Synced repo using 'sync_with_huggingface' Github Action

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+nate_is_humming.wav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,362 @@
+###########################################
+# For fast downloads from Hugging Face Hub
+# **Requires the hf_transfer package**
+###########################################
+import os
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+###########################################
+
+import json
+import random
+import typing as tp
+from datetime import datetime
+from pathlib import Path
+from functools import partial
+
+import gradio as gr
+import torch
+import torchaudio
+import numpy as np
+
+from audiocraft.models import musicgen
+from audiocraft.data.audio import audio_write
+from audiocraft.utils.notebook import display_audio
+
+from pitch_correction_utils import autotune, closest_pitch, aclosest_pitch_from_scale
+
+
+def ta_to_librosa_format(waveform):
+    """
+    Convert an audio tensor from torchaudio format to librosa format.
+
+    Args:
+    waveform (torch.Tensor): Audio tensor from torchaudio with shape (n_channels, n_samples).
+
+    Returns:
+    np.ndarray: Audio array in librosa format with shape (n_samples,) or (2, n_samples).
+    """
+    # Ensure waveform is in CPU and convert to numpy
+    waveform_np = waveform.numpy()
+
+    # Check if audio is mono or stereo and transpose if necessary
+    if waveform_np.shape[0] == 1:
+        # Remove the channel dimension for mono
+        waveform_np = waveform_np.squeeze(0)
+    else:
+        # Transpose to switch from (n_channels, n_samples) to (n_samples, n_channels)
+        waveform_np = waveform_np.transpose()
+
+    # Normalize to [-1, 1] if not already
+    if waveform_np.dtype in [np.int16, np.int32]:
+        waveform_np = waveform_np / np.iinfo(waveform_np.dtype).max
+
+    return waveform_np
+
+
+def librosa_to_ta_format(waveform_np):
+    """
+    Convert an audio array from librosa format to torchaudio format.
+
+    Args:
+    waveform_np (np.ndarray): Audio array from librosa with shape (n_samples,) or (2, n_samples).
+
+    Returns:
+    torch.Tensor: Audio tensor in torchaudio format with shape (n_channels, n_samples).
+    """
+    # Ensure it is a float32 array normalized to [-1, 1]
+    waveform_np = np.array(waveform_np, dtype=np.float32)
+
+    if waveform_np.ndim == 1:
+        # Add a channel dimension for mono
+        waveform_np = waveform_np[np.newaxis, :]
+    else:
+        # Transpose to switch from (n_samples, n_channels) to (n_channels, n_samples)
+        waveform_np = waveform_np.transpose()
+
+    # Convert numpy array to PyTorch tensor
+    waveform = torch.from_numpy(waveform_np)
+    return waveform
+
+
+def run_autotune(y, sr, correction_method="closest", scale=None):
+    # Only mono-files are handled. If stereo files are supplied, only the first channel is used.
+    if y.ndim > 1:
+        y = y[0, :]
+
+    # Pick the pitch adjustment strategy according to the arguments.
+    correction_function = closest_pitch if correction_method == 'closest' else \
+        partial(aclosest_pitch_from_scale, scale=scale)
+
+    # Torchaudio -> librosa
+    y = ta_to_librosa_format(y)
+    # Autotune
+    pitch_corrected_y = autotune(y, sr, correction_function, plot=False)
+    # Librosa -> torchaudio
+    pitch_corrected_y = librosa_to_ta_format(pitch_corrected_y)
+
+    return pitch_corrected_y
+
+
+def set_all_seeds(seed):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+def _preprocess_audio(
+    audio_path, model: musicgen.MusicGen, duration: tp.Optional[int] = None
+):
+    wav, sr = torchaudio.load(audio_path)
+    wav = torchaudio.functional.resample(wav, sr, model.sample_rate)
+    wav = wav.mean(dim=0, keepdim=True)
+
+    # Calculate duration in seconds if not provided
+    if duration is None:
+        duration = wav.shape[1] / model.sample_rate
+
+    # Check if duration is more than 30 seconds
+    if duration > 30:
+        raise ValueError("Duration cannot be more than 30 seconds")
+
+    end_sample = int(model.sample_rate * duration)
+    wav = wav[:, :end_sample]
+
+    assert wav.shape[0] == 1
+    assert wav.shape[1] == model.sample_rate * duration
+
+    wav = wav.cuda()
+    wav = wav.unsqueeze(1)
+
+    with torch.no_grad():
+        gen_audio = model.compression_model.encode(wav)
+
+    codes, scale = gen_audio
+
+    assert scale is None
+
+    return codes
+
+
+def _get_stemmed_wav_patched(wav, sample_rate):
+    print("Skipping stem separation!")
