upd ui

app.py

@@ -1,296 +1,16 @@
-import re
 import os
 import json
-import time
-import torch
-import random
 import shutil
 import argparse
 import warnings
 import gradio as gr
-import soundfile as sf
-from transformers import GPT2Config
-from model import Patchilizer, TunesFormer
-from convert import abc2xml, xml2img, xml2, transpose_octaves_abc
-from utils import (
-    PATCH_NUM_LAYERS,
-    PATCH_LENGTH,
-    CHAR_NUM_LAYERS,
-    PATCH_SIZE,
-    SHARE_WEIGHTS,
-    TEMP_DIR,
-    WEIGHTS_DIR,
-    DEVICE,
-)
-
-
-def get_args(parser: argparse.ArgumentParser):
-    parser.add_argument(
-        "-num_tunes",
-        type=int,
-        default=1,
-        help="the number of independently computed returned tunes",
-    )
-    parser.add_argument(
-        "-max_patch",
-        type=int,
-        default=128,
-        help="integer to define the maximum length in tokens of each tune",
-    )
-    parser.add_argument(
-        "-top_p",
-        type=float,
-        default=0.8,
-        help="float to define the tokens that are within the sample operation of text generation",
-    )
-    parser.add_argument(
-        "-top_k",
-        type=int,
-        default=8,
-        help="integer to define the tokens that are within the sample operation of text generation",
-    )
-    parser.add_argument(
-        "-temperature",
-        type=float,
-        default=1.2,
-        help="the temperature of the sampling operation",
-    )
-    parser.add_argument("-seed", type=int, default=None, help="seed for randomstate")
-    parser.add_argument(
-        "-show_control_code",
-        type=bool,
-        default=False,
-        help="whether to show control code",
-    )
-    parser.add_argument(
-        "-template",
-        type=bool,
-        default=True,
-        help="whether to generate by template",
-    )
-    return parser.parse_args()
-
-
-def get_abc_key_val(text: str, key="K"):
-    pattern = re.escape(key) + r":(.*?)\n"
-    match = re.search(pattern, text)
-    if match:
-        return match.group(1).strip()
-    else:
-        return None
-
-
-def adjust_volume(in_audio: str, dB_change: int):
-    y, sr = sf.read(in_audio)
-    sf.write(in_audio, y * 10 ** (dB_change / 20), sr)
-
-
-def generate_music(
-    args,
-    emo: str,
-    weights: str,
-    outdir=TEMP_DIR,
-    fix_tempo=None,
-    fix_pitch=None,
-    fix_volume=None,
-):
-    patchilizer = Patchilizer()
-    patch_config = GPT2Config(
-        num_hidden_layers=PATCH_NUM_LAYERS,
-        max_length=PATCH_LENGTH,
-        max_position_embeddings=PATCH_LENGTH,
-        vocab_size=1,
-    )
-    char_config = GPT2Config(
-        num_hidden_layers=CHAR_NUM_LAYERS,
-        max_length=PATCH_SIZE,
-        max_position_embeddings=PATCH_SIZE,
-        vocab_size=128,
-    )
-    model = TunesFormer(patch_config, char_config, share_weights=SHARE_WEIGHTS)
-    checkpoint = torch.load(weights, map_location=DEVICE)
-    model.load_state_dict(checkpoint["model"])
-    model = model.to(DEVICE)
-    model.eval()
-    prompt = f"A:{emo}\n"
-    tunes = ""
-    num_tunes = args.num_tunes
-    max_patch = args.max_patch
-    top_p = args.top_p
-    top_k = args.top_k
-    temperature = args.temperature
-    seed = args.seed
-    show_control_code = args.show_control_code
-    fname_prefix = emo if args.template else "Melody"
-    print(" Hyper parms ".center(60, "#"), "\n")
-    args_dict: dict = vars(args)
-    for arg in args_dict.keys():
-        print(f"{arg}: {str(args_dict[arg])}")
-
-    print("\n", " Output tunes ".center(60, "#"))
-    start_time = time.time()
-    for i in range(num_tunes):
-        title = f"T:{fname_prefix} Fragment\n"
-        artist = f"C:Generated by AI\n"
-        tune = f"X:{str(i + 1)}\n{title}{artist}{prompt}"
-        lines = re.split(r"(\n)", tune)
-        tune = ""
-        skip = False
-        for line in lines:
-            if show_control_code or line[:2] not in ["S:", "B:", "E:", "D:"]:
-                if not skip:
-                    print(line, end="")
-                    tune += line
-
-                skip = False
-
-            else:
-                skip = True
-
-        input_patches = torch.tensor(
-            [patchilizer.encode(prompt, add_special_patches=True)[:-1]],
-            device=DEVICE,
-        )
-        if tune == "":
-            tokens = None
-
-        else:
-            prefix = patchilizer.decode(input_patches[0])
-            remaining_tokens = prompt[len(prefix) :]
