app.py

import gradio as gr
import torch
from tc5.config import SAMPLE_RATE, HOP_LENGTH
from tc5.model import TaikoConformer5
from tc5 import infer as tc5infer
from tc6.model import TaikoConformer6
from tc6 import infer as tc6infer
from tc7.model import TaikoConformer7
from tc7 import infer as tc7infer
from gradio_client import Client, handle_file
import tempfile

DEVICE = torch.device("cpu")

# Load model once
tc5 = TaikoConformer5.from_pretrained("JacobLinCool/taiko-conformer-5")
tc5.to(DEVICE)
tc5.eval()

# Load TC6 model
tc6 = TaikoConformer6.from_pretrained("JacobLinCool/taiko-conformer-6")
tc6.to(DEVICE)
tc6.eval()

# Load TC7 model
tc7 = TaikoConformer7.from_pretrained("JacobLinCool/taiko-conformer-7")
tc7.to(DEVICE)
tc7.eval()

synthesizer = Client("ryanlinjui/taiko-music-generator")


def infer_tc5(audio, nps, bpm):
    audio_path = audio
    filename = audio_path.split("/")[-1]
    # Preprocess
    mel_input, nps_input = tc5infer.preprocess_audio(audio_path, nps)
    # Inference
    don_energy, ka_energy, drumroll_energy = tc5infer.run_inference(
        tc5, mel_input, nps_input, DEVICE
    )
    output_frame_hop_sec = HOP_LENGTH / SAMPLE_RATE
    onsets = tc5infer.decode_onsets(
        don_energy,
        ka_energy,
        drumroll_energy,
        output_frame_hop_sec,
        threshold=0.3,
        min_distance_frames=3,
    )
    # Generate plot
    plot = tc5infer.plot_results(
        mel_input,
        don_energy,
        ka_energy,
        drumroll_energy,
        onsets,
        output_frame_hop_sec,
    )
    # Generate TJA content
    tja_content = tc5infer.write_tja(onsets, bpm=bpm, audio=filename)

    # wrtie TJA content to a temporary file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".tja") as temp_tja_file:
        temp_tja_file.write(tja_content.encode("utf-8"))
        tja_path = temp_tja_file.name

    result = synthesizer.predict(
        param_0=handle_file(tja_path),
        param_1=handle_file(audio_path),
        param_2="達人譜面 / Master",
        param_3=16,
        param_4=5,
        param_5=5,
        param_6=5,
        param_7=5,
        param_8=5,
        param_9=5,
        param_10=5,
        param_11=5,
        param_12=5,
        param_13=5,
        param_14=5,
        param_15=5,
        api_name="/handle",
    )

    oni_audio = result[1]

    return oni_audio, plot, tja_content


def infer_tc6(audio, nps, bpm, difficulty, level):
    audio_path = audio
    filename = audio_path.split("/")[-1]
    # Preprocess
    mel_input = tc6infer.preprocess_audio(audio_path)
    nps_input = torch.tensor(nps, dtype=torch.float32).to(DEVICE)
    difficulty_input = torch.tensor(difficulty, dtype=torch.float32).to(DEVICE)
    level_input = torch.tensor(level, dtype=torch.float32).to(DEVICE)
    # Inference
    don_energy, ka_energy, drumroll_energy = tc6infer.run_inference(
        tc6, mel_input, nps_input, difficulty_input, level_input, DEVICE
    )
    output_frame_hop_sec = HOP_LENGTH / SAMPLE_RATE
    onsets = tc6infer.decode_onsets(
        don_energy,
        ka_energy,
        drumroll_energy,
        output_frame_hop_sec,
        threshold=0.3,
        min_distance_frames=3,
    )
    # Generate plot
    plot = tc6infer.plot_results(
        mel_input,
        don_energy,
        ka_energy,
        drumroll_energy,
        onsets,
        output_frame_hop_sec,
    )
    # Generate TJA content
    tja_content = tc6infer.write_tja(onsets, bpm=bpm, audio=filename)