+    return wav
+
+
+class Pipeline:
+    def __init__(self, model_id, max_batch_size=4, do_skip_demucs=True):
+        self.model = musicgen.MusicGen.get_pretrained(model_id)
+        self.max_batch_size = max_batch_size
+        self.do_skip_demucs = do_skip_demucs
+
+        if self.do_skip_demucs:
+            self.model.lm.condition_provider.conditioners.self_wav._get_stemmed_wav = _get_stemmed_wav_patched
+
+    def __call__(
+        self,
+        prompt,
+        input_audio=None,
+        scale=None,
+        continuation=False,
+        batch_size=1,
+        duration=15,
+        use_sampling=True,
+        temperature=1.0,
+        top_k=250,
+        top_p=0.0,
+        cfg_coef=3.0,
+        output_dir="./samples",  # change to google drive if you'd like
+        normalization_strategy="loudness",
+        seed=-1,
+        continuation_start=0,
+        continuation_end=None,
+    ):
+        print("Prompt:", prompt)
+        if scale == "closest":
+            scale = None
+
+        set_generation_params = lambda duration: self.model.set_generation_params(
+            duration=duration,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            cfg_coef=cfg_coef,
+        )
+
+        if not seed or seed == -1:
+            seed = torch.seed() % 2 ** 32 - 1
+            set_all_seeds(seed)
+        set_all_seeds(seed)
+        print(f"Using seed {seed}")
+        if not input_audio:
+            set_generation_params(duration)
+            wav, tokens = self.model.generate([prompt] * batch_size, progress=True, return_tokens=True)
+        else:
+            input_audio, sr = torchaudio.load(input_audio)
+            # Save a copy of the original input audio
+            original_input_audio = input_audio.clone()
+            print("Input audio shape:", input_audio.shape)
+            if scale is None:
+                print("Running pitch correction for 'closest' pitch")
+                input_audio = run_autotune(input_audio, sr, correction_method="closest")
+            else:
+                print("Running pitch correction for 'scale' pitch")
+                input_audio = run_autotune(input_audio, sr, correction_method="scale", scale=scale)
+            print(f"...Done running pitch correction. Shape after is {input_audio.shape}.\n")
+            input_audio = input_audio[None] if input_audio.dim() == 2 else input_audio
+
+            continuation_start = 0 if not continuation_start else continuation_start
+            if continuation_end is None or continuation_end == -1:
+                continuation_end = input_audio.shape[2] / sr
+
+            if continuation_start > continuation_end:
+                raise ValueError(
+                    "`continuation_start` must be less than or equal to `continuation_end`"
+                )
+
+            input_audio_wavform = input_audio[
+                ..., int(sr * continuation_start) : int(sr * continuation_end)
+            ]
+            input_audio_wavform = input_audio_wavform.repeat(batch_size, 1, 1)
+            # TODO - not using this - is that wrong??
