Duplicate from TheStinger/Ilaria_RVC

Co-authored-by: Ilaria <TheStinger@users.noreply.huggingface.co>

.env

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+OPENBLAS_NUM_THREADS = 1
+no_proxy = localhost, 127.0.0.1, ::1
+
+# You can change the location of the model, etc. by changing here
+weight_root = weights
+weight_uvr5_root = uvr5_weights
+index_root = logs
+rmvpe_root = assets/rmvpe

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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
+stftpitchshift filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.DS_Store
+__pycache__
+/TEMP
+/DATASETS
+/RUNTIME
+*.pyd
+hubert_base.pt
+.venv
+alexforkINSTALL.bat
+Changelog_CN.md
+Changelog_EN.md
+Changelog_KO.md
+difdep.py
+EasierGUI.py
+envfilescheck.bat
+export_onnx.py
+.vscode/
+export_onnx_old.py
+ffmpeg.exe
+ffprobe.exe
+Fixes/Launch_Tensorboard.bat
+Fixes/LOCAL_CREPE_FIX.bat
+Fixes/local_fixes.py
+Fixes/tensor-launch.py
+gui.py
+infer-web — backup.py
+infer-webbackup.py
+install_easy_dependencies.py
+install_easyGUI.bat
+installstft.bat
+Launch_Tensorboard.bat
+listdepend.bat
+LOCAL_CREPE_FIX.bat
+local_fixes.py
+oldinfer.py
+onnx_inference_demo.py
+Praat.exe
+requirementsNEW.txt
+rmvpe.pt
+rmvpe.onnx
+run_easiergui.bat
+tensor-launch.py
+values1.json
+使用需遵守的协议-LICENSE.txt
+!logs/
+
+logs/*
+logs/mute/0_gt_wavs/mute40k.spec.pt
+!logs/mute/

Applio-RVC-Fork/utils/README.md

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+# External Colab Code
+Code used to make Google Colab work correctly
+- Repo link: https://github.com/IAHispano/Applio-RVC-Fork/
+
+Thanks to https://github.com/kalomaze/externalcolabcode
+

Applio-RVC-Fork/utils/backups.py

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+import os
+import shutil
+import hashlib
+import time
+import base64
+
+
+
+
+LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
+WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
+GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
+
+def import_google_drive_backup():
+    print("Importing Google Drive backup...")
+    weights_exist = False
+    for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):
+        for filename in files:
+            filepath = os.path.join(root, filename)
+            if os.path.isfile(filepath) and not filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
+                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
+                backup_folderpath = os.path.dirname(backup_filepath)
+                if not os.path.exists(backup_folderpath):
+                    os.makedirs(backup_folderpath)
+                    print(f'Created backup folder: {backup_folderpath}', flush=True)
+                shutil.copy2(filepath, backup_filepath) # copy file with metadata
+                print(f'Imported file from Google Drive backup: {filename}')
+            elif filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')) and filename.endswith('.pth'):
+                weights_exist = True
+                weights_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, os.path.join(GOOGLE_DRIVE_PATH, 'weights')))
+                weights_folderpath = os.path.dirname(weights_filepath)
+                if not os.path.exists(weights_folderpath):
+                    os.makedirs(weights_folderpath)
+                    print(f'Created weights folder: {weights_folderpath}', flush=True)
+                shutil.copy2(filepath, weights_filepath) # copy file with metadata
+                print(f'Imported file from weights: {filename}')
+    if weights_exist:
+        print("Copied weights from Google Drive backup to local weights folder.")
+    else:
+        print("No weights found in Google Drive backup.")
+    print("Google Drive backup import completed.")
+
+def get_md5_hash(file_path):
+    hash_md5 = hashlib.md5()
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+def copy_weights_folder_to_drive():
+    destination_folder = os.path.join(GOOGLE_DRIVE_PATH, 'weights')
+    try:
+        if not os.path.exists(destination_folder):
+            os.makedirs(destination_folder)
+
+        num_copied = 0
+        for filename in os.listdir(WEIGHTS_FOLDER):
+            if filename.endswith('.pth'):
+                source_file = os.path.join(WEIGHTS_FOLDER, filename)
+                destination_file = os.path.join(destination_folder, filename)
+                if not os.path.exists(destination_file):
+                    shutil.copy2(source_file, destination_file)
+                    num_copied += 1
+                    print(f"Copied {filename} to Google Drive!")
+
+        if num_copied == 0:
+            print("No new finished models found for copying.")
+        else:
+            print(f"Finished copying {num_copied} files to Google Drive!")
+
+    except Exception as e:
+        print(f"An error occurred while copying weights: {str(e)}")
+        # You can log the error or take appropriate actions here.
+
+def backup_files():
+    print("\nStarting backup loop...")
+    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
+    fully_updated = False  # boolean to track if all files are up to date
+    
+    while True:
+        try:
+            updated = False  # flag to check if any files were updated
+            last_backup_timestamps = {}
+
+            try:
+                with open(last_backup_timestamps_path, 'r') as f:
+                    last_backup_timestamps = dict(line.strip().split(':') for line in f)
+            except FileNotFoundError:
+                pass  # File does not exist yet, which is fine
+            
+            for root, dirs, files in os.walk(LOGS_FOLDER):
+                for filename in files:
+                    if filename != 'last_backup_timestamps.txt':
+                        filepath = os.path.join(root, filename)
+                        if os.path.isfile(filepath):
+                            backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
+                            backup_folderpath = os.path.dirname(backup_filepath)
+                            if not os.path.exists(backup_folderpath):
+                                os.makedirs(backup_folderpath)
+                                print(f'Created backup folder: {backup_folderpath}', flush=True)
+                            # check if file has changed since last backup
+                            last_backup_timestamp = last_backup_timestamps.get(filepath)
+                            current_timestamp = os.path.getmtime(filepath)
+                            if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
+                                shutil.copy2(filepath, backup_filepath)  # copy file with metadata
+                                last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
+                                if last_backup_timestamp is None:
+                                    print(f'Backed up file: {filename}')
+                                else:
+                                    print(f'Updating backed up file: {filename}')
+                                updated = True
+                                fully_updated = False  # if a file is updated, all files are not up to date
+            
+            # check if any files were deleted in Colab and delete them from the backup drive
+            for filepath in list(last_backup_timestamps.keys()):
+                if not os.path.exists(filepath):
+                    backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
+                    if os.path.exists(backup_filepath):
+                        os.remove(backup_filepath)
+                        print(f'Deleted file: {filepath}')
+                    del last_backup_timestamps[filepath]
+                    updated = True
+                    fully_updated = False  # if a file is deleted, all files are not up to date
+            
+            if not updated and not fully_updated:
+                print("Files are up to date.")
+                fully_updated = True  # if all files are up to date, set the boolean to True
+                copy_weights_folder_to_drive()
+                sleep_time = 15
+            else:
+                sleep_time = 0.1
+            
+            with open(last_backup_timestamps_path, 'w') as f:
+                for filepath, timestamp in last_backup_timestamps.items():
+                    f.write(f'{filepath}:{timestamp}\n')
+            
+            time.sleep(sleep_time)  # wait for 15 seconds before checking again, or 0.1s if not fully up to date to speed up backups
+        
+        except Exception as e:
+            print(f"An error occurred: {str(e)}")
+            # You can log the error or take appropriate actions here.

