore-1.0.2 httpx-0.25.1 multiprocess-0.70.15 orjson-3.9.10 pyarrow-hotfix-0.6 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 typing-extensions-4.8.0 uvicorn-0.24.0.post1 websockets-11.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n",
        "import soundfile as sf\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import time\n",
        "from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan\n",
        "from datasets import load_dataset\n",
        "import torch\n",
        "import gradio as gr\n",
        "from transformers import pipeline\n",
        "\n",
        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
        "int_device = 0 if device == \"cuda:0\" else -1\n",
        "\n",
        "\n",
        "# Load Speech to Text model\n",
        "\n",
        "transcriber = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\", device = int_device)\n",
        "\n",
        "\n",
        "# Load Large Language Model model\n",
        "\n",
        "tokenizer_lmm = AutoTokenizer.from_pretrained(\"smallcloudai/Refact-1_6B-fim\", trust_remote_code=True)\n",
        "model_mm = AutoModelForCausalLM.from_pretrained(\"smallcloudai/Refact-1_6B-fim\", trust_remote_code=True).to(device)\n",
        "\n",
        "prompt_template = \"<empty_output>SYSTEM {system}\\n\" \\\n",
        "            \"<empty_output>USER {query}\\n\" \\\n",
        "            \"<empty_output>ASSISTANT\"\n",
        "\n",
        "\n",
        "# Load Text to Speech Generator model\n",
        "\n",
        "synthesiser = pipeline(\"text-to-speech\", \"microsoft/speecht5_tts\", device = int_device)\n",
        "\n",
        "embeddings_dataset = load_dataset(\"Matthijs/cmu-arctic-xvectors\", split=\"validation\")\n",
        "speaker_embeddings = torch.tensor(embeddings_dataset[6800][\"xvector\"]).unsqueeze(0).to(device)\n",
        "\n",
        "print(f'Running with {device} -- {int_device}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "A new version of the following files was downloaded from https://huggingface.co/smallcloudai/Refact-1_6B-fim:\n",
            "- configuration_gpt_refact.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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            "A new version of the following files was downloaded from https://huggingface.co/smallcloudai/Refact-1_6B-fim:\n",
            "- modeling_gpt_refact.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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            ],
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              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
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          "text": [
            "Running with cpu -- -1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.signal import resample\n",
        "import torchaudio\n",
        "import soundfile as sf\n",
        "\n",
        "# Speech to Text Funciton\n",
        "\n",
        "def gen_stt(audio_input):\n",
        "\n",
        "    if type(audio_input) == str:\n",
        "\n",
        "      audio_array, sr = sf.read('/content/sample_audio.wav')\n",
        "\n",
        "    else:\n",
        "\n",
        "      sr, audio_array = audio_input\n",
        "      audio_array = audio_array.astype(np.float32)\n",
        "      audio_array /= np.max(np.abs(audio_array))\n",
        "\n",
        "    print('\\nActivating STT')\n",
        "    start_time = time.time()\n",
        "\n",
        "    transcription = transcriber({\"sampling_rate\": sr, \"raw\": audio_array})[\"text\"]\n",
        "\n",
        "    stop_time = time.time()\n",
        "    print(f'SST Done, Time {stop_time - start_time}')\n",
        "\n",
        "    print(transcription)\n",
        "\n",
        "    return transcription\n",
        "\n",
        "# Convert numbers to their alpha representation\n",
        "\n",
        "import re\n",
        "import inflect\n",
        "\n",
        "def convert_numbers_to_words(text):\n",
        "    # Initialize inflect engine\n",
        "    p = inflect.engine()\n",
        "\n",
        "    # Regular expression to find numbers\n",
        "    regex = r'\\d+'\n",
        "    matches = re.findall(regex, text)\n",
        "\n",
        "    # Replace numbers with their textual representations\n",
        "    for match in matches:\n",
        "        word_representation = p.number_to_words(int(match))\n",
        "        text = text.replace(match, word_representation)\n",
        "\n",
        "    new_string = text.replace('-', ' ')\n",
        "\n",
        "    return new_string\n",
        "\n",
        "\n",
        "# Large Language Model Funciton\n",
        "\n",
        "def gen_llm(text_inp, temp):\n",
        "\n",
        "    print('\\nActivating LLM')\n",
        "\n",
        "    start_time = time.time()\n",
        "\n",
        "    prompt = prompt_template.format(system=\"You are an AI assistant, keep your responses less than 30 words.\",\n",
        "                                    query = text_inp)\n",
        "\n",
        "    inputs = tokenizer_lmm.encode(prompt, return_tensors=\"pt\").to(device)\n",
        "    outputs = model_mm.generate(inputs, max_length=150, temperature= temp)\n",
        "    ten_gen_outs = tokenizer_lmm.decode(outputs[0], skip_special_tokens = True)\n",
        "\n",
        "    assistant_response = ten_gen_outs[ten_gen_outs.rfind(\"ASSISTANT\") + len(\"ASSISTANT\"):].strip()\n",
        "\n",
        "    converted_assistant_response = convert_numbers_to_words(assistant_response)\n",
        "\n",
        "    stop_time = time.time()\n",
        "\n",
        "    print(f'LLM Done, Time {stop_time - start_time}')\n",
        "    print(converted_assistant_response)\n",
        "\n",
        "    return converted_assistant_response\n",
        "\n",
        "\n",
        "# Text to Speech Generator Funciton\n",
        "\n",
        "def gen_tts(text_input):\n",
        "\n",
        "    print('\\nActivating TTS')\n",
        "    start_time = time.time()\n",
        "\n",
        "    speech = synthesiser(text_input, forward_params={\"speaker_embeddings\": speaker_embeddings})\n",
        "    scaled_array = (speech[\"audio\"] * np.iinfo(np.int16).max).astype(np.int16)\n",
        "\n",
        "    stop_time = time.time()\n",
        "    print(stop_time - start_time)\n",
        "    print(f'TTS Done, Time {stop_time - start_time}')\n",
        "\n",
        "    # return path\n",
        "    return 16000, scaled_array\n",
        "\n",
        "\n",
        "# # Converse Funciton\n",
        "\n",
        "def converse(audio_input, temp):\n",
        "\n",
        "    text_input = gen_stt(audio_input)\n",
        "\n",
        "    response = gen_llm(text_input, temp)\n",
        "\n",
        "    speech_array = gen_tts(response)\n",
        "\n",
        "    return speech_array"
      ],
      "metadata": {
        "id": "QdycfNorq-sQ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Launch Gradio\n",
        "#"
      ],
      "metadata": {
        "id": "1KGMUH_uE0M5"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "demo = gr.Interface(\n",
        "    converse,\n",
        "    [gr.Audio(source=\"microphone\"),\n",
        "    gr.Slider(0.01, 1, value=0.5, label=\"Precision vs Creativity\", info=\"Choose between 0 and 1\"),\n",
        "    ],\n",
        "    outputs = \"audio\",\n",
        ")\n",
        "\n",
        "demo.launch(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "oB0kFwghK2Rp",
        "outputId": "c6d2ec0f-0af2-4a72-b2c4-0749f16cf292"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://8a2889778354d40d80.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://8a2889778354d40d80.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
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          "metadata": {},
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a6QoAoD_EnXQ"
      },
      "execution_count": 6,
      "outputs": []
    }
  ]
}