+            input_audio_duration = input_audio_wavform.shape[-1] / sr
+
+            if continuation:
+                set_generation_params(duration)  # + input_audio_duration)  # SEE TODO above
+                print("Continuation wavform shape!", input_audio_wavform.shape)
+                wav, tokens = self.model.generate_continuation(
+                    prompt=input_audio_wavform,
+                    prompt_sample_rate=sr,
+                    descriptions=[prompt] * batch_size,
+                    progress=True,
+                    return_tokens=True
+                )
+            else:
+                print("Melody wavform shape!", input_audio_wavform.shape)
+                set_generation_params(duration)
+                wav, tokens = self.model.generate_with_chroma(
+                    [prompt] * batch_size, input_audio_wavform, sr, progress=True, return_tokens=True
+                )
+        wav, tokens = wav.cpu(), tokens.cpu()
+        # Write to files
+        output_dir = Path(output_dir)
+        output_dir.mkdir(exist_ok=True, parents=True)
+        dt_str = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        if input_audio is not None:
+            outfile_path = output_dir / f"{dt_str}_input_raw"
+            audio_write(
+                outfile_path,
+                original_input_audio,
+                sr,
+                strategy=normalization_strategy,
+            )
+            outfile_path = output_dir / f"{dt_str}_input_pitch_corrected"
+            audio_write(
+                outfile_path,
+                input_audio_wavform[0],
+                sr,
+                strategy=normalization_strategy,
+            )
+
+        for i in range(batch_size):
+            outfile_path = output_dir / f"{dt_str}_{i:02d}"
+            audio_write(
+                outfile_path,
+                wav[i],
+                self.model.sample_rate,
+                strategy=normalization_strategy,
+            )
+        json_out_path = output_dir / f"{dt_str}.json"
+        json_out_path.write_text(json.dumps(dict(
+            prompt=prompt,
+            batch_size=batch_size,
+            duration=duration,
+            use_sampling=use_sampling,
+            temperature=temperature,
+            top_k=top_k,
+            cfg_coef=cfg_coef,
+        )))
+
+        to_return = [None] * (self.max_batch_size + 1)
+        if input_audio is not None:
+            print(f"trying to return input audio wavform of shape: {input_audio_wavform.shape}")
+            to_return[0] = (sr, input_audio_wavform[0].T.numpy())
+
+        for i in range(batch_size):
+            to_return[i + 1] = (self.model.sample_rate, wav[i].T.numpy())
+            print(wav[i].shape)
+        return to_return
+
+
+def main(model_id="nateraw/musicgen-songstarter-v0.2", max_batch_size=4, share=False, debug=False):
+    pipeline = Pipeline(model_id, max_batch_size)
+    interface = gr.Interface(
+        fn=pipeline.__call__,
+        inputs=[
+            gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
+            gr.Audio(
+                sources=["microphone"],
+                waveform_options=gr.WaveformOptions(
+                    waveform_color="#01C6FF",
+                    waveform_progress_color="#0066B4",
+                    skip_length=2,
+                    show_controls=False,
+                ),
+                type="filepath",
+            ),
+            gr.Dropdown(["closest", "A:maj", "A:min", "Bb:maj", "Bb:min", "B:maj", "B:min", "C:maj", "C:min", "Db:maj", "Db:min", "D:maj", "D:min", "Eb:maj", "Eb:min", "E:maj", "E:min", "F:maj", "F:min", "Gb:maj", "Gb:min", "G:maj", "G:min", "Ab:maj", "Ab:min"], label="Scale for pitch correction.", value="closest"),
+            gr.Checkbox(label="Is Continuation", value=False),
+            gr.Slider(label="Batch Size", value=1, minimum=1, maximum=pipeline.max_batch_size, step=1),
+            gr.Slider(label="Duration", value=15, minimum=4, maximum=30),
+            gr.Checkbox(label="Use Sampling", value=True),
+            gr.Slider(label="Temperature", value=1.0, minimum=0.0, maximum=2.0),