-            tokens = torch.tensor(
-                [patchilizer.bos_token_id] + [ord(c) for c in remaining_tokens],
-                device=DEVICE,
-            )
-
-        while input_patches.shape[1] < max_patch:
-            predicted_patch, seed = model.generate(
-                input_patches,
-                tokens,
-                top_p=top_p,
-                top_k=top_k,
-                temperature=temperature,
-                seed=seed,
-            )
-            tokens = None
-            if predicted_patch[0] != patchilizer.eos_token_id:
-                next_bar = patchilizer.decode([predicted_patch])
-                if show_control_code or next_bar[:2] not in ["S:", "B:", "E:", "D:"]:
-                    print(next_bar, end="")
-                    tune += next_bar
-
-                if next_bar == "":
-                    break
-
-                next_bar = remaining_tokens + next_bar
-                remaining_tokens = ""
-                predicted_patch = torch.tensor(
-                    patchilizer.bar2patch(next_bar),
-                    device=DEVICE,
-                ).unsqueeze(0)
-                input_patches = torch.cat(
-                    [input_patches, predicted_patch.unsqueeze(0)],
-                    dim=1,
-                )
-
-            else:
-                break
-
-        tunes += f"{tune}\n\n"
-        print("\n")
-
-    # fix tempo
-    if fix_tempo != None:
-        tempo = f"Q:{fix_tempo}\n"
-
-    else:
-        tempo = f"Q:{random.randint(88, 132)}\n"
-        if emo == "Q1":
-            tempo = f"Q:{random.randint(160, 184)}\n"
-        elif emo == "Q2":
-            tempo = f"Q:{random.randint(184, 228)}\n"
-        elif emo == "Q3":
-            tempo = f"Q:{random.randint(40, 69)}\n"
-        elif emo == "Q4":
-            tempo = f"Q:{random.randint(40, 69)}\n"
-
-    Q_val = get_abc_key_val(tunes, "Q")
-    if Q_val:
-        tunes = tunes.replace(f"Q:{Q_val}\n", "")
-
-    K_val = get_abc_key_val(tunes)
-    if K_val == "none":
-        K_val = "C"
-        tunes = tunes.replace("K:none\n", f"K:{K_val}\n")
-
-    tunes = tunes.replace(f"A:{emo}\n", tempo)
-    # fix mode:major/minor
-    mode = "major" if emo == "Q1" or emo == "Q4" else "minor"
-    if (mode == "major") and ("m" in K_val):
-        tunes = tunes.replace(f"\nK:{K_val}\n", f"\nK:{K_val.split('m')[0]}\n")
-
-    elif (mode == "minor") and (not "m" in K_val):
-        tunes = tunes.replace(f"\nK:{K_val}\n", f"\nK:{K_val.replace('dor', '')}min\n")
-
-    print("Generation time: {:.2f} seconds".format(time.time() - start_time))
-    timestamp = time.strftime("%a_%d_%b_%Y_%H_%M_%S", time.localtime())
-    try:
-        # fix avg_pitch (octave)
-        if fix_pitch != None:
-            if fix_pitch:
-                tunes, xml = transpose_octaves_abc(
-                    tunes,
-                    f"{outdir}/{timestamp}.musicxml",
-                    fix_pitch,
-                )
-                tunes = tunes.replace(title + title, title)
-                os.rename(xml, f"{outdir}/[{fname_prefix}]{timestamp}.musicxml")
-                xml = f"{outdir}/[{fname_prefix}]{timestamp}.musicxml"
-
-        else:
-            if mode == "minor":
-                offset = -12
-                if emo == "Q2":
-                    offset -= 12
-
-                tunes, xml = transpose_octaves_abc(
-                    tunes,
-                    f"{outdir}/{timestamp}.musicxml",
-                    offset,
-                )
-                tunes = tunes.replace(title + title, title)
-                os.rename(xml, f"{outdir}/[{fname_prefix}]{timestamp}.musicxml")
-                xml = f"{outdir}/[{fname_prefix}]{timestamp}.musicxml"
-
-            else:
-                xml = abc2xml(tunes, f"{outdir}/[{fname_prefix}]{timestamp}.musicxml")
-
-        audio = xml2(xml, "wav")
-        if fix_volume != None:
-            if fix_volume:
-                adjust_volume(audio, fix_volume)
-
-        elif os.path.exists(audio):
-            if emo == "Q1":
-                adjust_volume(audio, 5)
-
-            elif emo == "Q2":
-                adjust_volume(audio, 10)
-
-        mxl = xml2(xml, "mxl")
-        midi = xml2(xml, "mid")
-        pdf, jpg = xml2img(xml)
-        return audio, midi, pdf, xml, mxl, tunes, jpg
-
-    except Exception as e:
-        print(f"{e}")
-        return generate_music(args, emo, weights)
+from generate import generate_music, get_args
+from utils import WEIGHTS_DIR, TEMP_DIR
 