Applio-RVC-Fork/utils/backups_test.py

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+
+import os
+import shutil
+import hashlib
+import time
+
+LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
+WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
+GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
+
+def import_google_drive_backup():
+    print("Importing Google Drive backup...")
+    GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'  # change this to your Google Drive path
+    LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
+    WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
+    weights_exist = False
+    files_to_copy = []
+    weights_to_copy = []
+    
+    def handle_files(root, files, is_weight_files=False):
+        for filename in files:
+            filepath = os.path.join(root, filename)
+            if filename.endswith('.pth') and is_weight_files:
+                weights_exist = True
+                backup_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
+            else:
+                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
+            backup_folderpath = os.path.dirname(backup_filepath)
+            if not os.path.exists(backup_folderpath):
+                os.makedirs(backup_folderpath)
+                print(f'Created folder: {backup_folderpath}', flush=True)
+            if is_weight_files:
+                weights_to_copy.append((filepath, backup_filepath))
+            else:
+                files_to_copy.append((filepath, backup_filepath))
+
+    for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'logs')):
+        handle_files(root, files)
+    
+    for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
+        handle_files(root, files, True)
+
+    # Copy files in batches
+    total_files = len(files_to_copy)
+    start_time = time.time()
+    for i, (source, dest) in enumerate(files_to_copy, start=1):
+        with open(source, 'rb') as src, open(dest, 'wb') as dst:
+            shutil.copyfileobj(src, dst, 1024*1024)  # 1MB buffer size
+        # Report progress every 5 seconds or after every 100 files, whichever is less frequent
+        if time.time() - start_time > 5 or i % 100 == 0:
+            print(f'\rCopying file {i} of {total_files} ({i * 100 / total_files:.2f}%)', end="")
+            start_time = time.time()
+    print(f'\nImported {len(files_to_copy)} files from Google Drive backup')
+
+    # Copy weights in batches
+    total_weights = len(weights_to_copy)
+    start_time = time.time()
+    for i, (source, dest) in enumerate(weights_to_copy, start=1):
+        with open(source, 'rb') as src, open(dest, 'wb') as dst:
+            shutil.copyfileobj(src, dst, 1024*1024)  # 1MB buffer size
+        # Report progress every 5 seconds or after every 100 files, whichever is less frequent
+        if time.time() - start_time > 5 or i % 100 == 0:
+            print(f'\rCopying weight file {i} of {total_weights} ({i * 100 / total_weights:.2f}%)', end="")
+            start_time = time.time()
+    if weights_exist:
+        print(f'\nImported {len(weights_to_copy)} weight files')
+        print("Copied weights from Google Drive backup to local weights folder.")
+    else:
+        print("\nNo weights found in Google Drive backup.")
+    print("Google Drive backup import completed.")
+
+def backup_files():
+    print("\n Starting backup loop...")
+    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
+    fully_updated = False  # boolean to track if all files are up to date
+    try:
+        with open(last_backup_timestamps_path, 'r') as f:
+            last_backup_timestamps = dict(line.strip().split(':') for line in f)
+    except:
+        last_backup_timestamps = {}
+
+    while True:
+        updated = False
+        files_to_copy = []
+        files_to_delete = []
+
+        for root, dirs, files in os.walk(LOGS_FOLDER):
+            for filename in files:
+                if filename != 'last_backup_timestamps.txt':
+                    filepath = os.path.join(root, filename)
+                    if os.path.isfile(filepath):
+                        backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
+                        backup_folderpath = os.path.dirname(backup_filepath)
+
+                        if not os.path.exists(backup_folderpath):
+                            os.makedirs(backup_folderpath)
+                            print(f'Created backup folder: {backup_folderpath}', flush=True)
+
+                        # check if file has changed since last backup
+                        last_backup_timestamp = last_backup_timestamps.get(filepath)
+                        current_timestamp = os.path.getmtime(filepath)
+                        if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
+                            files_to_copy.append((filepath, backup_filepath))  # add to list of files to copy
+                            last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
+                            updated = True
+                            fully_updated = False  # if a file is updated, all files are not up to date
+
+        # check if any files were deleted in Colab and delete them from the backup drive
+        for filepath in list(last_backup_timestamps.keys()):
+            if not os.path.exists(filepath):
+                backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
+                if os.path.exists(backup_filepath):
+                    files_to_delete.append(backup_filepath)  # add to list of files to delete
+                del last_backup_timestamps[filepath]
+                updated = True
+                fully_updated = False  # if a file is deleted, all files are not up to date
+
+        # Copy files in batches
+        if files_to_copy:
+            for source, dest in files_to_copy:
+                shutil.copy2(source, dest)
+            print(f'Copied or updated {len(files_to_copy)} files')
+
+        # Delete files in batches
+        if files_to_delete:
+            for file in files_to_delete:
+                os.remove(file)
+            print(f'Deleted {len(files_to_delete)} files')
+
+        if not updated and not fully_updated:
+            print("Files are up to date.")
+            fully_updated = True  # if all files are up to date, set the boolean to True
+            copy_weights_folder_to_drive()
+
+        with open(last_backup_timestamps_path, 'w') as f:
+            for filepath, timestamp in last_backup_timestamps.items():
+                f.write(f'{filepath}:{timestamp}\n')
+        time.sleep(15)  # wait for 15 seconds before checking again