+            gr.Slider(label="Top K", value=250, minimum=0, maximum=1000),
+            gr.Slider(label="Top P", value=0.0, minimum=0.0, maximum=1.0),
+            gr.Slider(label="CFG Coef", value=3.0, minimum=0.0, maximum=10.0),
+            gr.Textbox(label="Output Dir", value="./samples"),
+            gr.Dropdown(["loudness", "clip", "peak", "rms"], value="loudness", label="Strategy for normalizing audio."),
+            gr.Slider(label="random seed", minimum=-1, maximum=9e8),
+        ],
+        outputs=[gr.Audio(label=("Input " if i == 0 else "") + f"Audio {i}") for i in range(pipeline.max_batch_size + 1)],
+        title="🎶 Generate song ideas with musicgen-songstarter-v0.2 🎶",
+        description="Check out the repo [here](https://huggingface.co/nateraw/musicgen-songstarter-v0.2)",
+        examples=[
+            ["hip hop, soul, piano, chords, jazz, neo jazz, G# minor, 140 bpm", None, "closest", False, 1, 8, True, 1.0, 250, 0.0, 3.0, "./samples", "loudness", -1],
+            ["acoustic, guitar, melody, rnb, trap, E minor, 85 bpm", None, "closest", False, 1, 8, True, 1.0, 250, 0.0, 3.0, "./samples", "loudness", -1],
+            ["synth, dark, hip hop, melody, trap, Gb minor, 140 bpm", "./nate_is_singing_Gb_minor.wav", "Gb:min", False, 1, 7, True, 1.0, 250, 0.0, 3.0, "./samples", "loudness", -1],
+            ["drill, layered, melody, songstarters, trap, C# minor, 130 bpm", None, "closest", False, 1, 8, True, 1.0, 250, 0.0, 3.0, "./samples", "loudness", -1],
+            ["hip hop, soul, rnb, neo soul, songstarters, B minor, 140 bpm", None, "closest", False, 1, 8, True, 1.0, 250, 0.0, 3.0, "./samples", "loudness", -1],
+            ["music, mallets, bells, melody, dancehall, african, afropop & afrobeats", "./nate_is_singing_Gb_minor.wav", "Gb:min", False, 1, 7, True, 1.0, 250, 0.0, 4.5, "./samples", "loudness", -1],
+        ]
+    )
+    interface.launch(share=share, debug=debug)
+
+
+if __name__ == '__main__':
+    from fire import Fire
+    Fire(main)
+
+    # For testing
+
+    # pipe = Pipeline("nateraw/musicgen-songstarter-v0.2", max_batch_size=4)
+    # example_input = (
+    #     "hip hop, soul, piano, chords, jazz, neo jazz, G# minor, 140 bpm",
+    #     "nate_is_humming.wav",
+    #     "closest",
+    #     False,
+    #     1,
+    #     8,
+    #     True,
+    #     1.0,
+    #     250,
+    #     0.0,
+    #     3.0,
+    #     "./samples",
+    #     "loudness",
+    #     -1,
+    #     0,
+    #     None
+    # )
+    # out = pipe(*example_input)

nate_is_humming.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a62520e3026bc71b06fa75a8120c3b46524a0a34dcac9661e3e27632e294b11f
+size 1196036

pitch_correction_utils.py

@@ -0,0 +1,161 @@
+from functools import partial
+from pathlib import Path
+import argparse
+import librosa
+import librosa.display
+import numpy as np
+import matplotlib.pyplot as plt
+import soundfile as sf
+import scipy.signal as sig
+import psola
+
+
+SEMITONES_IN_OCTAVE = 12
+
+
+def degrees_from(scale: str):
+    """Return the pitch classes (degrees) that correspond to the given scale"""
+    degrees = librosa.key_to_degrees(scale)
+    # To properly perform pitch rounding to the nearest degree from the scale, we need to repeat
+    # the first degree raised by an octave. Otherwise, pitches slightly lower than the base degree
+    # would be incorrectly assigned.
+    degrees = np.concatenate((degrees, [degrees[0] + SEMITONES_IN_OCTAVE]))
+    return degrees
+
+
+def closest_pitch(f0):
+    """Round the given pitch values to the nearest MIDI note numbers"""
+    midi_note = np.around(librosa.hz_to_midi(f0))
+    # To preserve the nan values.
+    nan_indices = np.isnan(f0)
+    midi_note[nan_indices] = np.nan
+    # Convert back to Hz.
+    return librosa.midi_to_hz(midi_note)
+
+
+def closest_pitch_from_scale(f0, scale):
+    """Return the pitch closest to f0 that belongs to the given scale"""
+    # Preserve nan.
+    if np.isnan(f0):
+        return np.nan
+    degrees = degrees_from(scale)
+    midi_note = librosa.hz_to_midi(f0)
+    # Subtract the multiplicities of 12 so that we have the real-valued pitch class of the
+    # input pitch.