 
 def infer_by_template(dataset: str, v: str, a: str, add_chord: bool):
-    if os.path.exists(TEMP_DIR):
-        shutil.rmtree(TEMP_DIR)
-
-    os.makedirs(TEMP_DIR, exist_ok=True)
+    status = "Success"
+    audio = midi = pdf = xml = mxl = tunes = jpg = None
     emotion = "Q1"
     if v == "Low" and a == "High":
         emotion = "Q2"
@@ -301,14 +21,20 @@ def infer_by_template(dataset: str, v: str, a: str, add_chord: bool):
     elif v == "High" and a == "Low":
         emotion = "Q4"
 
-    parser = argparse.ArgumentParser()
-    args = get_args(parser)
-    args.template = True
-    return generate_music(
-        args,
-        emo=emotion,
-        weights=f"{WEIGHTS_DIR}/{dataset.lower()}/weights.pth",
-    )
+    try:
+        parser = argparse.ArgumentParser()
+        args = get_args(parser)
+        args.template = True
+        audio, midi, pdf, xml, mxl, tunes, jpg = generate_music(
+            args,
+            emo=emotion,
+            weights=f"{WEIGHTS_DIR}/{dataset.lower()}/weights.pth",
+        )
+
+    except Exception as e:
+        status = f"{e}"
+
+    return status, audio, midi, pdf, xml, mxl, tunes, jpg
 
 
 def infer_by_features(
@@ -320,10 +46,8 @@ def infer_by_features(
     rms: int,
     add_chord: bool,
 ):
-    if os.path.exists(TEMP_DIR):
-        shutil.rmtree(TEMP_DIR)
-
-    os.makedirs(TEMP_DIR, exist_ok=True)
+    status = "Success"
+    audio = midi = pdf = xml = mxl = tunes = jpg = None
     emotion = "Q1"
     if mode == "Minor" and pitch_std == "High":
         emotion = "Q2"
@@ -334,78 +58,99 @@ def infer_by_features(
     elif mode == "Major" and pitch_std == "Low":
         emotion = "Q4"
 
-    parser = argparse.ArgumentParser()
-    args = get_args(parser)
-    args.template = False
-    return generate_music(
-        args,
-        emo=emotion,
-        weights=f"{WEIGHTS_DIR}/{dataset.lower()}/weights.pth",
-        fix_tempo=tempo,
-        fix_pitch=octave,
-        fix_volume=rms,
-    )
-
-
-def feedback(fixed_emo: str, source_dir="./flagged", target_dir="./feedbacks"):
-    if not fixed_emo:
-        return "Please select feedback before submitting! "
-
-    os.makedirs(target_dir, exist_ok=True)
-    for root, _, files in os.walk(source_dir):
-        for file in files:
-            if file.endswith(".mxl"):
-                prompt_emo = file.split("]")[0][1:]
-                if prompt_emo != fixed_emo:
-                    file_path = os.path.join(root, file)
-                    target_path = os.path.join(
-                        target_dir, file.replace(".mxl", f"_{fixed_emo}.mxl")
-                    )
-                    shutil.copy(file_path, target_path)
-                    return f"Copied {file_path} to {target_path}"
+    try:
+        parser = argparse.ArgumentParser()
+        args = get_args(parser)
+        args.template = False
+        audio, midi, pdf, xml, mxl, tunes, jpg = generate_music(
+            args,
+            emo=emotion,
+            weights=f"{WEIGHTS_DIR}/{dataset.lower()}/weights.pth",
+            fix_tempo=tempo,
+            fix_pitch=octave,
+            fix_volume=rms,
+        )
 
