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import gradio as gr
from transformers import pipeline
import tempfile, os
from midi2audio import FluidSynth
# --- Music Generation Logic (API-like Function) ---
def generate_music_api(midi_data=None, chord_progression=None, tempo=120, temperature=0.95, nb_tokens=512, bar_range="0-4"):
try:
# Load the MusicLang Predict model (replace with actual loading code)
ml = ... # Example: ml = pipeline("music-generation", model="your-musiclang-predict-model")
# Handle different generation scenarios based on inputs
if midi_data is not None and chord_progression.strip() != "":
# Continue sequence with chord progression
generated_score = ml.continue_sequence(
midi_data,
chord_progression=chord_progression,
nb_tokens=int(nb_tokens),
temperature=float(temperature),
# ... other parameters
)
elif midi_data is not None and chord_progression.strip() == "":
# Generate using the uploaded MIDI file as a prompt
generated_score = ml.predict(
midi_data, # Use the uploaded MIDI as the prompt
nb_tokens=int(nb_tokens),
temperature=float(temperature),
# ... other parameters
)
else:
# Generate with specific chord progression
generated_score = ml.predict_chords(
chord_progression,
# ... other parameters
)
# Save generated files to temporary locations
temp_midi_file = tempfile.NamedTemporaryFile(suffix=".mid", delete=False)
midi_path = temp_midi_file.name
generated_score.to_midi(midi_path, tempo=tempo, time_signature=time_signature) # Assuming time_signature is defined
temp_mp3_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
mp3_path = temp_mp3_file.name
# ... (convert MIDI to MP3 using FluidSynth and FFmpeg)
# Read binary data from the temporary files
with open(mp3_path, 'rb') as f_mp3:
mp3_binary = f_mp3.read()
with open(midi_path, 'rb') as f_midi:
midi_binary = f_midi.read()
# Remove temporary files
os.remove(mp3_path)
os.remove(midi_path)
return {
"mp3": mp3_binary,
"midi": midi_binary,
"chord_repr": chord_repr, # Assuming chord_repr is still needed
"tempo_message": tempo_message # Assuming tempo_message is still needed
}
except Exception as e:
return {"error": str(e)}
# --- Gradio Interface ---
def musiclang_gradio(midi_file, chord_progression, tempo, temperature, nb_tokens, bar_range):
midi_data = None
if midi_file:
with open(midi_file.name, "rb") as f:
midi_data = f.read()
api_response = generate_music_api(midi_data=midi_data, chord_progression=chord_progression, tempo=tempo, temperature=temperature, nb_tokens=nb_tokens, bar_range=bar_range)
if "error" in api_response:
return None, None, api_response["error"]
# Create temporary files for Gradio
mp3_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
midi_path = tempfile.NamedTemporaryFile(suffix=".mid", delete=False).name
# Write binary data to temporary files
with open(mp3_path, "wb") as f:
f.write(api_response["mp3"])
with open(midi_path, "wb") as f:
f.write(api_response["midi"])
return mp3_path, midi_path, None
with gr.Blocks() as demo:
# Introductory text
gr.Markdown("""
# Controllable Symbolic Music Generation with MusicLang Predict
[MusicLang Predict](https://github.com/musiclang/musiclang_predict) offers advanced controllability features and high-quality music generation by manipulating symbolic music.
You can for example use it to continue your composition with a specific chord progression.
""")
with gr.Row():
with gr.Column():
with gr.Row():
midi_file = gr.File(label="Prompt MIDI File (Optional)", type="filepath", file_types=[".mid", ".midi"],
elem_id='midi_file_input')
with gr.Column():
bar_range = gr.Textbox(label="Bar Range of input file (eg: 0-4 for first four bars)", placeholder="0-4",
value="0-4", elem_id='bar_range_input')
nb_tokens = gr.Number(label="Nb Tokens",
value=512, minimum=256, maximum=2048, step=256, elem_id='nb_tokens_input')
temperature = gr.Slider(
label="Temperature",
value=0.95,
visible=False,
minimum=0.1, maximum=1.0, step=0.1, elem_id='temperature_input')
tempo = gr.Slider(label="Tempo", value=120, minimum=60, maximum=240, step=1, elem_id='tempo_input')
with gr.Row():
chord_progression = gr.Textbox(
label="Chord Progression (Optional)",
placeholder="Am CM Dm7/F E7 Asus4", lines=2, value="", elem_id='chord_progression_input')
with gr.Row():
generate_btn = gr.Button("Generate", elem_id='generate_button')
with gr.Column():
info_message = gr.Textbox(label="Info Message", elem_id='info_message_output')
generated_music = gr.Audio(label="Preview generated Music", elem_id='generated_music_output')
generated_midi = gr.File(label="Download MIDI", elem_id='generated_midi_output')
generate_btn.click(
fn=musiclang_gradio,
inputs=[midi_file, chord_progression, tempo, temperature, nb_tokens, bar_range],
outputs=[generated_music, generated_midi, info_message]
)
with gr.Row():
with gr.Column():
gr.Markdown("## Examples")
gr.Examples(
examples=[["/home/user/app/bach_847.mid", "", 120, 0.95, 512, "0-4"],
["/home/user/app/bach_847.mid", "Cm C7/E Fm F#dim G7", 120, 0.95, 512, "0-4"],
["/home/user/app/boney_m_ma_baker.mid", "", 120, 0.95, 512, "0-4"],
["/home/user/app/eminem_slim_shady.mid", "Cm AbM BbM G7 Cm", 120, 0.95, 512, "0-4"],
["/home/user/app/mozart_alla_turca.mid", "", 120, 0.95, 512, "0-4"],
["/home/user/app/mozart_alla_turca.mid", "Am Em CM G7 E7 Am Am E7 Am", 120, 0.95, 512, "0-4"],
["/home/user/app/daft_punk_around_the_world.mid", "", 120, 0.95, 512, "0-4"],
],
inputs=[midi_file, chord_progression, tempo, temperature, nb_tokens, bar_range],
outputs=[generated_music, generated_midi, info_message],
fn=musiclang_gradio,
cache_examples=True,
)
demo.launch() |