Applio-RVC-Fork/utils/clonerepo_experimental.py

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+import os
+import subprocess
+import shutil
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm.notebook import tqdm
+from pathlib import Path
+import requests
+
+def run_script():
+    def run_cmd(cmd):
+        process = subprocess.run(cmd, shell=True, check=True, text=True)
+        return process.stdout
+
+    # Change the current directory to /content/
+    os.chdir('/content/')
+    print("Changing dir to /content/")
+
+    # Your function to edit the file
+    def edit_file(file_path):
+        temp_file_path = "/tmp/temp_file.py"
+        changes_made = False
+        with open(file_path, "r") as file, open(temp_file_path, "w") as temp_file:
+            previous_line = ""
+            second_previous_line = ""
+            for line in file:
+                new_line = line.replace("value=160", "value=128")
+                if new_line != line:
+                    print("Replaced 'value=160' with 'value=128'")
+                    changes_made = True
+                line = new_line
+
+                new_line = line.replace("crepe hop length: 160", "crepe hop length: 128")
+                if new_line != line:
+                    print("Replaced 'crepe hop length: 160' with 'crepe hop length: 128'")
+                    changes_made = True
+                line = new_line
+
+                new_line = line.replace("value=0.88", "value=0.75")
+                if new_line != line:
+                    print("Replaced 'value=0.88' with 'value=0.75'")
+                    changes_made = True
+                line = new_line
+
+                if "label=i18n(\"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络\")" in previous_line and "value=1," in line:
+                    new_line = line.replace("value=1,", "value=0.25,")
+                    if new_line != line:
+                        print("Replaced 'value=1,' with 'value=0.25,' based on the condition")
+                        changes_made = True
+                    line = new_line
+
+                if "label=i18n(\"总训练轮数total_epoch\")" in previous_line and "value=20," in line:
+                    new_line = line.replace("value=20,", "value=500,")
+                    if new_line != line:
+                        print("Replaced 'value=20,' with 'value=500,' based on the condition for DEFAULT EPOCH")
+                        changes_made = True
+                    line = new_line
+
+                if 'choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny' in previous_line:
+                    if 'value="pm",' in line:
+                        new_line = line.replace('value="pm",', 'value="mangio-crepe",')
+                        if new_line != line:
+                            print("Replaced 'value=\"pm\",' with 'value=\"mangio-crepe\",' based on the condition")
+                            changes_made = True
+                        line = new_line
+
+                new_line = line.replace('label=i18n("输入训练文件夹路径"), value="E:\\\\语音音频+标注\\\\米津玄师\\\\src"', 'label=i18n("输入训练文件夹路径"), value="/content/dataset/"')
+                if new_line != line:
+                    print("Replaced 'label=i18n(\"输入训练文件夹路径\"), value=\"E:\\\\语音音频+标注\\\\米津玄师\\\\src\"' with 'label=i18n(\"输入训练文件夹路径\"), value=\"/content/dataset/\"'")
+                    changes_made = True
+                line = new_line
+
+                if 'label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),' in second_previous_line:
+                    if 'value=i18n("否"),' in line:
+                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
+                        if new_line != line:
+                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE ONLY LATEST")
+                            changes_made = True
+                        line = new_line
+
+                if 'label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),' in second_previous_line:
+                    if 'value=i18n("否"),' in line:
+                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
+                        if new_line != line:
+                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE SMALL WEIGHTS")
+                            changes_made = True
+                        line = new_line
+
+                temp_file.write(line)
+                second_previous_line = previous_line
+                previous_line = line
+
+        # After finished, we replace the original file with the temp one
+        import shutil
+        shutil.move(temp_file_path, file_path)
+
+        if changes_made:
+            print("Changes made and file saved successfully.")
+        else:
+            print("No changes were needed.")
+
+    # Define the repo path
+    repo_path = '/content/Applio-RVC-Fork'
+
+    def copy_all_files_in_directory(src_dir, dest_dir):
+        # Iterate over all files in source directory
+        for item in Path(src_dir).glob('*'):
+            if item.is_file():
+                # Copy each file to destination directory
+                shutil.copy(item, dest_dir)
+            else:
+                # If it's a directory, make a new directory in the destination and copy the files recursively
+                new_dest = Path(dest_dir) / item.name
+                new_dest.mkdir(exist_ok=True)
+                copy_all_files_in_directory(str(item), str(new_dest))
+
+    def clone_and_copy_repo(repo_path):
+        # New repository link
+        new_repo_link = "https://github.com/IAHispano/Applio-RVC-Fork/"
+        # Temporary path to clone the repository
+        temp_repo_path = "/content/temp_Applio-RVC-Fork"
+        # New folder name
+        new_folder_name = "Applio-RVC-Fork"
+
+        # Clone the latest code from the new repository to a temporary location
+        run_cmd(f"git clone {new_repo_link} {temp_repo_path}")
+        os.chdir(temp_repo_path)
+
+        run_cmd(f"git checkout 3fa4dad3d8961e5ca2522e9e12c0b4ddb71ad402")
+        run_cmd(f"git checkout f9e606c279cb49420597519b0a83b92be81e42e4")
+        run_cmd(f"git checkout 9e305588844c5442d58add1061b29beeca89d679")
+        run_cmd(f"git checkout bf92dc1eb54b4f28d6396a4d1820a25896cc9af8")
+        run_cmd(f"git checkout c3810e197d3cb98039973b2f723edf967ecd9e61")
+        run_cmd(f"git checkout a33159efd134c2413b0afe26a76b7dc87926d2de")
+        run_cmd(f"git checkout 24e251fb62c662e39ac5cf9253cc65deb9be94ec")
+        run_cmd(f"git checkout ad5667d3017e93232dba85969cddac1322ba2902")
+        run_cmd(f"git checkout ce9715392cf52dd5a0e18e00d1b5e408f08dbf27")
+        run_cmd(f"git checkout 7c7da3f2ac68f3bd8f3ad5ca5c700f18ab9f90eb")
+        run_cmd(f"git checkout 4ac395eab101955e8960b50d772c26f592161764")
+        run_cmd(f"git checkout b15b358702294c7375761584e5276c811ffab5e8")
+        run_cmd(f"git checkout 1501793dc490982db9aca84a50647764caa66e51")
+        run_cmd(f"git checkout 21f7faf57219c75e6ba837062350391a803e9ae2")
+        run_cmd(f"git checkout b5eb689fbc409b49f065a431817f822f554cebe7")
+        run_cmd(f"git checkout 7e02fae1ebf24cb151bf6cbe787d06734aa65862")
+        run_cmd(f"git checkout 6aea5ea18ed0b9a1e03fa5d268d6bc3c616672a9")
+        run_cmd(f"git checkout f0f9b25717e59116473fb42bd7f9252cfc32b398")
+        run_cmd(f"git checkout b394de424088a81fc081224bc27338a8651ad3b2")
+        run_cmd(f"git checkout f1999406a88b80c965d2082340f5ea2bfa9ab67a")
+        run_cmd(f"git checkout d98a0fa8dc715308dfc73eac5c553b69c6ee072b")
+        run_cmd(f"git checkout d73267a415fb0eba98477afa43ef71ffd82a7157")
+        run_cmd(f"git checkout 1a03d01356ae79179e1fb8d8915dc9cc79925742")
+        run_cmd(f"git checkout 81497bb3115e92c754300c9b3992df428886a3e9")
+        run_cmd(f"git checkout c5af1f8edcf79cb70f065c0110e279e78e48caf9")
+        run_cmd(f"git checkout cdb3c90109387fa4dfa92f53c3864c71170ffc77")
+
+        # Edit the file here, before copying
+        #edit_file(f"{temp_repo_path}/infer-web.py")
+
+        # Copy all files from the cloned repository to the existing path
+        copy_all_files_in_directory(temp_repo_path, repo_path)
+        print(f"Copying all {new_folder_name} files from GitHub.")
+
+        # Change working directory back to /content/
+        os.chdir('/content/')
+        print("Changed path back to /content/")
+        
+        # Remove the temporary cloned repository
+        shutil.rmtree(temp_repo_path)
+
+    # Call the function
+    clone_and_copy_repo(repo_path)
+
+    # Download the credentials file for RVC archive sheet
+    os.makedirs('/content/Applio-RVC-Fork/stats/', exist_ok=True)
+    run_cmd("wget -q https://cdn.discordapp.com/attachments/945486970883285045/1114717554481569802/peppy-generator-388800-07722f17a188.json -O /content/Applio-RVC-Fork/stats/peppy-generator-388800-07722f17a188.json")
+
+    # Forcefully delete any existing torchcrepe dependencies downloaded from an earlier run just in case
+    shutil.rmtree('/content/Applio-RVC-Fork/torchcrepe', ignore_errors=True)
+    shutil.rmtree('/content/torchcrepe', ignore_errors=True)
+
+    # Download the torchcrepe folder from the maxrmorrison/torchcrepe repository
+    run_cmd("git clone https://github.com/maxrmorrison/torchcrepe.git")
+    shutil.move('/content/torchcrepe/torchcrepe', '/content/Applio-RVC-Fork/')
+    shutil.rmtree('/content/torchcrepe', ignore_errors=True)  # Delete the torchcrepe repository folder
+
+    # Change the current directory to /content/Applio-RVC-Fork
+    os.chdir('/content/Applio-RVC-Fork')
+    os.makedirs('pretrained', exist_ok=True)
+    os.makedirs('uvr5_weights', exist_ok=True)
+
+def download_file(url, filepath):
+    response = requests.get(url, stream=True)
+    response.raise_for_status()
+
+    with open(filepath, "wb") as file:
+        for chunk in response.iter_content(chunk_size=8192):
+            if chunk:
+                file.write(chunk)
+
+def download_pretrained_models():
+    pretrained_models = {
+        "pretrained": [
+            "D40k.pth",
+            "G40k.pth",
+            "f0D40k.pth",
+            "f0G40k.pth"
+        ],
+        "pretrained_v2": [
+            "D40k.pth",
+            "G40k.pth",
+            "f0D40k.pth",
+            "f0G40k.pth",
+            "f0G48k.pth",
+            "f0D48k.pth"
+        ],
+        "uvr5_weights": [
+            "HP2-人声vocals+非人声instrumentals.pth",
+            "HP5-主旋律人声vocals+其他instrumentals.pth",
+            "VR-DeEchoNormal.pth",
+            "VR-DeEchoDeReverb.pth",
+            "VR-DeEchoAggressive.pth",
+            "HP5_only_main_vocal.pth",
+            "HP3_all_vocals.pth",
+            "HP2_all_vocals.pth"
+        ]
+    }
+    part2 = "I"
+    base_url = "https://huggingface.co/lj1995/VoiceConversionWebU" + part2 + "/resolve/main/"
+    base_path = "/content/Applio-RVC-Fork/"
+    base_pathm = base_path
+
+    # Calculate total number of files to download
+    total_files = sum(len(files) for files in pretrained_models.values()) + 1  # +1 for hubert_base.pt
+
+    with tqdm(total=total_files, desc="Downloading files") as pbar:
+        for folder, models in pretrained_models.items():
+            folder_path = os.path.join(base_path, folder)
+            os.makedirs(folder_path, exist_ok=True)
+            for model in models:
+                url = base_url + folder + "/" + model
+                filepath = os.path.join(folder_path, model)
+                download_file(url, filepath)
+                pbar.update()
+
+        # Download hubert_base.pt to the base path
+        hubert_url = base_url + "hubert_base.pt"
+        hubert_filepath = os.path.join(base_pathm, "hubert_base.pt")
+        download_file(hubert_url, hubert_filepath)
+        pbar.update()
+def clone_repository(run_download):
+    with ThreadPoolExecutor(max_workers=2) as executor:
+        executor.submit(run_script)
+        if run_download:
+            executor.submit(download_pretrained_models)

