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| import gradio as gr | |
| from musiclang_predict import MusicLangPredictor | |
| import random | |
| import subprocess | |
| import os | |
| import torchaudio | |
| import torch | |
| import numpy as np | |
| from audiocraft.models import MusicGen | |
| from audiocraft.data.audio import audio_write | |
| from pydub import AudioSegment | |
| import spaces | |
| import tempfile | |
| from pydub import AudioSegment | |
| import io | |
| # Check if CUDA is available | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Utility Functions | |
| def peak_normalize(y, target_peak=0.97): | |
| return target_peak * (y / np.max(np.abs(y))) | |
| def rms_normalize(y, target_rms=0.05): | |
| return y * (target_rms / np.sqrt(np.mean(y**2))) | |
| def preprocess_audio(waveform): | |
| waveform_np = waveform.cpu().squeeze().numpy() # Move to CPU before converting to NumPy | |
| # processed_waveform_np = rms_normalize(peak_normalize(waveform_np)) | |
| return torch.from_numpy(waveform_np).unsqueeze(0).to(device) | |
| def create_slices(song, sr, slice_duration, bpm, num_slices=5): | |
| song_length = song.shape[-1] / sr | |
| slices = [] | |
| # Ensure the first slice is from the beginning of the song | |
| first_slice_waveform = song[..., :int(slice_duration * sr)] | |
| slices.append(first_slice_waveform) | |
| for i in range(1, num_slices): | |
| possible_start_indices = list(range(int(slice_duration * sr), int(song_length * sr), int(4 * 60 / bpm * sr))) | |
| if not possible_start_indices: | |
| # If there are no valid start indices, duplicate the first slice | |
| slices.append(first_slice_waveform) | |
| continue | |
| random_start = random.choice(possible_start_indices) | |
| slice_end = random_start + int(slice_duration * sr) | |
| if slice_end > song_length * sr: | |
| # Wrap around to the beginning of the song | |
| remaining_samples = int(slice_end - song_length * sr) | |
| slice_waveform = torch.cat([song[..., random_start:], song[..., :remaining_samples]], dim=-1) | |
| else: | |
| slice_waveform = song[..., random_start:slice_end] | |
| if len(slice_waveform.squeeze()) < int(slice_duration * sr): | |
| additional_samples_needed = int(slice_duration * sr) - len(slice_waveform.squeeze()) | |
| slice_waveform = torch.cat([slice_waveform, song[..., :additional_samples_needed]], dim=-1) | |
| slices.append(slice_waveform) | |
| return slices | |
| def calculate_duration(bpm, min_duration=29, max_duration=30): | |
| single_bar_duration = 4 * 60 / bpm | |
| bars = max(min_duration // single_bar_duration, 1) | |
| while single_bar_duration * bars < min_duration: | |
| bars += 1 | |
| duration = single_bar_duration * bars | |
| while duration > max_duration and bars > 1: | |
| bars -= 1 | |
| duration = single_bar_duration * bars | |
| return duration | |
| def generate_midi(seed, use_chords, chord_progression, bpm): | |
| if seed == "": | |
| seed = random.randint(1, 10000) | |
| ml = MusicLangPredictor('musiclang/musiclang-v2') | |
| try: | |
| seed = int(seed) | |
| except ValueError: | |
| seed = random.randint(1, 10000) | |
| nb_tokens = 1024 | |
| temperature = 0.9 | |
| top_p = 1.0 | |
| if use_chords and chord_progression.strip(): | |
| score = ml.predict_chords( | |
| chord_progression, | |
| time_signature=(4, 4), | |
| temperature=temperature, | |
| topp=top_p, | |
| rng_seed=seed | |
| ) | |
| else: | |
| score = ml.predict( | |
| nb_tokens=nb_tokens, | |
| temperature=temperature, | |
| topp=top_p, | |
| rng_seed=seed | |
| ) | |
| midi_filename = f"output_{seed}.mid" | |
| wav_filename = midi_filename.replace(".mid", ".wav") | |
| score.to_midi(midi_filename, tempo=bpm, time_signature=(4, 4)) | |
| subprocess.run(["fluidsynth", "-ni", "font.sf2", midi_filename, "-F", wav_filename, "-r", "44100"]) | |