-                else:
-                    return "Thanks for your feedback!"
+    except Exception as e:
+        status = f"{e}"
 
-    return "No .mxl files found in the source directory."
+    return status, audio, midi, pdf, xml, mxl, tunes, jpg
 
 
-def save_template(
-    label: str,
-    pitch_std: str,
-    mode: str,
-    tempo: int,
-    octave: int,
-    rms: int,
+def feedback(
+    fixed_emo: str,
+    source_dir=f"./{TEMP_DIR}/output",
+    target_dir=f"./{TEMP_DIR}/feedback",
 ):
-    if (
-        label
-        and pitch_std
-        and mode
-        and tempo != None
-        and octave != None
-        and rms != None
-    ):
-        json_str = json.dumps(
-            {
-                "label": label,
-                "pitch_std": pitch_std == "High",
-                "mode": mode == "Major",
-                "tempo": tempo,
-                "octave": octave,
-                "volume": rms,
-            }
-        )
+    try:
+        if not fixed_emo:
+            raise ValueError("Please select feedback before submitting! ")
+
+        os.makedirs(target_dir, exist_ok=True)
+        for root, _, files in os.walk(source_dir):
+            for file in files:
+                if file.endswith(".mxl"):
+                    prompt_emo = file.split("]")[0][1:]
+                    if prompt_emo != fixed_emo:
+                        file_path = os.path.join(root, file)
+                        target_path = os.path.join(
+                            target_dir, file.replace(".mxl", f"_{fixed_emo}.mxl")
+                        )
+                        shutil.copy(file_path, target_path)
+                        return f"Copied {file_path} to {target_path}"
+
+                    else:
+                        return "Thanks for your feedback!"
+
+        return "No .mxl files found in the source directory."
+
+    except Exception as e:
+        return f"{e}"
+
+
+def save_template(label: str, pitch_std: str, mode: str, tempo: int, octave: int, rms):
+    status = "Success"
+    template = None
+    try:
+        if (
+            label
+            and pitch_std
+            and mode
+            and tempo != None
+            and octave != None
+            and rms != None
+        ):
+            json_str = json.dumps(
+                {
+                    "label": label,
+                    "pitch_std": pitch_std == "High",
+                    "mode": mode == "Major",
+                    "tempo": tempo,
+                    "octave": octave,
+                    "volume": rms,
+                }
+            )
+
+            with open(
+                f"./{TEMP_DIR}/feedback/templates.jsonl",
+                "a",
+                encoding="utf-8",
+            ) as file:
+                file.write(json_str + "\n")
 
-        with open("./feedbacks/templates.jsonl", "a", encoding="utf-8") as file:
-            file.write(json_str + "\n")
+            template = f"./{TEMP_DIR}/feedback/templates.jsonl"
+
+        else:
+            raise ValueError("Please check features")
+
+    except Exception as e:
+        status = f"{e}"
+
+    return status, template
 