+    degree = midi_note % SEMITONES_IN_OCTAVE
+    # Find the closest pitch class from the scale.
+    degree_id = np.argmin(np.abs(degrees - degree))
+    # Calculate the difference between the input pitch class and the desired pitch class.
+    degree_difference = degree - degrees[degree_id]
+    # Shift the input MIDI note number by the calculated difference.
+    midi_note -= degree_difference
+    # Convert to Hz.
+    return librosa.midi_to_hz(midi_note)
+
+
+def aclosest_pitch_from_scale(f0, scale):
+    """Map each pitch in the f0 array to the closest pitch belonging to the given scale."""
+    sanitized_pitch = np.zeros_like(f0)
+    for i in np.arange(f0.shape[0]):
+        sanitized_pitch[i] = closest_pitch_from_scale(f0[i], scale)
+    # Perform median filtering to additionally smooth the corrected pitch.
+    smoothed_sanitized_pitch = sig.medfilt(sanitized_pitch, kernel_size=11)
+    # Remove the additional NaN values after median filtering.
+    smoothed_sanitized_pitch[np.isnan(smoothed_sanitized_pitch)] = \
+        sanitized_pitch[np.isnan(smoothed_sanitized_pitch)]
+    return smoothed_sanitized_pitch
+
+
+def autotune(audio, sr, correction_function, plot=False):
+    # Set some basis parameters.
+    frame_length = 2048
+    hop_length = frame_length // 4
+    fmin = librosa.note_to_hz('C2')
+    fmax = librosa.note_to_hz('C7')
+
+    # Pitch tracking using the PYIN algorithm.
+    f0, voiced_flag, voiced_probabilities = librosa.pyin(audio,
+                                                         frame_length=frame_length,
+                                                         hop_length=hop_length,
+                                                         sr=sr,
+                                                         fmin=fmin,
+                                                         fmax=fmax)
+
+    # Apply the chosen adjustment strategy to the pitch.
+    corrected_f0 = correction_function(f0)
+
+    if plot:
+        # Plot the spectrogram, overlaid with the original pitch trajectory and the adjusted
+        # pitch trajectory.
+        stft = librosa.stft(audio, n_fft=frame_length, hop_length=hop_length)
+        time_points = librosa.times_like(stft, sr=sr, hop_length=hop_length)
+        log_stft = librosa.amplitude_to_db(np.abs(stft), ref=np.max)
+        fig, ax = plt.subplots()
+        img = librosa.display.specshow(log_stft, x_axis='time', y_axis='log', ax=ax, sr=sr, hop_length=hop_length, fmin=fmin, fmax=fmax)
+        fig.colorbar(img, ax=ax, format="%+2.f dB")
+        ax.plot(time_points, f0, label='original pitch', color='cyan', linewidth=2)
+        ax.plot(time_points, corrected_f0, label='corrected pitch', color='orange', linewidth=1)
+        ax.legend(loc='upper right')
+        plt.ylabel('Frequency [Hz]')
+        plt.xlabel('Time [M:SS]')
+        plt.savefig('pitch_correction.png', dpi=300, bbox_inches='tight')
+
+    # Pitch-shifting using the PSOLA algorithm.
+    return psola.vocode(audio, sample_rate=int(sr), target_pitch=corrected_f0, fmin=fmin, fmax=fmax)
+
+
+def main(
+    vocals_file,
+    plot=False,
+    correction_method="closest",
+    scale=None
+):
+    """Run autotune-like pitch correction on the given audio file.
+
+    Args:
+        vocals_file (str): Filepath to the audio file to be pitch-corrected.
+        plot (bool, optional): Whether to plot the results. Defaults to False.
+        correction_method (str, optional): The pitch correction method to use. Defaults to `"closest"`. If set to "closest", the pitch will be rounded to the nearest MIDI note.
+            If set to "scale", the pitch will be rounded to the nearest note in the given `scale`.
+        scale (str, optional): The scale to use for pitch correction. ex. `"C:min"` / `"A:maj"`. Defaults to None.
+    """    
+    
+    # Parse the command line arguments.