Applio-RVC-Fork/utils/dependency.py

@@ -0,0 +1,170 @@
+import os
+import csv
+import shutil
+import tarfile
+import subprocess
+from pathlib import Path
+from datetime import datetime
+
+def install_packages_but_jank_af():
+    packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
+    pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
+                    'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
+                    'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
+                    'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', 
+                    'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
+
+    print("Updating and installing system packages...")
+    for package in packages:
+        print(f"Installing {package}...")
+        subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
+
+    print("Updating and installing pip packages...")
+    subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
+
+    print('Packages up to date.')
+
+
+def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage):
+    # Mounting Google Drive
+    if not ForceTemporaryStorage:
+        from google.colab import drive
+
+        if not os.path.exists('/content/drive'):
+            drive.mount('/content/drive')
+        else:
+            print('Drive is already mounted. Proceeding...')
+
+    # Function to install dependencies with progress
+    def install_packages():
+        packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
+        pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
+                        'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
+                        'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
+                        'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', 
+                        'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
+
+        print("Updating and installing system packages...")
+        for package in packages:
+            print(f"Installing {package}...")
+            subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
+
+        print("Updating and installing pip packages...")
+        subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
+
+
+        print('Packages up to date.')
+
+    # Function to scan a directory and writes filenames and timestamps
+    def scan_and_write(base_path, output_file):
+        with open(output_file, 'w', newline='') as f:
+            writer = csv.writer(f)
+            for dirpath, dirs, files in os.walk(base_path):
+                for filename in files:
+                    fname = os.path.join(dirpath, filename)
+                    try:
+                        mtime = os.path.getmtime(fname)
+                        writer.writerow([fname, mtime])
+                    except Exception as e:
+                        print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}')
+        print(f'Finished recording filesystem timestamps to {output_file}.')
+
+    # Function to compare files
+    def compare_files(old_file, new_file):
+        old_files = {}
+        new_files = {}
+
+        with open(old_file, 'r') as f:
+            reader = csv.reader(f)
+            old_files = {rows[0]:rows[1] for rows in reader}
+
+        with open(new_file, 'r') as f:
+            reader = csv.reader(f)
+            new_files = {rows[0]:rows[1] for rows in reader}
+
+        removed_files = old_files.keys() - new_files.keys()
+        added_files = new_files.keys() - old_files.keys()
+        unchanged_files = old_files.keys() & new_files.keys()
+
+        changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]}
+
+        for file in removed_files:
+            print(f'File has been removed: {file}')
+
+        for file in changed_files:
+            print(f'File has been updated: {file}')
+
+        return list(added_files) + list(changed_files)
+
+    # Check if CachedRVC.tar.gz exists
+    if ForceTemporaryStorage:
+        file_path = '/content/CachedRVC.tar.gz'
+    else:
+        file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz'
+
+    content_file_path = '/content/CachedRVC.tar.gz'
+    extract_path = '/'
+
+    if not os.path.exists(file_path):
+        folder_path = os.path.dirname(file_path)
+        os.makedirs(folder_path, exist_ok=True)
+        print('No cached dependency install found. Attempting to download GitHub backup..')
+
+        try:
+            download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz"
+            subprocess.run(["wget", "-O", file_path, download_url])
+            print('Download completed successfully!')
+        except Exception as e:
+            print('Download failed:', str(e))
+
+            # Delete the failed download file
+            if os.path.exists(file_path):
+                os.remove(file_path)
+            print('Failed download file deleted. Continuing manual backup..')
+
+    if Path(file_path).exists():
+        if ForceTemporaryStorage:
+            print('Finished downloading CachedRVC.tar.gz.')
+        else:
+            print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...')
+
+        # Check if ForceTemporaryStorage is True and skip copying if it is
+        if ForceTemporaryStorage:
+            pass
+        else:
+            shutil.copy(file_path, content_file_path)
+
+        print('Beginning backup copy operation...')
+
+        with tarfile.open(content_file_path, 'r:gz') as tar:
+            for member in tar.getmembers():
+                target_path = os.path.join(extract_path, member.name)
+                try:
+                    tar.extract(member, extract_path)
+                except Exception as e:
+                    print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate')
+                    ForceUpdateDependencies = True
+            print(f'Extraction of {content_file_path} to {extract_path} completed.')
+
+        if ForceUpdateDependencies:
+            install_packages()
+            ForceUpdateDependencies = False
+    else:
+        print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...')
+        scan_and_write('/usr/', '/content/usr_files.csv')
+
+        install_packages()
+
+        scan_and_write('/usr/', '/content/usr_files_new.csv')
+        changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv')
+
+        with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar:
+            for file in changed_files:
+                new_tar.add(file)
+                print(f'Added to tar: {file}')
+
+        os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True)
+        shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz')
+        print('Updated CachedRVC.tar.gz copied to Google Drive.')
+        print('Dependencies fully up to date; future runs should be faster.')
+

Applio-RVC-Fork/utils/i18n.py

@@ -0,0 +1,28 @@
+import locale
+import json
+import os
+
+
+def load_language_list(language):
+    with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
+        language_list = json.load(f)
+    return language_list
+
+
+class I18nAuto:
+    def __init__(self, language=None):
+        if language in ["Auto", None]:
+            language = "es_ES"
+        if not os.path.exists(f"./i18n/{language}.json"):
+            language = "es_ES"
+            language = "es_ES"
+        self.language = language
+        # print("Use Language:", language)
+        self.language_map = load_language_list(language)
+
+    def __call__(self, key):
+        return self.language_map.get(key, key)
+
+    def print(self):
+      # print("Use Language:", self.language)
+        print("")