 
 if __name__ == "__main__":
     warnings.filterwarnings("ignore")
-    if os.path.exists("./flagged"):
-        shutil.rmtree("./flagged")
-
     with gr.Blocks() as demo:
         gr.Markdown(
             "## The current CPU-based version on HuggingFace has slow inference, you can access the GPU-based mirror on [ModelScope](https://www.modelscope.cn/studios/monetjoe/EMelodyGen)"
@@ -423,79 +168,69 @@ if __name__ == "__main__":
                     label="Dataset",
                     value="Rough4Q",
                 )
-                gr.Markdown("# Generate by emotion condition")
-                gr.Image(
-                    "https://www.modelscope.cn/studio/monetjoe/EMelodyGen/resolve/master/src/4q.jpg",
-                    show_label=False,
-                    show_download_button=False,
-                    show_fullscreen_button=False,
-                    show_share_button=False,
-                )
-                valence_radio = gr.Radio(
-                    ["Low", "High"],
-                    label="Valence (reflects negative-positive levels of emotion)",
-                    value="High",
-                )
-                arousal_radio = gr.Radio(
-                    ["Low", "High"],
-                    label="Arousal (reflects the calmness-intensity of the emotion)",
-                    value="High",
-                )
-                chord_check = gr.Checkbox(
-                    label="Generate chords (coming soon)",
-                    value=False,
-                )
-                gen_btn_1 = gr.Button("Generate")
-                gr.Markdown("# Generate by feature control")
-                std_option = gr.Radio(["Low", "High"], label="Pitch SD", value="High")
-                mode_option = gr.Radio(["Minor", "Major"], label="Mode", value="Major")
-                tempo_option = gr.Slider(
-                    minimum=40,
-                    maximum=228,
-                    step=1,
-                    value=120,
-                    label="Tempo (BPM)",
-                )
-                octave_option = gr.Slider(
-                    minimum=-24,
-                    maximum=24,
-                    step=12,
-                    value=0,
-                    label="Octave (±12)",
-                )
-                volume_option = gr.Slider(
-                    minimum=-5,
-                    maximum=10,
-                    step=5,
-                    value=0,
-                    label="Volume (dB)",
-                )
-                chord_check_2 = gr.Checkbox(
-                    label="Generate chords (coming soon)",
-                    value=False,
-                )
-                gen_btn_2 = gr.Button("Generate")
-                template_radio = gr.Radio(
-                    ["Q1", "Q2", "Q3", "Q4"],
-                    label="The emotion to which the current template belongs",
-                )
-                save_btn = gr.Button("Save template")
-                gr.Markdown(
-                    """
-## Cite
-```bibtex
-@inproceedings{Zhou2025EMelodyGen,
-  title     = {EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template},
-  author    = {Monan Zhou and Xiaobing Li and Feng Yu and Wei Li},
-  month     = {Mar},
-  year      = {2025},
-  publisher = {GitHub},
-  version   = {0.1},
-  url       = {https://github.com/monetjoe/EMelodyGen}
-}
-```
-                    """
-                )
+                with gr.Tab("By template"):
+                    gr.Image(
+                        "https://www.modelscope.cn/studio/monetjoe/EMelodyGen/resolve/master/src/4q.jpg",
+                        show_label=False,
+                        show_download_button=False,
+                        show_fullscreen_button=False,
+                        show_share_button=False,
+                    )
+                    valence_radio = gr.Radio(
+                        ["Low", "High"],
+                        label="Valence (reflects negative-positive levels of emotion)",
+                        value="High",
+                    )
+                    arousal_radio = gr.Radio(
+                        ["Low", "High"],
+                        label="Arousal (reflects the calmness-intensity of the emotion)",
+                        value="High",
+                    )
+                    chord_check = gr.Checkbox(
+                        label="Generate chords (coming soon)",
+                        value=False,
+                    )
+                    gen_btn_1 = gr.Button("Generate")
+
+                with gr.Tab("By feature control"):
+                    std_option = gr.Radio(
+                        ["Low", "High"], label="Pitch SD", value="High"
+                    )
+                    mode_option = gr.Radio(
+                        ["Minor", "Major"], label="Mode", value="Major"
+                    )
+                    tempo_option = gr.Slider(
+                        minimum=40,
+                        maximum=228,
+                        step=1,
+                        value=120,
+                        label="Tempo (BPM)",
+                    )
+                    octave_option = gr.Slider(
+                        minimum=-24,
+                        maximum=24,
+                        step=12,
+                        value=0,
+                        label="Octave (±12)",
+                    )
+                    volume_option = gr.Slider(
+                        minimum=-5,
+                        maximum=10,
+                        step=5,
+                        value=0,
+                        label="Volume (dB)",
+                    )
+                    chord_check_2 = gr.Checkbox(
+                        label="Generate chords (coming soon)",
+                        value=False,
+                    )
+                    gen_btn_2 = gr.Button("Generate")
+                    template_radio = gr.Radio(
+                        ["Q1", "Q2", "Q3", "Q4"],
+                        label="The emotion to which the current template belongs",
+                    )
+                    save_btn = gr.Button("Save template")
+                    dld_template = gr.File(label="Download template")
 
             with gr.Column():
                 wav_audio = gr.Audio(label="Audio", type="filepath")
@@ -506,20 +241,35 @@ if __name__ == "__main__":
                 abc_textbox = gr.Textbox(label="ABC notation", show_copy_button=True)
                 staff_img = gr.Image(label="Staff", type="filepath")
 