+    # ap = argparse.ArgumentParser()
+    # ap.add_argument('vocals_file')
+    # ap.add_argument('--plot', '-p', action='store_true', default=False,
+    #                 help='if set, will produce a plot of the results')
+    # ap.add_argument('--correction-method', '-c', choices=['closest', 'scale'], default='closest')
+    # ap.add_argument('--scale', '-s', type=str, help='see librosa.key_to_degrees;'
+    #                                                 ' used only for the \"scale\" correction'
+    #                                                 ' method')
+    # args = ap.parse_args(args=args)
+
+    filepath = Path(vocals_file)
+
+    # Load the audio file.
+    y, sr = librosa.load(str(filepath), sr=None, mono=False)
+
+    # Only mono-files are handled. If stereo files are supplied, only the first channel is used.
+    if y.ndim > 1:
+        y = y[0, :]
+
+    # Pick the pitch adjustment strategy according to the arguments.
+    correction_function = closest_pitch if correction_method == 'closest' else \
+        partial(aclosest_pitch_from_scale, scale=scale)
+
+    # Perform the auto-tuning.
+    pitch_corrected_y = autotune(y, sr, correction_function, plot)
+
+    # Write the corrected audio to an output file.
+    filepath = filepath.parent / (filepath.stem + '_pitch_corrected' + filepath.suffix)
+    sf.write(str(filepath), pitch_corrected_y, sr)
+    return pitch_corrected_y
+
+
+if __name__=='__main__':
+    # main("./singing_music_idea.wav --plot -c closest".split())
+    # python pitch_correction_utils.py --vocals_file "./nate_is_humming.wav" --plot -c closest
+    from fire import Fire
+    Fire(main)
+

requirements.txt

@@ -0,0 +1,6 @@
+git+https://[email protected]/facebookresearch/audiocraft#egg=audiocraft
+hf_transfer
+gradio
+psola
+torchvision==0.16.0
+fire

singing_songstarter_demo.ipynb

@@ -0,0 +1,78 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "machine_shape": "hm",
+      "gpuType": "A100",
+      "authorship_tag": "ABX9TyMm+2HEY3Dh8UBT+NJ/CIoa",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/nateraw/singing-songstarter/blob/main/singing_songstarter_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Singing Songstarter Demo\n",
+        "\n",
+        "This is a demo of using [`musicgen-songstarter-v0.2`](https://hf.co/nateraw/musicgen-songstarter-v0.2), a large stereo musicgen trained to be useful for music producers, for the task of voice-to-music.\n",
+        "\n",
+        "**Hum an idea, get a music sample!** 🚀\n",
+        "\n",
+        "### Usage\n",
+        "\n",
+        "1. Run the cell below.\n",
+        "\n",
+        "2. You can ignore \"restart this runtime\" message when it pops up\n",
+        "3. Click the public share link. Should look like: `\"Running on public URL: https://<your-link-here>\"`\n",
+        "4. Enjoy 🔥\n",
+        "\n",
+        "\n",
+        "### If you think this notebook is cool, consider supporting me by:\n",
+        "  - giving [the model](https://hf.co/nateraw/musicgen-songstarter-v0.2) a heart on Hugging Face ❤️\n",
+        "  - following me on [GitHub](https://github.com/nateraw) 👨‍💻\n",
+        "  - following me on [X/twitter](https://twitter.com/nateraw) X\n",
+        "  - giving [the demo repo](https://github.com/nateraw/singing-songstarter) a star ⭐️\n",
+        "\n",
+        "If you have any questions/concerns about this demo, please [file an issue on GitHub](https://github.com/nateraw/singing-songstarter)."
+      ],
+      "metadata": {
+        "id": "hBsE8AuVsgG8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-fw0bpXysAUG"
+      },
+      "outputs": [],
+      "source": [
+        "%cd /content\n",
+        "! git clone https://github.com/nateraw/singing-songstarter\n",
+        "%cd /content/singing-songstarter\n",
+        "! pip install -r requirements.txt\n",
+        "! python app.py --share --debug"
+      ]
+    }
+  ]
+}