Applio_(Mangio_RVC_Fork).ipynb

@@ -0,0 +1,169 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "izLwNF_8T1TK"
+      },
+      "outputs": [],
+      "source": [
+        "#@title <font color='#06ae56'>**🍏 Applio (Mangio-RVC-Fork)**</font>\n",
+        "import time\n",
+        "import os\n",
+        "import subprocess\n",
+        "import shutil\n",
+        "import threading\n",
+        "import base64\n",
+        "import threading\n",
+        "import time\n",
+        "from IPython.display import HTML, clear_output\n",
+        "\n",
+        "nosv_name1 = base64.b64decode(('ZXh0ZXJuYWxj').encode('ascii')).decode('ascii')\n",
+        "nosv_name2 = base64.b64decode(('b2xhYmNvZGU=').encode('ascii')).decode('ascii')\n",
+        "guebui = base64.b64decode(('V2U=').encode('ascii')).decode('ascii')\n",
+        "guebui2 = base64.b64decode(('YlVJ').encode('ascii')).decode('ascii')\n",
+        "pbestm = base64.b64decode(('cm12cGU=').encode('ascii')).decode('ascii')\n",
+        "tryre = base64.b64decode(('UmV0cmlldmFs').encode('ascii')).decode('ascii')\n",
+        "\n",
+        "xdsame = '/content/'+ tryre +'-based-Voice-Conversion-' + guebui + guebui2 +'/'\n",
+        "\n",
+        "collapsible_section = \"\"\"\n",
+        "<br>\n",
+        "<br>\n",
+        "<details style=\"border: 1px solid #ddd; border-radius: 5px; padding: 10px; margin-bottom: 10px;\">\n",
+        "    <summary open style=\"font-weight: bold; cursor: pointer;\">🚀 Click to learn more about Applio</summary>\n",
+        "    <div style=\"margin-left: 20px;\">\n",
+        "        <ul>\n",
+        "            <li><a href=\"https://github.com/Mangio621/Mangio-RVC-Fork\" style=\"color: #06ae56;\">Mangio-RVC-Fork</a> - Source of inspiration and base for this improved code, special thanks to the developers.</li>\n",
+        "            <li><a href=\"https://github.com/Anjok07/ultimatevocalremovergui\" style=\"color: #06ae56;\">UltimateVocalRemover</a> - Used for voice and instrument separation.</li>\n",
+        "            <li>Vidal, Blaise & Aitron - Contributors to the Applio version.</li>\n",
+        "            <li>kalomaze - Creator of external scripts that help the functioning of Applio.</li>\n",
+        "        </ul>\n",
+        "        <p style=\"color: #fff;\">Join and contribute to the project on <a href=\"https://github.com/IAHispano/Applio-RVC-Fork\" style=\"color: #06ae56;\">our GitHub repository</a>.</p>\n",
+        "    </div>\n",
+        "</details>\n",
+        "<br>\n",
+        "<button style=\"font-weight: bold; cursor: pointer; background-color: #06ae56; color: white; border: 1px solid #fff; border-radius: 4px; padding: 10px 20px; text-decoration: none;\" onclick=\"window.open('https://discord.gg/IAHispano', '_blank')\">🍏 Join our support Discord server (IA Hispano)</button>\n",
+        "<br>\n",
+        "<br>\n",
+        "\"\"\"\n",
+        "#@markdown **Settings:**\n",
+        "ForceUpdateDependencies = True\n",
+        "ForceNoMountDrive = False\n",
+        "#@markdown Restore your backup from Google Drive.\n",
+        "LoadBackupDrive = False #@param{type:\"boolean\"}\n",
+        "#@markdown Make regular backups of your model's training.\n",
+        "AutoBackups = True #@param{type:\"boolean\"}\n",
+        "if not os.path.exists(xdsame):\n",
+        " current_path = os.getcwd()\n",
+        " shutil.rmtree('/content/')\n",
+        " os.makedirs('/content/', exist_ok=True)\n",
+        "\n",
+        " os.chdir(current_path)\n",
+        " !git clone https://github.com/IAHispano/$nosv_name1$nosv_name2 /content/$tryre-based-Voice-Conversion-$guebui$guebui2/utils\n",
+        " clear_output()\n",
+        "\n",
+        " os.chdir(xdsame)\n",
+        " from utils.dependency import *\n",
+        " from utils.clonerepo_experimental import *\n",
+        " os.chdir(\"..\")\n",
+        "\n",
+        "\n",
+        "\n",
+        " setup_environment(ForceUpdateDependencies, ForceNoMountDrive)\n",
+        " clone_repository(True)\n",
+        "\n",
+        " !wget https://huggingface.co/lj1995/VoiceConversion$guebui$guebui2/resolve/main/rmvpe.pt -P /content/Retrieval-based-Voice-Conversion-$guebui$guebui2/\n",
+        " clear_output()\n",
+        "\n",
+        "base_path = \"/content/Retrieval-based-Voice-Conversion-$guebui$guebui2/\"\n",
+        "clear_output()\n",
+        "\n",
+        "\n",
+        "\n",
+        "from utils import backups\n",
+        "\n",
+        "LOGS_FOLDER = xdsame + '/logs'\n",
+        "if not os.path.exists(LOGS_FOLDER):\n",
+        "    os.makedirs(LOGS_FOLDER)\n",
+        "    clear_output()\n",
+        "\n",
+        "WEIGHTS_FOLDER = xdsame + '/logs' + '/weights'\n",
+        "if not os.path.exists(WEIGHTS_FOLDER):\n",
+        "    os.makedirs(WEIGHTS_FOLDER)\n",
+        "    clear_output()\n",
+        "\n",
+        "others_FOLDER = xdsame + '/audio-others'\n",
+        "if not os.path.exists(others_FOLDER):\n",
+        "    os.makedirs(others_FOLDER)\n",
+        "    clear_output()\n",
+        "\n",
+        "audio_outputs_FOLDER = xdsame + '/audio-outputs'\n",
+        "if not os.path.exists(audio_outputs_FOLDER):\n",
+        "    os.makedirs(audio_outputs_FOLDER)\n",
+        "    clear_output()\n",
+        "\n",
+        "if LoadBackupDrive:\n",
+        "    backups.import_google_drive_backup()\n",
+        "    clear_output()\n",
+        "\n",
+        "#@markdown Choose the language in which you want the interface to be available.\n",
+        "i18n_path = xdsame + 'i18n.py'\n",
+        "i18n_new_path = xdsame + 'utils/i18n.py'\n",
+        "try:\n",
+        "    if os.path.exists(i18n_path) and os.path.exists(i18n_new_path):\n",
+        "        shutil.move(i18n_new_path, i18n_path)\n",
+        "\n",
+        "    SelectedLanguage = \"en_US\" #@param [\"es_ES\", \"en_US\", \"zh_CN\", \"ar_AR\", \"id_ID\", \"pt_PT\", \"ru_RU\", \"ur_UR\", \"tr_TR\", \"it_IT\", \"de_DE\"]\n",
+        "    new_language_line = '            language = \"' + SelectedLanguage + '\"\\n'\n",
+        "#@markdown <a href=\"https://discord.gg/iahispano\"><font>If you need more help, feel free to join our official Discord server!</font></a>\n",
+        "    with open(i18n_path, 'r') as file:\n",
+        "        lines = file.readlines()\n",
+        "\n",
+        "    with open(i18n_path, 'w') as file:\n",
+        "        for index, line in enumerate(lines):\n",
+        "            if index == 14:\n",
+        "                file.write(new_language_line)\n",
+        "            else:\n",
+        "                file.write(line)\n",
+        "\n",
+        "except FileNotFoundError:\n",
+        "    print(\"Translation couldn't be applied successfully. Please restart the environment and run the cell again.\")\n",
+        "\n",
+        "def start_web_server():\n",
+        "    %cd /content/$tryre-based-Voice-Conversion-$guebui$guebui2\n",
+        "    %load_ext tensorboard\n",
+        "    clear_output()\n",
+        "    %tensorboard --logdir /content/$tryre-based-Voice-Conversion-$guebui$guebui2/logs\n",
+        "    !mkdir -p /content/$tryre-based-Voice-Conversion-$guebui$guebui2/audios\n",
+        "    display(HTML(collapsible_section))\n",
+        "    !python3 infer-web.py --colab --pycmd python3\n",
+        "\n",
+        "if AutoBackups:\n",
+        "  web_server_thread = threading.Thread(target=start_web_server)\n",
+        "  web_server_thread.start()\n",
+        "  backups.backup_files()\n",
+        "\n",
+        "else:\n",
+        "  start_web_server()"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

Dockerfile

@@ -0,0 +1,29 @@
+# syntax=docker/dockerfile:1
+
+FROM python:3.10-bullseye
+
+EXPOSE 7865
+
+WORKDIR /app
+
+COPY . .
+
+RUN apt update && apt install -y -qq ffmpeg aria2 && apt clean
+
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d assets/pretrained_v2/ -o D40k.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d assets/pretrained_v2/ -o G40k.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d assets/pretrained_v2/ -o f0D40k.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d assets/pretrained_v2/ -o f0G40k.pth
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d assets/uvr5_weights/ -o HP2-人声vocals+非人声instrumentals.pth
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d assets/uvr5_weights/ -o HP5-主旋律人声vocals+其他instrumentals.pth
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d assets/hubert -o hubert_base.pt
+
+RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d assets/hubert -o rmvpe.pt
+
+VOLUME [ "/app/weights", "/app/opt" ]
+
+CMD ["python3", "infer-web.py"]