-                gr.Interface(
-                    fn=feedback,
-                    inputs=gr.Radio(
-                        ["Q1", "Q2", "Q3", "Q4"],
-                        label="Feedback: the emotion you believe the generated result should belong to",
-                    ),
-                    outputs=gr.Textbox(show_copy_button=False, show_label=False),
-                    allow_flagging="never",
+            with gr.Column():
+                status_bar = gr.Textbox(label="Status", show_copy_button=True)
+                fdb_radio = gr.Radio(
+                    ["Q1", "Q2", "Q3", "Q4"],
+                    label="Feedback: the emotion you believe the generated result should belong to",
                 )
+                fdb_btn = gr.Button("Submit")
 
+                gr.Markdown(
+                    """## Cite
+                    ```bibtex
+                    @inproceedings{Zhou2025EMelodyGen,
+                        title     = {EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template},
+                        author    = {Monan Zhou and Xiaobing Li and Feng Yu and Wei Li},
+                        month     = {Mar},
+                        year      = {2025},
+                        publisher = {GitHub},
+                        version   = {0.1},
+                        url       = {https://github.com/monetjoe/EMelodyGen}
+                    }
+                    ```"""
+                )
+
+        # actions
         gen_btn_1.click(
             fn=infer_by_template,
             inputs=[dataset_option, valence_radio, arousal_radio, chord_check],
             outputs=[
+                status_bar,
                 wav_audio,
                 midi_file,
                 pdf_file,
@@ -542,6 +292,7 @@ if __name__ == "__main__":
                 chord_check,
             ],
             outputs=[
+                status_bar,
                 wav_audio,
                 midi_file,
                 pdf_file,
@@ -562,6 +313,9 @@ if __name__ == "__main__":
                 octave_option,
                 volume_option,
             ],
+            outputs=[status_bar, dld_template],
         )
 
+        fdb_btn.click(fn=feedback, inputs=fdb_radio, outputs=status_bar)
+
     demo.launch()