    # wrtie TJA content to a temporary file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".tja") as temp_tja_file:
        temp_tja_file.write(tja_content.encode("utf-8"))
        tja_path = temp_tja_file.name

    result = synthesizer.predict(
        param_0=handle_file(tja_path),
        param_1=handle_file(audio_path),
        param_2="達人譜面 / Master",
        param_3=16,
        param_4=5,
        param_5=5,
        param_6=5,
        param_7=5,
        param_8=5,
        param_9=5,
        param_10=5,
        param_11=5,
        param_12=5,
        param_13=5,
        param_14=5,
        param_15=5,
        api_name="/handle",
    )

    oni_audio = result[1]

    return oni_audio, plot, tja_content


def infer_tc7(audio, nps, bpm, difficulty, level):
    audio_path = audio
    filename = audio_path.split("/")[-1]
    # Preprocess
    mel_input = tc7infer.preprocess_audio(audio_path)
    nps_input = torch.tensor(nps, dtype=torch.float32).to(DEVICE)
    difficulty_input = torch.tensor(difficulty, dtype=torch.float32).to(DEVICE)
    level_input = torch.tensor(level, dtype=torch.float32).to(DEVICE)
    # Inference
    don_energy, ka_energy, drumroll_energy = tc7infer.run_inference(
        tc7, mel_input, nps_input, difficulty_input, level_input, DEVICE
    )
    output_frame_hop_sec = HOP_LENGTH / SAMPLE_RATE
    onsets = tc7infer.decode_onsets(
        don_energy,
        ka_energy,
        drumroll_energy,
        output_frame_hop_sec,
        threshold=0.3,
        min_distance_frames=3,
    )
    # Generate plot
    plot = tc7infer.plot_results(
        mel_input,
        don_energy,
        ka_energy,
        drumroll_energy,
        onsets,
        output_frame_hop_sec,
    )
    # Generate TJA content
    tja_content = tc7infer.write_tja(onsets, bpm=bpm, audio=filename)

    # wrtie TJA content to a temporary file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".tja") as temp_tja_file:
        temp_tja_file.write(tja_content.encode("utf-8"))
        tja_path = temp_tja_file.name

    result = synthesizer.predict(
        param_0=handle_file(tja_path),
        param_1=handle_file(audio_path),
        param_2="達人譜面 / Master",
        param_3=16,
        param_4=5,
        param_5=5,
        param_6=5,
        param_7=5,
        param_8=5,
        param_9=5,
        param_10=5,
        param_11=5,
        param_12=5,
        param_13=5,
        param_14=5,
        param_15=5,
        api_name="/handle",
    )

    oni_audio = result[1]

    return oni_audio, plot, tja_content


def run_inference(audio, model_choice, nps, bpm, difficulty, level):
    if model_choice == "TC5":
        return infer_tc5(audio, nps, bpm)
    elif model_choice == "TC6":
        return infer_tc6(audio, nps, bpm, difficulty, level)
    else:  # TC7
        return infer_tc7(audio, nps, bpm, difficulty, level)


with gr.Blocks() as demo:
    gr.Markdown("# Taiko Conformer 5/7 Demo")
    with gr.Row():
        audio_input = gr.Audio(sources="upload", type="filepath", label="Input Audio")

    with gr.Row():
        model_choice = gr.Dropdown(
            choices=["TC5", "TC6", "TC7"],
            value="TC7",
            label="Model Selection",
            info="Choose between TaikoConformer 5, 6 or 7",
        )

    with gr.Row():
        nps = gr.Slider(
            value=5.0,
            minimum=0.5,
            maximum=11.0,
            step=0.5,
            label="NPS (Notes Per Second)",
        )
        bpm = gr.Slider(
            value=240,
            minimum=160,
            maximum=640,
            step=1,
            label="BPM (Used by TJA Quantization)",
        )

    with gr.Row():
        difficulty = gr.Slider(
            value=3.0,
            minimum=1.0,
            maximum=3.0,
            step=1.0,
            label="Difficulty",
            visible=False,
            info="1=Normal, 2=Hard, 3=Oni",
        )
        level = gr.Slider(
            value=8.0,
            minimum=1.0,
            maximum=10.0,
            step=1.0,
            label="Level",
            visible=False,
            info="Difficulty level from 1 to 10",
        )

    audio_output = gr.Audio(label="Generated Audio", type="filepath")
    plot_output = gr.Plot(label="Onset/Energy Plot")
    tja_output = gr.Textbox(label="TJA File Content", show_copy_button=True)
    run_btn = gr.Button("Run Inference")

    # Update visibility of TC7-specific controls based on model selection
    def update_visibility(model_choice):
        if model_choice == "TC7" or model_choice == "TC6":
            return gr.update(visible=True), gr.update(visible=True)
        else:
            return gr.update(visible=False), gr.update(visible=False)

    model_choice.change(
        update_visibility, inputs=[model_choice], outputs=[difficulty, level]
    )

    run_btn.click(
        run_inference,
        inputs=[audio_input, model_choice, nps, bpm, difficulty, level],
        outputs=[audio_output, plot_output, tja_output],
    )

if __name__ == "__main__":
    demo.launch()