| # Clean up temporary MIDI file | |
| os.remove(midi_filename) | |
| sample_rate = 44100 # Assuming fixed sample rate from fluidsynth command | |
| return wav_filename | |
| def generate_music(wav_filename, prompt_duration, musicgen_model, num_iterations, bpm): | |
| # Load the audio from the passed file path | |
| song, sr = torchaudio.load(wav_filename) | |
| song = song.to(device) | |
| # Use the user-provided BPM value for duration calculation | |
| duration = calculate_duration(bpm) | |
| # Create slices from the song using the user-provided BPM value | |
| slices = create_slices(song, sr, 35, bpm, num_slices=5) | |
| # Load the model | |
| model_name = musicgen_model.split(" ")[0] | |
| model_continue = MusicGen.get_pretrained(model_name) | |
| # Setting generation parameters | |
| model_continue.set_generation_params( | |
| use_sampling=True, | |
| top_k=250, | |
| top_p=0.0, | |
| temperature=1.0, | |
| duration=duration, | |
| cfg_coef=3 | |
| ) | |
| all_audio_files = [] | |
| for i in range(num_iterations): | |
| slice_idx = i % len(slices) | |
| print(f"Running iteration {i + 1} using slice {slice_idx}...") | |
| prompt_waveform = slices[slice_idx][..., :int(prompt_duration * sr)] | |
| prompt_waveform = preprocess_audio(prompt_waveform) | |
| output = model_continue.generate_continuation(prompt_waveform, prompt_sample_rate=sr, progress=True) | |
| output = output.cpu() # Move the output tensor back to CPU | |
| # Make sure the output tensor has at most 2 dimensions | |
| if len(output.size()) > 2: | |
| output = output.squeeze() | |
| filename_without_extension = f'continue_{i}' | |
| filename_with_extension = f'{filename_without_extension}.wav' | |
| audio_write(filename_with_extension, output, model_continue.sample_rate, strategy="loudness", loudness_compressor=True) | |
| all_audio_files.append(f'{filename_without_extension}.wav.wav') # Assuming the library appends an extra .wav | |
| # Combine all audio files | |
| combined_audio = AudioSegment.empty() | |
| for filename in all_audio_files: | |
| combined_audio += AudioSegment.from_wav(filename) | |
| combined_audio_filename = f"combined_audio_{random.randint(1, 10000)}.mp3" | |
| combined_audio.export(combined_audio_filename, format="mp3") | |
| # Clean up temporary files | |
| for filename in all_audio_files: | |
| os.remove(filename) | |
| return combined_audio_filename | |
| def continue_music(input_audio_path, prompt_duration, musicgen_model, num_iterations, bpm): | |
| # Load the audio from the given file path | |
| song, sr = torchaudio.load(input_audio_path) | |
| song = song.to(device) | |
| # Load the model and set generation parameters | |
| model_continue = MusicGen.get_pretrained(musicgen_model.split(" ")[0]) | |
| model_continue.set_generation_params( | |
| use_sampling=True, | |
| top_k=250, | |
| top_p=0.0, | |
| temperature=1.0, | |
| duration=calculate_duration(bpm), | |
| cfg_coef=3 | |
| ) | |
| original_audio = AudioSegment.from_mp3(input_audio_path) | |
| current_audio = original_audio | |
| file_paths_for_cleanup = [] # List to track generated file paths for cleanup | |
| for i in range(num_iterations): | |
| # Calculate the slice from the end of the current audio based on prompt_duration | |
| num_samples = int(prompt_duration * sr) | |
| if current_audio.duration_seconds * 1000 < prompt_duration * 1000: | |
| raise ValueError("The prompt_duration is longer than the current audio length.") | |
| start_time = current_audio.duration_seconds * 1000 - prompt_duration * 1000 | |
| prompt_audio = current_audio[start_time:] | |
| # Convert the prompt audio to a PyTorch tensor | |
| prompt_bytes = prompt_audio.export(format="wav").read() | |
| prompt_waveform, _ = torchaudio.load(io.BytesIO(prompt_bytes)) | |
| prompt_waveform = prompt_waveform.to(device) | |
| # Prepare the audio slice for generation | |