generate.py

@@ -0,0 +1,290 @@
+import re
+import os
+import shutil
+import time
+import torch
+import random
+import argparse
+import soundfile as sf
+from transformers import GPT2Config
+from model import Patchilizer, TunesFormer
+from convert import abc2xml, xml2img, xml2, transpose_octaves_abc
+from utils import (
+    PATCH_NUM_LAYERS,
+    PATCH_LENGTH,
+    CHAR_NUM_LAYERS,
+    PATCH_SIZE,
+    SHARE_WEIGHTS,
+    TEMP_DIR,
+    DEVICE,
+)
+
+
+def get_args(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "-num_tunes",
+        type=int,
+        default=1,
+        help="the number of independently computed returned tunes",
+    )
+    parser.add_argument(
+        "-max_patch",
+        type=int,
+        default=128,
+        help="integer to define the maximum length in tokens of each tune",
+    )
+    parser.add_argument(
+        "-top_p",
+        type=float,
+        default=0.8,
+        help="float to define the tokens that are within the sample operation of text generation",
+    )
+    parser.add_argument(
+        "-top_k",
+        type=int,
+        default=8,
+        help="integer to define the tokens that are within the sample operation of text generation",
+    )
+    parser.add_argument(
+        "-temperature",
+        type=float,
+        default=1.2,
+        help="the temperature of the sampling operation",
+    )
+    parser.add_argument("-seed", type=int, default=None, help="seed for randomstate")
+    parser.add_argument(
+        "-show_control_code",
+        type=bool,
+        default=False,
+        help="whether to show control code",
+    )
+    parser.add_argument(
+        "-template",
+        type=bool,
+        default=True,
+        help="whether to generate by template",
+    )
+    return parser.parse_args()
+
+
+def get_abc_key_val(text: str, key="K"):
+    pattern = re.escape(key) + r":(.*?)\n"
+    match = re.search(pattern, text)
+    if match:
+        return match.group(1).strip()
+    else:
+        return None
+
+
+def adjust_volume(in_audio: str, dB_change: int):
+    y, sr = sf.read(in_audio)
+    sf.write(in_audio, y * 10 ** (dB_change / 20), sr)
+
+
+def clean_dir(dir_path: str):
+    if os.path.exists(dir_path):
+        shutil.rmtree(dir_path)
+
+    os.makedirs(dir_path)
+
+
+def generate_music(
+    args,
+    emo: str,
+    weights: str,
+    outdir=f"{TEMP_DIR}/output",
+    fix_tempo=None,
+    fix_pitch=None,
+    fix_volume=None,
+):
+    clean_dir(outdir)
+    patchilizer = Patchilizer()
+    patch_config = GPT2Config(
+        num_hidden_layers=PATCH_NUM_LAYERS,
+        max_length=PATCH_LENGTH,
+        max_position_embeddings=PATCH_LENGTH,
+        vocab_size=1,
+    )
+    char_config = GPT2Config(
+        num_hidden_layers=CHAR_NUM_LAYERS,
+        max_length=PATCH_SIZE,
+        max_position_embeddings=PATCH_SIZE,
+        vocab_size=128,
+    )
+    model = TunesFormer(patch_config, char_config, share_weights=SHARE_WEIGHTS)
+    checkpoint = torch.load(weights, map_location=DEVICE)
+    model.load_state_dict(checkpoint["model"])
+    model = model.to(DEVICE)
+    model.eval()
+    prompt = f"A:{emo}\n"
+    tunes = ""
+    num_tunes = args.num_tunes
+    max_patch = args.max_patch
+    top_p = args.top_p
+    top_k = args.top_k
+    temperature = args.temperature
+    seed = args.seed
+    show_control_code = args.show_control_code
+    fname_prefix = emo if args.template else "Melody"
+    print(" Hyper parms ".center(60, "#"), "\n")
+    args_dict: dict = vars(args)
+    for arg in args_dict.keys():
+        print(f"{arg}: {str(args_dict[arg])}")
+
+    print("\n", " Output tunes ".center(60, "#"))
+    start_time = time.time()
+    for i in range(num_tunes):
+        title = f"T:{fname_prefix} Fragment\n"
+        artist = f"C:Generated by AI\n"
+        tune = f"X:{str(i + 1)}\n{title}{artist}{prompt}"
+        lines = re.split(r"(\n)", tune)
+        tune = ""
+        skip = False
+        for line in lines:
+            if show_control_code or line[:2] not in ["S:", "B:", "E:", "D:"]:
+                if not skip:
+                    print(line, end="")
+                    tune += line
+
+                skip = False
+
+            else:
+                skip = True
+
+        input_patches = torch.tensor(
+            [patchilizer.encode(prompt, add_special_patches=True)[:-1]],
+            device=DEVICE,
+        )
+        if tune == "":
+            tokens = None
+
+        else:
+            prefix = patchilizer.decode(input_patches[0])
+            remaining_tokens = prompt[len(prefix) :]
+            tokens = torch.tensor(
+                [patchilizer.bos_token_id] + [ord(c) for c in remaining_tokens],
+                device=DEVICE,
+            )
+
+        while input_patches.shape[1] < max_patch:
+            predicted_patch, seed = model.generate(
+                input_patches,
+                tokens,
+                top_p=top_p,
+                top_k=top_k,
+                temperature=temperature,
+                seed=seed,
+            )
+            tokens = None
+            if predicted_patch[0] != patchilizer.eos_token_id:
+                next_bar = patchilizer.decode([predicted_patch])
+                if show_control_code or next_bar[:2] not in ["S:", "B:", "E:", "D:"]:
+                    print(next_bar, end="")
+                    tune += next_bar
+
+                if next_bar == "":
+                    break
+
+                next_bar = remaining_tokens + next_bar
+                remaining_tokens = ""
+                predicted_patch = torch.tensor(
+                    patchilizer.bar2patch(next_bar),
+                    device=DEVICE,
+                ).unsqueeze(0)
+                input_patches = torch.cat(
+                    [input_patches, predicted_patch.unsqueeze(0)],
+                    dim=1,
+                )
+
+            else:
+                break
+
+        tunes += f"{tune}\n\n"
+        print("\n")
+
+    # fix tempo
+    if fix_tempo != None:
+        tempo = f"Q:{fix_tempo}\n"
+
+    else:
+        tempo = f"Q:{random.randint(88, 132)}\n"
+        if emo == "Q1":
+            tempo = f"Q:{random.randint(160, 184)}\n"
+        elif emo == "Q2":
+            tempo = f"Q:{random.randint(184, 228)}\n"
+        elif emo == "Q3":
+            tempo = f"Q:{random.randint(40, 69)}\n"
+        elif emo == "Q4":
+            tempo = f"Q:{random.randint(40, 69)}\n"
+
+    Q_val = get_abc_key_val(tunes, "Q")
+    if Q_val:
+        tunes = tunes.replace(f"Q:{Q_val}\n", "")
+
+    K_val = get_abc_key_val(tunes)
+    if K_val == "none":
+        K_val = "C"
+        tunes = tunes.replace("K:none\n", f"K:{K_val}\n")
+
+    tunes = tunes.replace(f"A:{emo}\n", tempo)
+    # fix mode:major/minor
+    mode = "major" if emo == "Q1" or emo == "Q4" else "minor"
+    if (mode == "major") and ("m" in K_val):
+        tunes = tunes.replace(f"\nK:{K_val}\n", f"\nK:{K_val.split('m')[0]}\n")
+
+    elif (mode == "minor") and (not "m" in K_val):
+        tunes = tunes.replace(f"\nK:{K_val}\n", f"\nK:{K_val.replace('dor', '')}min\n")
+
+    print("Generation time: {:.2f} seconds".format(time.time() - start_time))
+    timestamp = time.strftime("%a_%d_%b_%Y_%H_%M_%S", time.localtime())
+    try:
+        # fix avg_pitch (octave)
+        if fix_pitch != None:
+            if fix_pitch:
+                tunes, xml = transpose_octaves_abc(
+                    tunes,
+                    f"{outdir}/{timestamp}.musicxml",
+                    fix_pitch,
+                )
+                tunes = tunes.replace(title + title, title)
+                os.rename(xml, f"{outdir}/[{fname_prefix}]{timestamp}.musicxml")
+                xml = f"{outdir}/[{fname_prefix}]{timestamp}.musicxml"
+
+        else:
+            if mode == "minor":
+                offset = -12
+                if emo == "Q2":
+                    offset -= 12
+
+                tunes, xml = transpose_octaves_abc(
+                    tunes,
+                    f"{outdir}/{timestamp}.musicxml",
+                    offset,
+                )
+                tunes = tunes.replace(title + title, title)
+                os.rename(xml, f"{outdir}/[{fname_prefix}]{timestamp}.musicxml")
+                xml = f"{outdir}/[{fname_prefix}]{timestamp}.musicxml"
+
+            else:
+                xml = abc2xml(tunes, f"{outdir}/[{fname_prefix}]{timestamp}.musicxml")
+
+        audio = xml2(xml, "wav")
+        if fix_volume != None:
+            if fix_volume:
+                adjust_volume(audio, fix_volume)
+
+        elif os.path.exists(audio):
+            if emo == "Q1":
+                adjust_volume(audio, 5)
+
+            elif emo == "Q2":
+                adjust_volume(audio, 10)
+
+        mxl = xml2(xml, "mxl")
+        midi = xml2(xml, "mid")
+        pdf, jpg = xml2img(xml)
+        return audio, midi, pdf, xml, mxl, tunes, jpg
+
+    except Exception as e:
+        print(f"{e}")
+        return generate_music(args, emo, weights)

requirements.txt

@@ -1,7 +1,6 @@
 torch
 music21
 pymupdf
-autopep8
 soundfile
 unidecode
 pillow==9.4.0

utils.py

@@ -4,11 +4,16 @@ import time
 import torch
 import requests
 import subprocess
+import modelscope
+import huggingface_hub
 from tqdm import tqdm
-from huggingface_hub import snapshot_download
 
-TEMP_DIR = "./flagged"
-WEIGHTS_DIR = snapshot_download("monetjoe/EMelodyGen", cache_dir="./__pycache__")
+TEMP_DIR = "./__pycache__"
+WEIGHTS_DIR = (
+    huggingface_hub.snapshot_download("monetjoe/EMelodyGen", cache_dir=TEMP_DIR)
+    if os.getenv("language")
+    else modelscope.snapshot_download("monetjoe/EMelodyGen", cache_dir=TEMP_DIR)
+)
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 PATCH_LENGTH = 128  # Patch Length
 PATCH_SIZE = 32  # Patch Size