Fixes/local_fixes.py

@@ -0,0 +1,136 @@
+import os
+import sys
+import time
+import shutil
+import requests
+import zipfile
+
+def insert_new_line(file_name, line_to_find, text_to_insert):
+    lines = []
+    with open(file_name, 'r', encoding='utf-8') as read_obj:
+        lines = read_obj.readlines()
+    already_exists = False
+    with open(file_name + '.tmp', 'w', encoding='utf-8') as write_obj:
+        for i in range(len(lines)):
+            write_obj.write(lines[i])
+            if lines[i].strip() == line_to_find:
+                # If next line exists and starts with sys.path.append, skip
+                if i+1 < len(lines) and lines[i+1].strip().startswith("sys.path.append"):
+                    print('It was already fixed! Skip adding a line...')
+                    already_exists = True
+                    break
+                else:
+                    write_obj.write(text_to_insert + '\n')
+    # If no existing sys.path.append line was found, replace the original file
+    if not already_exists:
+        os.replace(file_name + '.tmp', file_name)
+        return True
+    else:
+        # If existing line was found, delete temporary file
+        os.remove(file_name + '.tmp')
+        return False
+
+def replace_in_file(file_name, old_text, new_text):
+    with open(file_name, 'r', encoding='utf-8') as file:
+        file_contents = file.read()
+
+    if old_text in file_contents:
+        file_contents = file_contents.replace(old_text, new_text)
+        with open(file_name, 'w', encoding='utf-8') as file:
+            file.write(file_contents)
+            return True
+
+    return False
+
+if __name__ == "__main__":
+    current_path = os.getcwd()
+    file_name = os.path.join(current_path, "infer", "modules", "train", "extract", "extract_f0_print.py")
+    line_to_find = 'import numpy as np, logging'
+    text_to_insert = "sys.path.append(r'" + current_path + "')"
+    
+
+    success_1 = insert_new_line(file_name, line_to_find, text_to_insert)
+    if success_1:
+        print('The first operation was successful!')
+    else:
+        print('He skipped the first operation because it was already fixed!')
+
+    file_name = 'infer-web.py'
+    old_text = 'with gr.Blocks(theme=gr.themes.Soft()) as app:'
+    new_text = 'with gr.Blocks() as app:'
+
+    success_2 = replace_in_file(file_name, old_text, new_text)
+    if success_2:
+        print('The second operation was successful!')
+    else:
+        print('The second operation was omitted because it was already fixed!')
+
+    print('Local corrections successful! You should now be able to infer and train locally in Applio RVC Fork.')
+    
+    time.sleep(5)
+
+def find_torchcrepe_directory(directory):
+    """
+    Recursively searches for the topmost folder named 'torchcrepe' within a directory.
+    Returns the path of the directory found or None if none is found.
+    """
+    for root, dirs, files in os.walk(directory):
+        if 'torchcrepe' in dirs:
+            return os.path.join(root, 'torchcrepe')
+    return None
+
+def download_and_extract_torchcrepe():
+    url = 'https://github.com/maxrmorrison/torchcrepe/archive/refs/heads/master.zip'
+    temp_dir = 'temp_torchcrepe'
+    destination_dir = os.getcwd()
+
+    try:
+        torchcrepe_dir_path = os.path.join(destination_dir, 'torchcrepe')
+
+        if os.path.exists(torchcrepe_dir_path):
+            print("Skipping the torchcrepe download. The folder already exists.")
+            return
+
+        # Download the file
+        print("Starting torchcrepe download...")
+        response = requests.get(url)
+
+        # Raise an error if the GET request was unsuccessful
+        response.raise_for_status()
+        print("Download completed.")
+
+        # Save the downloaded file
+        zip_file_path = os.path.join(temp_dir, 'master.zip')
+        os.makedirs(temp_dir, exist_ok=True)
+        with open(zip_file_path, 'wb') as file:
+            file.write(response.content)
+        print(f"Zip file saved to {zip_file_path}")
+
+        # Extract the zip file
+        print("Extracting content...")
+        with zipfile.ZipFile(zip_file_path, 'r') as zip_file:
+            zip_file.extractall(temp_dir)
+        print("Extraction completed.")
+
+        # Locate the torchcrepe folder and move it to the destination directory
+        torchcrepe_dir = find_torchcrepe_directory(temp_dir)
+        if torchcrepe_dir:
+            shutil.move(torchcrepe_dir, destination_dir)
+            print(f"Moved the torchcrepe directory to {destination_dir}!")
+        else:
+            print("The torchcrepe directory could not be located.")
+
+    except Exception as e:
+        print("Torchcrepe not successfully downloaded", e)
+
+    # Clean up temporary directory
+    if os.path.exists(temp_dir):
+        shutil.rmtree(temp_dir)
+
+# Run the function
+download_and_extract_torchcrepe()
+
+temp_dir = 'temp_torchcrepe'
+
+if os.path.exists(temp_dir):
+    shutil.rmtree(temp_dir)

Fixes/tensor-launch.py

@@ -0,0 +1,15 @@
+import threading
+import time
+from tensorboard import program
+import os
+
+log_path = "logs"
+
+if __name__ == "__main__":
+    tb = program.TensorBoard()
+    tb.configure(argv=[None, '--logdir', log_path])
+    url = tb.launch()
+    print(f'Tensorboard can be accessed at: {url}')
+
+    while True:
+        time.sleep(600)  # Keep the main thread running

LICENSE

@@ -0,0 +1,59 @@
+MIT License
+
+Copyright (c) 2023 liujing04
+Copyright (c) 2023 源文雨
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+The licenses for related libraries are as follows:
+
+ContentVec
+https://github.com/auspicious3000/contentvec/blob/main/LICENSE
+MIT License
+
+VITS
+https://github.com/jaywalnut310/vits/blob/main/LICENSE
+MIT License
+
+HIFIGAN
+https://github.com/jik876/hifi-gan/blob/master/LICENSE
+MIT License
+
+gradio
+https://github.com/gradio-app/gradio/blob/main/LICENSE
+Apache License 2.0
+
+ffmpeg
+https://github.com/FFmpeg/FFmpeg/blob/master/COPYING.LGPLv3
+https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2021-02-28-12-32/ffmpeg-n4.3.2-160-gfbb9368226-win64-lgpl-4.3.zip
+LPGLv3 License
+MIT License
+
+ultimatevocalremovergui
+https://github.com/Anjok07/ultimatevocalremovergui/blob/master/LICENSE
+https://github.com/yang123qwe/vocal_separation_by_uvr5
+MIT License
+
+audio-slicer
+https://github.com/openvpi/audio-slicer/blob/main/LICENSE
+MIT License
+
+PySimpleGUI
+https://github.com/PySimpleGUI/PySimpleGUI/blob/master/license.txt
+LPGLv3 License

LazyImport.py

@@ -0,0 +1,13 @@
+from importlib.util import find_spec, LazyLoader, module_from_spec
+from sys import modules
+
+def lazyload(name):
+    if name in modules:
+        return modules[name]
+    else:
+        spec = find_spec(name)
+        loader = LazyLoader(spec.loader)
+        module = module_from_spec(spec)
+        modules[name] = module
+        loader.exec_module(module)
+        return module

