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{
"cells": [
{
"cell_type": "markdown",
"source": [
"## Prerequisites"
],
"metadata": {
"id": "w4LtdMb23tZ4"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "JgJLBIh3fm-W"
},
"source": [
"### Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EodUpreufqD-"
},
"outputs": [],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bOn11huvfuXc"
},
"outputs": [],
"source": [
"!pip install --upgrade --quiet pip\n",
"!pip install --quiet git+https://github.com/huggingface/transformers.git"
]
},
{
"cell_type": "code",
"source": [
"!pip install typing-extensions==4.5.0\n",
"!pip install python-multipart\n",
"!pip install kaleido\n",
"!pip install notebook>=6.5.5\n",
"!pip install click>=8.0\n",
"!pip install fastapi\n",
"!pip install \"uvicorn[standard]\"\n",
"!pip install pyngrok"
],
"metadata": {
"id": "Nl0CQxwHCrFd"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ROxnljVbf6_o"
},
"source": [
"### Load the models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ToxW1gbLf6tr"
},
"outputs": [],
"source": [
"from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed\n",
"\n",
"model = MusicgenForConditionalGeneration.from_pretrained(\"facebook/musicgen-small\")\n",
"processor = MusicgenProcessor.from_pretrained(\"facebook/musicgen-small\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "omP9Hg1ajUKM"
},
"outputs": [],
"source": [
"import torch\n",
"from IPython.display import Audio\n",
"\n",
"sampling_rate = model.config.audio_encoder.sampling_rate\n",
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"model.to(device)\n",
"None"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "852jZKSqiKoT"
},
"source": [
"## Music Generation functionality"
]
},
{
"cell_type": "markdown",
"source": [
"#### Model Class"
],
"metadata": {
"id": "8nydshMdxKab"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import typing\n",
"\n",
"class AudioPalette:\n",
" def __init__(self):\n",
" pass\n",
"\n",
" def set_prompt(self, caption: str | typing.List[str]):\n",
" self.caption = caption\n",
"\n",
" def generate(self):\n",
" if isinstance(self.caption, str):\n",
" return self.generate_single(max_new_tokens=1024)\n",
" else:\n",
" return self.generate_multiple()\n",
"\n",
" def generate_single(self, prompt=None, max_new_tokens=512):\n",
" if not prompt:\n",
" prompt = self.caption\n",
" inputs = processor(\n",
" text=[prompt],\n",
" padding=True,\n",
" return_tensors=\"pt\",\n",
" sampling_rate=sampling_rate\n",
" )\n",
"\n",
" audio_values = model.generate(**inputs.to(device), do_sample=True, guidance_scale=3, max_new_tokens=max_new_tokens)\n",
" return audio_values\n",
"\n",
" def generate_audio_with_melody_conditioning(self, prompt, melody, max_new_tokens=256):\n",
" inputs = processor(\n",
" text=[prompt],\n",
" audio=melody[0, 0].cpu().numpy(),\n",
" padding=True,\n",
" return_tensors=\"pt\",\n",
" sampling_rate=sampling_rate\n",
" )\n",
"\n",
" # set_seed(1)\n",
" audio_values = model.generate(**inputs.to(device), do_sample=True, guidance_scale=3, max_new_tokens=max_new_tokens)\n",
" return audio_values\n",
"\n",
" def generate_multiple(self):\n",
" for idx, prompt in enumerate(self.caption):\n",
" if idx == 0:\n",
" audio = self.generate_single(prompt, 256)\n",
" else:\n",
" audio = self.generate_audio_with_melody_conditioning(prompt, audio)\n",
" return audio"
],
"metadata": {
"id": "4V49E7xpxNPu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"audiopalette = AudioPalette()"
],
"metadata": {
"id": "qW65Q68o-R7f"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "P3OmxnaBA9E-"
},
"source": [
"#### API Creation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Dxlw3ODsTtuB"
},
"outputs": [],
"source": [
"from fastapi import FastAPI\n",
"from pydantic import BaseModel, Field\n",
"from fastapi.middleware.cors import CORSMiddleware\n",
"\n",
"app = FastAPI()\n",
"\n",
"app.add_middleware(\n",
" CORSMiddleware,\n",
" allow_origins=['*'],\n",
" allow_credentials=True,\n",
" allow_methods=['*'],\n",
" allow_headers=['*'],\n",
")"
]
},
{
"cell_type": "code",
"source": [
"import typing\n",
"import numpy as np\n",
"\n",
"class Prompt(BaseModel):\n",
" caption: str | typing.List[str]\n",
"\n",
"class FileData(BaseModel):\n",
" file_path: str\n",
"\n",
"# class Melody(BaseModel):\n",
"# audio: np.ndarray\n",
"\n",
"# class Config:\n",
"# arbitrary_types_allowed = True"
],
"metadata": {
"id": "iYUH3-GpfbN8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PTQCdon0A9FA"
},
"outputs": [],
"source": [
"import tempfile\n",
"import scipy\n",
"\n",
"from fastapi.responses import FileResponse\n",
"\n",
"@app.get('/')\n",
"async def root():\n",
" return {\"message\": \"Hello World\"}\n",
"\n",
"@app.post('/download')\n",
"async def download(file_data: FileData):\n",
" file_path = file_data.file_path\n",
" return FileResponse(file_path)\n",
"\n",
"@app.post('/generate')\n",
"async def gen_music(prompt: Prompt):\n",
" audiopalette.set_prompt(prompt.caption)\n",
" audio = audiopalette.generate()\n",
"\n",
" file_path = None\n",
" with tempfile.NamedTemporaryFile(delete=False) as f:\n",
" scipy.io.wavfile.write(f, rate=sampling_rate, data=audio[0, 0].cpu().numpy())\n",
" file_path = f.name\n",
"\n",
" if not file_path:\n",
" return {\"error\": \"There has been an error\"}\n",
" return {\"file_path\": f\"{file_path}\"}\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ALpNtVpHA9FA"
},
"source": [
"#### Run the API"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "w3eG8rfRA9FB"
},
"outputs": [],
"source": [
"from getpass import getpass\n",
"\n",
"import nest_asyncio\n",
"import uvicorn\n",
"from pyngrok import ngrok"
]
},
{
"cell_type": "code",
"source": [
"ngrok_auth_token = getpass(prompt=\"Enter ngrok auth token: \")\n",
"ngrok.set_auth_token(ngrok_auth_token)"
],
"metadata": {
"id": "QFDDncCJEs4f"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yOhMLxA5A9FB"
},
"outputs": [],
"source": [
"ngrok_tunnel = ngrok.connect(8000)\n",
"print(\"Public URL:\", ngrok_tunnel.public_url)\n",
"nest_asyncio.apply()\n",
"uvicorn.run(app, port=8000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "606pRql4A9FC"
},
"source": [
"#### Kill ngrok Connection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "k7Tbq8w-A9FC"
},
"outputs": [],
"source": [
"ngrok.kill()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"w4LtdMb23tZ4"
],
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |