| |
|
|
| import datetime |
| import os |
| import random |
|
|
| import gradio as gr |
| from gradio.components import Markdown as m |
|
|
| def get_time(): |
| now = datetime.datetime.now() |
| return now.strftime("%m/%d/%Y, %H:%M:%S") |
|
|
| def generate_recording(): |
| return random.choice(["new-sax-1.mp3", "new-sax-1.wav"]) |
|
|
| def reconstruct(audio): |
| return random.choice(["new-sax-1.mp3", "new-sax-1.wav"]) |
|
|
| io1 = gr.Interface( |
| lambda x, y, z: os.path.join(os.path.dirname(__file__),"sax.wav"), |
| [ |
| gr.Slider(label="pitch"), |
| gr.Slider(label="loudness"), |
| gr.Audio(label="base audio file (optional)"), |
| ], |
| gr.Audio(), |
| ) |
|
|
| io2 = gr.Interface( |
| lambda x, y, z: os.path.join(os.path.dirname(__file__),"flute.wav"), |
| [ |
| gr.Slider(label="pitch"), |
| gr.Slider(label="loudness"), |
| gr.Audio(label="base audio file (optional)"), |
| ], |
| gr.Audio(), |
| ) |
|
|
| io3 = gr.Interface( |
| lambda x, y, z: os.path.join(os.path.dirname(__file__),"trombone.wav"), |
| [ |
| gr.Slider(label="pitch"), |
| gr.Slider(label="loudness"), |
| gr.Audio(label="base audio file (optional)"), |
| ], |
| gr.Audio(), |
| ) |
|
|
| io4 = gr.Interface( |
| lambda x, y, z: os.path.join(os.path.dirname(__file__),"sax2.wav"), |
| [ |
| gr.Slider(label="pitch"), |
| gr.Slider(label="loudness"), |
| gr.Audio(label="base audio file (optional)"), |
| ], |
| gr.Audio(), |
| ) |
|
|
| demo = gr.Blocks(title="Neural Instrument Cloning") |
|
|
| with demo.clear(): |
| m( |
| """ |
| ## Neural Instrument Cloning from Very Few Samples |
| <center><img src="https://media.istockphoto.com/photos/brass-trombone-picture-id490455809?k=20&m=490455809&s=612x612&w=0&h=l9KJvH_25z0QTLggHrcH_MsR4gPLH7uXwDPUAZ_C5zk=" width="400px"></center>""" |
| ) |
| m( |
| """ |
| This Blocks implementation is an adaptation [a report written](https://erlj.notion.site/Neural-Instrument-Cloning-from-very-few-samples-2cf41d8b630842ee8c7eb55036a1bfd6) by Nicolas Jonason and Bob L.T. Sturm. |
| |
| I've implemented it in Blocks to show off some cool features, such as embedding live ML demos. More on that ahead... |
| |
| ### What does this machine learning model do? |
| It combines techniques from neural voice cloning with musical instrument synthesis. This makes it possible to produce neural instrument synthesisers from just seconds of target instrument audio. |
| |
| ### Audio Examples |
| Here are some **real** 16 second saxophone recordings: |
| """ |
| ) |
| gr.Audio(os.path.join(os.path.dirname(__file__),"sax.wav"), label="Here is a real 16 second saxophone recording:") |
| gr.Audio(os.path.join(os.path.dirname(__file__),"sax.wav")) |
|
|
| m( |
| """\n |
| Here is a **generated** saxophone recordings:""" |
| ) |
| a = gr.Audio(os.path.join(os.path.dirname(__file__),"new-sax.wav")) |
|
|
| gr.Button("Generate a new saxophone recording") |
|
|
| m( |
| """ |
| ### Inputs to the model |
| The inputs to the model are: |
| * pitch |
| * loudness |
| * base audio file |
| """ |
| ) |
|
|
| m( |
| """ |
| Try the model live! |
| """ |
| ) |
|
|
| gr.TabbedInterface( |
| [io1, io2, io3, io4], ["Saxophone", "Flute", "Trombone", "Another Saxophone"] |
| ) |
|
|
| m( |
| """ |
| ### Using the model for cloning |
| You can also use this model a different way, to simply clone the audio file and reconstruct it |
| using machine learning. Here, we'll show a demo of that below: |
| """ |
| ) |
|
|
| a2 = gr.Audio() |
| a2.change(reconstruct, a2, a2) |
|
|
| m( |
| """ |
| Thanks for reading this! As you may have realized, all of the "models" in this demo are fake. They are just designed to show you what is possible using Blocks 🤗. |
| |
| For details of the model, read the [original report here](https://erlj.notion.site/Neural-Instrument-Cloning-from-very-few-samples-2cf41d8b630842ee8c7eb55036a1bfd6). |
| |
| *Details for nerds*: this report was "launched" on: |
| """ |
| ) |
|
|
| t = gr.Textbox(label="timestamp") |
|
|
| demo.load(get_time, [], t) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