| prompt_waveform = preprocess_audio(prompt_waveform) | |
| output = model_continue.generate_continuation(prompt_waveform, prompt_sample_rate=sr, progress=True) | |
| output = output.cpu() # Move the output tensor back to CPU | |
| if len(output.size()) > 2: | |
| output = output.squeeze() | |
| filename_without_extension = f'continue_{i}' | |
| filename_with_extension = f'{filename_without_extension}.wav' | |
| correct_filename_extension = f'{filename_without_extension}.wav.wav' # Apply the workaround for audio_write | |
| audio_write(filename_with_extension, output, model_continue.sample_rate, strategy="loudness", loudness_compressor=True) | |
| generated_audio_segment = AudioSegment.from_wav(correct_filename_extension) | |
| # Replace the prompt portion with the generated audio | |
| current_audio = current_audio[:start_time] + generated_audio_segment | |
| file_paths_for_cleanup.append(correct_filename_extension) # Add to cleanup list | |
| combined_audio_filename = f"combined_audio_{random.randint(1, 10000)}.mp3" | |
| current_audio.export(combined_audio_filename, format="mp3") | |
| # Clean up temporary files using the list of file paths | |
| for file_path in file_paths_for_cleanup: | |
| os.remove(file_path) | |
| return combined_audio_filename | |
| # Define the expandable sections | |
| musiclang_blurb = """ | |
| ## musiclang | |
| musiclang is a controllable ai midi model. it can generate midi sequences based on user-provided parameters, or unconditionally. | |
| [<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub" width="20" style="vertical-align:middle"> musiclang github](https://github.com/MusicLang/musiclang_predict) | |
| [<img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" width="20" style="vertical-align:middle"> musiclang huggingface space](https://huggingface.co/spaces/musiclang/musiclang-predict) | |
| """ | |
| musicgen_blurb = """ | |
| ## musicgen | |
| musicgen is a transformer-based music model that generates audio. It can also do something called a continuation, which was initially meant to extend musicgen outputs beyond 30 seconds. it can be used with any input audio to produce surprising results. | |
| [<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub" width="20" style="vertical-align:middle"> audiocraft github](https://github.com/facebookresearch/audiocraft) | |
| visit https://thecollabagepatch.com/infinitepolo.mp3 or https://thecollabagepatch.com/audiocraft.mp3 to hear continuations in action. | |
| see also https://youtube.com/@thecollabagepatch | |
| """ | |
| finetunes_blurb = """ | |
| ## fine-tuned models | |
| the fine-tunes hosted on the huggingface hub are provided collectively by the musicgen discord community. thanks to vanya, mj, hoenn, septicDNB and of course, lyra. | |
| [<img src="https://cdn.iconscout.com/icon/free/png-256/discord-3691244-3073764.png" alt="Discord" width="20" style="vertical-align:middle"> musicgen discord](https://discord.gg/93kX8rGZ) | |
| [<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" style="vertical-align:middle"> fine-tuning colab notebook by lyra](https://colab.research.google.com/drive/13tbcC3A42KlaUZ21qvUXd25SFLu8WIvb) | |
| """ | |
| # Define the fine-tunes blurb for each model | |
| fine_tunes_info = """ | |
| ## thepatch/vanya_ai_dnb_0.1 | |
| thepatch/vanya_ai_dnb_0.1 was trained by vanya. [vanya's Twitter](https://twitter.com/@veryVANYA) 🔗 - it treats almost all input audio as the beginning of a buildup to a dnb drop (can do downtempo well) | |
| ## thepatch/bleeps-medium | |
| thepatch/bleeps-medium was trained by kevin and lyra [lyra's Twitter](https://twitter.com/@_lyraaaa_) 🔗 - it is a medium model. it's more melodic and ambient sometimes than vanya's, but there's a 50/50 chance it gets real heavy with the edm vibes. It can be amazing at turning your chords into pads, and is a good percussionist. | |
| ## thepatch/budots_remix | |
| thepatch/budots_remix was trained by MJ BERSABEph. budots is a dope niche genre from the philippines apparently. this one will often do fascinating, demonic, kinds of vocal chopping. warning: it tends to speed up and slow down tempo, which makes it hard to use in a daw. | |
| ## thepatch/hoenn_lofi | |
| thepatch/hoenn_lofi is a large fine-tune by hoenn. [hoenn's Twitter](https://twitter.com/@eschatolocation) 🔗 - this model is a large boi, and it shows. even tho it is trained to do lo-fi, its ability to run with your melodies and not ruin them is unparalleled among the fine-tunes so far. | |
| ## thepatch/PhonkV2 | |
| thepatch/PhonkV2 was trained by MJ BERSABEph. there are multiple versions in the discord. | |
| ## foureyednymph/musicgen-sza-sos-small | |
| foureyednymph/musicgen-sza-sos-small was just trained by foureyednymph. We're all about to find out if it does continuations well. | |
| """ | |
| # Create the Gradio interface | |
| with gr.Blocks() as iface: | |
| gr.Markdown("# the-slot-machine") | |
| gr.Markdown("two ai's jamming. warning: outputs will be very strange, likely stupid, and possibly rad.") | |
| gr.Markdown("this is a musical slot machine. using musiclang, we get a midi output. then, we let a musicgen model. trim it so that you like the beginning of the output, and choose the prompt duration. Then we give it to musicgen to continue for 30 seconds. We can then choose a new model and prompt duration, trim it, and give it to musicgen to continue from the end of the output. Re-upload, trim again and repeat with a new musicgen model and different prompt duration if you want. ") | |
| with gr.Accordion("more info", open=False): | |
| gr.Markdown(musiclang_blurb) | |
| gr.Markdown(musicgen_blurb) | |
| gr.Markdown(finetunes_blurb) | |
| with gr.Accordion("fine-tunes info", open=False): | |
| gr.Markdown(fine_tunes_info) | |
| with gr.Row(): | |
| with gr.Column(): | |
| seed = gr.Textbox(label="Seed (leave blank for random)", value="") | |
| use_chords = gr.Checkbox(label="Control Chord Progression", value=False) | |
| chord_progression = gr.Textbox(label="Chord Progression (e.g., Am CM Dm E7 Am)", visible=True) | |
| bpm = gr.Slider(label="BPM", minimum=60, maximum=200, step=1, value=120) | |
| generate_midi_button = gr.Button("Generate MIDI") | |
| midi_audio = gr.Audio(label="Generated MIDI Audio", type="filepath") # Ensure this is set to handle file paths | |
| with gr.Column(): | |
| prompt_duration = gr.Dropdown(label="Prompt Duration (seconds)", choices=list(range(1, 11)), value=5) | |
| musicgen_model = gr.Dropdown(label="MusicGen Model", choices=[ | |
| "thepatch/vanya_ai_dnb_0.1 (small)", | |
| "thepatch/budots_remix (small)", | |
| "thepatch/PhonkV2 (small)", | |
| "thepatch/bleeps-medium (medium)", | |
| "thepatch/hoenn_lofi (large)", | |
| "foureyednymph/musicgen-sza-sos-small (small)" | |
| ], value="thepatch/vanya_ai_dnb_0.1 (small)") | |
| num_iterations = gr.Slider(label="this does nothing rn", minimum=1, maximum=1, step=1, value=1) | |
| generate_music_button = gr.Button("Generate Music") | |
| output_audio = gr.Audio(label="Generated Music", type="filepath") | |
| continue_button = gr.Button("Continue Generating Music") | |
| continue_output_audio = gr.Audio(label="Continued Music Output", type="filepath") | |
| # Connecting the components | |
| generate_midi_button.click(generate_midi, inputs=[seed, use_chords, chord_progression, bpm], outputs=[midi_audio]) | |
| generate_music_button.click(generate_music, inputs=[midi_audio, prompt_duration, musicgen_model, num_iterations, bpm], outputs=[output_audio]) | |
| continue_button.click(continue_music, inputs=[output_audio, prompt_duration, musicgen_model, num_iterations, bpm], outputs=continue_output_audio) | |
| iface.launch() |