MDX-Net_Colab.ipynb

@@ -0,0 +1,524 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wX9xzLur4tus"
+      },
+      "source": [
+        "# MDX-Net Colab\n",
+        "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
+        "  <img src=\"https://github.githubassets.com/pinned-octocat.svg\" alt=\"icon1\" style=\"margin-right:10px; height: 20px;\" width=\"1.5%\">\n",
+        "  <span>Trained models provided in this notebook are from <a href=\"https://github.com/Anjok07\">UVR-GUI</a>.</span>\n",
+        "</div>\n",
+        "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
+        "  <img src=\"https://github.com/Anjok07/ultimatevocalremovergui/raw/master/gui_data/img/GUI-Icon.ico\" alt=\"icon2\" style=\"margin-right:10px; height: 20px;margin-top:10px\" width=\"1.5%\">\n",
+        "  <span>OFFICIAL UVR GITHUB PAGE: <a href=\"https://github.com/Anjok07/ultimatevocalremovergui\">here</a>.</span>\n",
+        "</div>\n",
+        "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
+        "  <img src=\"https://avatars.githubusercontent.com/u/24620594\" alt=\"icon3\" style=\"margin-right:10px; height: 20px;\" width=\"1.5%\">\n",
+        "  <span>OFFICIAL CLI Version: <a href=\"https://github.com/tsurumeso/vocal-remover\">here</a>.</span>\n",
+        "</div>\n",
+        "<div style=\"display:flex; align-items:center; font-size: 16px;\">\n",
+        "  <img src=\"https://icons.getbootstrap.com/assets/icons/discord.svg\" alt=\"icon4\" style=\"margin-right:10px; height: 20px;\" width=\"1.5%\">\n",
+        "  <span>Join our <a href=\"https://cutt.ly/0TcDjmo\">Discord server</a>!</span>\n",
+        "</div>\n",
+        "<sup><br>Ultimate Vocal Remover (unofficial)</sup>\n",
+        "<sup><br>MDX-Net by <a href=\"https://github.com/kuielab\">kuielab</a> and adapted for Colaboratory by <a href=\"https://www.youtube.com/channel/UC0NiSV1jLMH-9E09wiDVFYw\">AudioHacker</a>.</sup>\n",
+        "\n",
+        "<sup><br>Your support means a lot to me. If you enjoy my work, please consider buying me a ko-fi:<br></sup>\n",
+        "[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/X8X6M8FR0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3J69RV7G8ocb",
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "import os\n",
+        "import os.path\n",
+        "import gc\n",
+        "import psutil\n",
+        "import requests\n",
+        "import subprocess\n",
+        "import glob\n",
+        "import time\n",
+        "import logging\n",
+        "import sys\n",
+        "from bs4 import BeautifulSoup\n",
+        "from google.colab import drive, files, output\n",
+        "from IPython.display import Audio, display\n",
+        "\n",
+        "if \"first_cell_ran\" in locals():\n",
+        "    print(\"You've ran this cell for this session. No need to run it again.\\nif you think something went wrong or you want to change mounting path, restart the runtime.\")\n",
+        "else:\n",
+        "    print('Setting up... please wait around 1-2 minute(s).')\n",
+        "\n",
+        "    branch = \"https://github.com/NaJeongMo/Colab-for-MDX_B\"\n",
+        "\n",
+        "    model_params = \"https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/model_data.json\"\n",
+        "    _Models = \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/\"\n",
+        "    # _models = \"https://pastebin.com/raw/jBzYB8vz\"\n",
+        "    _models = \"https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json\"\n",
+        "    stem_naming = \"https://pastebin.com/raw/mpH4hRcF\"\n",
+        "    arl_check_endpoint = 'https://dz.doubledouble.top/check' # param: arl?=<>\n",
+        "\n",
+        "    file_folder = \"Colab-for-MDX_B\"\n",
+        "\n",
+        "    model_ids = requests.get(_models).json()\n",
+        "    model_ids = model_ids[\"mdx_download_list\"].values()\n",
+        "\n",
+        "    model_params = requests.get(model_params).json()\n",
+        "    stem_naming = requests.get(stem_naming).json()\n",
+        "\n",
+        "    os.makedirs(\"tmp_models\", exist_ok=True)\n",
+        "\n",
+        "    # @markdown If you don't wish to mount google drive, uncheck this box.\n",
+        "    MountDrive = True  # @param{type:\"boolean\"}\n",
+        "    # @markdown The path for the drive to be mounted: Please be cautious when modifying this as it can cause issues if not done properly.\n",
+        "    mounting_path = \"/content/drive/MyDrive\"  # @param [\"snippets:\",\"/content/drive/MyDrive\",\"/content/drive/Shareddrives/<your shared drive name>\", \"/content/drive/Shareddrives/Shared Drive\"]{allow-input: true}\n",
+        "    # @markdown Force update and disregard local changes: discards all local modifications in your repository, effectively replacing all files with the versions from the original commit.\n",
+        "    force_update = False  # @param{type:\"boolean\"}\n",
+        "    # @markdown Auto Update (does not discard your changes)\n",
+        "    auto_update = True  # @param{type:\"boolean\"}\n",
+        "\n",
+        "\n",
+        "    reqs_apt = []  # !sudo apt-get install\n",
+        "    reqs_pip = [\"librosa>=0.6.3,<0.9\", \"onnxruntime_gpu\", \"deemix\", \"yt_dlp\"]  # pip3 install\n",
+        "\n",
+        "    class hide_opt:  # hide outputs\n",
+        "        def __enter__(self):\n",
+        "            self._original_stdout = sys.stdout\n",
+        "            sys.stdout = open(os.devnull, \"w\")\n",
+        "\n",
+        "        def __exit__(self, exc_type, exc_val, exc_tb):\n",
+        "            sys.stdout.close()\n",
+        "            sys.stdout = self._original_stdout\n",
+        "\n",
+        "    def get_size(bytes, suffix=\"B\"):  # read ram\n",
+        "        global svmem\n",
+        "        factor = 1024\n",
+        "        for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\"]:\n",
+        "            if bytes < factor:\n",
+        "                return f\"{bytes:.2f}{unit}{suffix}\"\n",
+        "            bytes /= factor\n",
+        "        svmem = psutil.virtual_memory()\n",
+        "\n",
+        "\n",
+        "    print('installing requirements...',end=' ')\n",
+        "    with hide_opt():\n",
+        "        for x in reqs_apt:\n",
+        "            subprocess.run([\"sudo\", \"apt-get\", \"install\", x])\n",
+        "        for x in reqs_pip:\n",
+        "            subprocess.run([\"python3\", \"-m\", \"pip\", \"install\", x])\n",
+        "    print('done')\n",
+        "\n",
+        "    def install_or_mount_drive():\n",
+        "        print(\n",
+        "            \"Please log in to your account by following the prompts in the pop-up tab.\\nThis step is necessary to install the files to your Google Drive.\\nIf you have any concerns about the safety of this notebook, you can choose not to mount your drive by unchecking the \\\"MountDrive\\\" checkbox.\"\n",
+        "        )\n",
+        "        drive.mount(\"/content/drive\", force_remount=True)\n",
+        "        os.chdir(mounting_path)\n",
+        "        # check if previous installation is done\n",
+        "        if os.path.exists(os.path.join(mounting_path, file_folder)):\n",
+        "            # update checking\n",
+        "            os.chdir(file_folder)\n",
+        "\n",
+        "            if force_update:\n",
+        "                print('Force updating...')\n",
+        "\n",
+        "                commands = [\n",
+        "                    [\"git\", \"pull\"],\n",
+        "                    [\"git\", \"checkout\", \"--\", \".\"],\n",
+        "                ]\n",
+        "\n",
+        "                for cmd in commands:\n",
+        "                    subprocess.run(cmd)\n",
+        "\n",
+        "            elif auto_update:\n",
+        "                print('Checking for updates...')\n",
+        "                commands = [\n",
+        "                    [\"git\", \"pull\"],\n",
+        "                ]\n",
+        "\n",
+        "                for cmd in commands:\n",
+        "                    subprocess.run(cmd)\n",
+        "        else:\n",
+        "            subprocess.run([\"git\", \"clone\", \"https://github.com/NaJeongMo/Colab-for-MDX_B.git\"])\n",
+        "            os.chdir(file_folder)\n",
+        "\n",
+        "    def use_uvr_without_saving():\n",
+        "        global mounting_path\n",
+        "        print(\"Notice: files won't be saved to personal drive.\")\n",
+        "        print(f\"Downloading {file_folder}...\", end=\" \")\n",
+        "        mounting_path = \"/content\"\n",
+        "        with hide_opt():\n",
+        "            os.chdir(mounting_path)\n",
+        "            subprocess.run([\"git\", \"clone\", \"https://github.com/NaJeongMo/Colab-for-MDX_B.git\"])\n",
+        "            os.chdir(file_folder)\n",
+        "\n",
+        "    if MountDrive:\n",
+        "        install_or_mount_drive()\n",
+        "    else:\n",
+        "        use_uvr_without_saving()\n",
+        "    print(\"done!\")\n",
+        "    if not os.path.exists(\"tracks\"):\n",
+        "        os.mkdir(\"tracks\")\n",
+        "\n",
+        "    print('Importing required libraries...',end=' ')\n",
+        "\n",
+        "    import os\n",
+        "    import mdx\n",
