feat: Initial commit

This creates a Gradio app that will let someone create sheet music out
of a given song.

.gitattributes

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.gitignore

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+# .gitignore courtesy of GitHub: https://github.com/github/gitignore/blob/main/Python.gitignore
+
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.vscode/launch.json

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+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Launch Demo",
+      "type": "python",
+      "request": "launch",
+      "module": "app",
+      "justMyCode": true
+    }
+  ]
+}

LICENSE

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+MIT License
+
+Copyright (c) 2022 Willie Chalmers III
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+# Hugging Face Spaces meta data
+# See https://huggingface.co/docs/hub/spaces#reference
+title: Song to Sheet
+emoji: 🎼
+colorFrom: gray
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: true
+license: mit
+---
+
+# Song to Sheet
+
+_A tool to create sheet music out of songs._
+
+## About
+
+### Inspiration
+
+I took an intro to deep learning class and was very unsatisfied with my
+performance, so I made this in a bout of curiosity.
+
+### How to use
+
+#### Casual Demo
+
+Coming soon: a demo on Hugging Face Spaces!
+
+#### Manual Installation
+
+Prerequisites:
+
+- Python 3.8 (really, anything above 3.6 will still work)
+- pip (or another package manager if you know what you're doing)
+
+If you're cloning from source, first install the dependencies:
+
+```bash
+# Optional: Create and activate a virtual environment
+python -m venv song-to-sheet
+./song-to-sheet/bin/activate # On windows: song-to-sheet\Scripts\activate
+
+# Now install the dependencies
+pip install -r requirements.txt
+```
+
+After getting set up, run the `app.py` script:
+
+```bash
+python app.py
+```
+
+You can also run it standalone (on bash):
+
+```bash
+./app.py
+```

app.py

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+#!/usr/bin/env python
+
+from typing import Tuple
+import gradio as gr
+import numpy as np
+
+from song_to_sheet.generator import SheetMusicGenerator
+
+INTRO_MESSAGE = """
+# Song to Sheet
+
+Upload a song file, and get sheet music of that song or MIDI.
+
+When you upload your track, we'll analyze what instruments are in the song and
+let you specify for which instruments we will generate the sheet music.
+
+*Note: This demo is currently non-functional! Only the project structure*
+*exists right now.*
+
+"""
+
+OUTPUT_INFO = """
+After the song has been analyzed, you can generate a MIDI and PDF conversion of
+the song by clicking "Generate Sheet". The outputs will appear below!
+"""
+
+
+generator = SheetMusicGenerator()
+
+
+def process_input(inputs: Tuple[int, np.ndarray]):
+    _, raw_data = inputs
+    print(inputs)
+    print(raw_data.shape)
+    return generator.analyze_track(raw_data)
+
+
+def get_final_outputs():
+    return generator.generate_midi(), generator.generate_pdf()
+
+
+gr.Interface
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown(INTRO_MESSAGE)
+    with gr.Row().style(equal_height=False):
+        with gr.Column():
+            audio_in = gr.Audio(source="upload", label="Upload song")
+            analyze_music_button = gr.Button("Analyze song")
+            instruments_detected = gr.Label(label="Instrument Likelihoods")
+            generate_sheet_button = gr.Button("Generate Sheet")
+        with gr.Column():
+            gr.Markdown(OUTPUT_INFO)
+            with gr.Tabs():
+                with gr.TabItem("MIDI output"):
+                    output_midi = gr.File(
+                        label="Download MIDI output", type="binary")
+                with gr.TabItem("Sheet music"):
+                    output_sheet = gr.File(
+                        label="Downlaod sheet music", type="binary")
+
+            # TODO: Include song statistics
+
+    analyze_music_button.click(
+        fn=process_input,
+        inputs=[
+            audio_in
+        ],
+        outputs=[
+            instruments_detected,
+        ]
+    )
+    generate_sheet_button.click(
+        fn=get_final_outputs,
+        inputs=[],
+        outputs=[
+            output_midi,
+            output_sheet
+        ]
+    )
+
+demo.launch()

data/references.md

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+# Project References
+
+These are the things that helped in building this project.
+
+## Tools/Software
+
+## Web-based Guides, Tutorials
+
+- [I built a sheet music transcriber](https://towardsdatascience.com/i-built-a-music-sheet-transcriber-heres-how-74708fe7c04c)
+
+## Research
+
+## Other

requirements.txt

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+gradio

song_to_sheet/detector.py

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+import numpy as np
+
+# TODO: Replace me with real process
+TEST_OUTPUTS = {
+    'piano': 0.95,
+    'guitar': 0.7,
+    'voice': 0.5,
+    'other': 0.2,
+}
+
+
+def detect_instruments(audio_data: np.ndarray):
+    """Run inference on a model to detect instruments in the given audio data.
+
+    This assumes that the audio given is a song or can be decomposed into
+    discrete instruments.
+
+    Returns:
+        Dict[str, float]: predicted confidences of instruments present in the
+        track.
+    """
+    return TEST_OUTPUTS

song_to_sheet/generator.py

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+import tempfile
+from pathlib import Path
+from typing import Dict
+import numpy as np
+
+from song_to_sheet.detector import detect_instruments
+
+
+class SheetMusicGenerator:
+    """A module that generates sheet music from sounds."""
+
+    def __init__(self, verbose=False):
+        """
+        Args:
+            verbose (bool): Whether to show detailed logging information.
+        """
+        self._song_data = None
+        self._verbose = verbose
+
+    def analyze_track(self, raw_data: np.ndarray) -> Dict[str, float]:
+        """Perform some preprocessing on the track and cache it in memory.
+
+        Args:
+            raw_data (ndarray): The encoded data from the track.
+
+        Returns:
+            Dict[str, float]: 
+        """
+        self._song_data = raw_data
+        try:
+            instruments = self._detect_instruments(raw_data)
+            # TODO: Figure out how to handle multiple instruments of the same type
+        except:
+            pass
+
+        return instruments
+
+    def _detect_instruments(self, raw_data: np.ndarray):
+        """Return a list of instruments and their probalities of being in the track."""
+        # TODO: Detect if given track even is a song (maybe)
+        instruments = detect_instruments(raw_data)
+        return instruments
+
+    def generate_midi(self):
+        """Generate a MIDI file of the current song."""
+        file = tempfile.NamedTemporaryFile(
+            delete=False, prefix='transcribed_song_', suffix='.mid')
+        return file.name
+
+    def generate_pdf(self):
+        """Generate a PDF file with the sheet music of the current song."""
+        file = tempfile.NamedTemporaryFile(
+            delete=False, prefix='transcribed_song_', suffix='.pdf')
+        return file.name
+
+
+class PDFGenerator:
+    """A module that generates PDFs of standard Western music notation."""