+        "    import librosa\n",
+        "    import torch\n",
+        "    import soundfile as sf\n",
+        "    import numpy as np\n",
+        "    import yt_dlp\n",
+        "\n",
+        "    from deezer import Deezer\n",
+        "    from deezer import TrackFormats\n",
+        "    import deemix\n",
+        "    from deemix.settings import load as loadSettings\n",
+        "    from deemix.downloader import Downloader\n",
+        "    from deemix import generateDownloadObject\n",
+        "\n",
+        "    logger = logging.getLogger(\"yt_dlp\")\n",
+        "    logger.setLevel(logging.ERROR)\n",
+        "\n",
+        "    def id_to_ptm(mkey):\n",
+        "        if mkey in model_ids:\n",
+        "            mpath = f\"/content/tmp_models/{mkey}\"\n",
+        "            if not os.path.exists(f'/content/tmp_models/{mkey}'):\n",
+        "                print('Downloading model...',end=' ')\n",
+        "                subprocess.run(\n",
+        "                    [\"wget\", _Models+mkey, \"-O\", mpath]\n",
+        "                )\n",
+        "                print(f'saved to {mpath}')\n",
+        "                # get_ipython().system(f'gdown {model_id} -O /content/tmp_models/{mkey}')\n",
+        "                return mpath\n",
+        "            else:\n",
+        "                return mpath\n",
+        "        else:\n",
+        "            mpath = f'models/{mkey}'\n",
+        "            return mpath\n",
+        "\n",
+        "    def prepare_mdx(custom_param=False, dim_f=None, dim_t=None, n_fft=None, stem_name=None, compensation=None):\n",
+        "        device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')\n",
+        "        if custom_param:\n",
+        "            assert not (dim_f is None or dim_t is None or n_fft is None or compensation is None), 'Custom parameter selected, but incomplete parameters are provided.'\n",
+        "            mdx_model = mdx.MDX_Model(\n",
+        "                device,\n",
+        "                dim_f = dim_f,\n",
+        "                dim_t = dim_t,\n",
+        "                n_fft = n_fft,\n",
+        "                stem_name=stem_name,\n",
+        "                compensation=compensation\n",
+        "            )\n",
+        "        else:\n",
+        "            model_hash = mdx.MDX.get_hash(onnx)\n",
+        "            if model_hash in model_params:\n",
+        "                mp = model_params.get(model_hash)\n",
+        "                mdx_model = mdx.MDX_Model(\n",
+        "                    device,\n",
+        "                    dim_f = mp[\"mdx_dim_f_set\"],\n",
+        "                    dim_t = 2**mp[\"mdx_dim_t_set\"],\n",
+        "                    n_fft = mp[\"mdx_n_fft_scale_set\"],\n",
+        "                    stem_name=mp[\"primary_stem\"],\n",
+        "                    compensation=compensation if not custom_param and compensation is not None else mp[\"compensate\"]\n",
+        "                )\n",
+        "        return mdx_model\n",
+        "\n",
+        "    def run_mdx(onnx, mdx_model,filename,diff=False,suffix=None,diff_suffix=None, denoise=False, m_threads=1):\n",
+        "        mdx_sess = mdx.MDX(onnx,mdx_model)\n",
+        "        print(f\"Processing: {filename}\")\n",
+        "        wave, sr = librosa.load(filename,mono=False, sr=44100)\n",
+        "        # normalizing input wave gives better output\n",
+        "        peak = max(np.max(wave), abs(np.min(wave)))\n",
+        "        wave /= peak\n",
+        "        if denoise:\n",
+        "            wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))\n",
+        "            wave_processed *= 0.5\n",
+        "        else:\n",
+        "            wave_processed = mdx_sess.process_wave(wave, m_threads)\n",
+        "        # return to previous peak\n",
+        "        wave_processed *= peak\n",
+        "\n",
+        "        stem_name = mdx_model.stem_name if suffix is None else suffix # use suffix if provided\n",
+        "        save_path = f\"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav\"\n",
+        "        save_path = os.path.join(\n",
+        "                'separated',\n",
+        "                save_path\n",
+        "            )\n",
+        "        sf.write(\n",
+        "            save_path,\n",
+        "            wave_processed.T,\n",
+        "            sr\n",
+        "        )\n",
+        "\n",
+        "        print(f'done, saved to: {save_path}')\n",
+        "\n",
+        "        if diff:\n",
+        "            diff_stem_name = stem_naming.get(stem_name) if diff_suffix is None else diff_suffix # use suffix if provided\n",
+        "            stem_name = f\"{stem_name}_diff\" if diff_stem_name is None else diff_stem_name\n",
+        "            save_path = f\"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav\"\n",
+        "            save_path = os.path.join(\n",
+        "                    'separated',\n",
+        "                    save_path\n",
+        "                )\n",
+        "            sf.write(\n",
+        "                save_path,\n",
+        "                (-wave_processed.T*mdx_model.compensation)+wave.T,\n",
+        "                sr\n",
+        "            )\n",
+        "            print(f'invert done, saved to: {save_path}')\n",
+        "        del mdx_sess, wave_processed, wave\n",
+        "        gc.collect()\n",
+        "\n",
+        "    def is_valid_url(url):\n",
+        "        import re\n",
+        "        regex = re.compile(\n",
+        "            r'^https?://'\n",
+        "            r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+[A-Z]{2,6}\\.?|'\n",
+        "            r'localhost|'\n",
+        "            r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})'\n",
+        "            r'(?::\\d+)?'\n",
+        "            r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n",
+        "        return url is not None and regex.search(url)\n",
+        "\n",
+        "    def download_deezer(link, arl, fmt='FLAC'):\n",
+        "        match fmt:\n",
+        "            case 'FLAC':\n",
+        "                bitrate = TrackFormats.FLAC\n",
+        "            case 'MP3_320':\n",
+        "                bitrate = TrackFormats.MP3_320\n",
+        "            case 'MP3_128':\n",
+        "                bitrate = TrackFormats.MP3_128\n",
+        "            case _:\n",
+        "                bitrate = TrackFormats.MP3_128\n",
+        "\n",
+        "        dz = Deezer()\n",
+        "        settings = loadSettings('dz_config')\n",
+        "        settings['downloadLocation'] = './tracks'\n",
+        "        if not dz.login_via_arl(arl.strip()):\n",
+        "            raise Exception('Error while logging in with provided ARL.')\n",
+        "        downloadObject = generateDownloadObject(dz, link, bitrate)\n",
+        "        print(f'Downloading {downloadObject.type}: \"{downloadObject.title}\" by {downloadObject.artist}...',end=' ',flush=True)\n",
+        "        Downloader(dz, downloadObject, settings).start()\n",
+        "        print(f'done.')\n",
+        "\n",
+        "        path_to_audio = []\n",
+        "        for file in downloadObject.files:\n",
+        "            path_to_audio.append(file[\"path\"])\n",
+        "\n",
+        "        return path_to_audio\n",
+        "\n",
+        "    def download_link(url):\n",
+        "        ydl_opts = {\n",
+        "            'format': 'bestvideo+bestaudio/best',\n",
+        "            'outtmpl': '%(title)s.%(ext)s',\n",
+        "            'nocheckcertificate': True,\n",
+        "            'ignoreerrors': True,\n",
+        "            'no_warnings': True,\n",
+        "            'extractaudio': True,\n",
+        "        }\n",
+        "        with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n",
+        "            result = ydl.extract_info(url, download=True)\n",
+        "            download_path = ydl.prepare_filename(result)\n",
+        "        return download_path\n",
+        "\n",
+        "    print('finished setting up!')\n",
+        "    first_cell_ran = True"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4hd1TzEGCiRo",
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "if 'first_cell_ran' in locals():\n",
+        "    os.chdir(mounting_path + '/' + file_folder + '/')\n",
+        "    #parameter markdowns-----------------\n",
+        "    #@markdown ### Input files\n",
+        "    #@markdown track filename: Upload your songs to the \"tracks\" folder. You may provide multiple links/files by spliting them with ;\n",
+        "    filename = \"https://deezer.com/album/281108671\" #@param {type:\"string\"}\n",
+        "    #@markdown onnx model (if you have your own model, upload it in models folder)\n",
+        "    onnx = \"UVR-MDX-NET-Inst_HQ_3.onnx\" #@param [\"Kim_Inst.onnx\", \"Kim_Vocal_1.onnx\", \"Kim_Vocal_2.onnx\", \"kuielab_a_bass.onnx\", \"kuielab_a_drums.onnx\", \"kuielab_a_other.onnx\", \"kuielab_a_vocals.onnx\", \"kuielab_b_bass.onnx\", \"kuielab_b_drums.onnx\", \"kuielab_b_other.onnx\", \"kuielab_b_vocals.onnx\", \"Reverb_HQ_By_FoxJoy.onnx\", \"UVR-MDX-NET-Inst_1.onnx\", \"UVR-MDX-NET-Inst_2.onnx\", \"UVR-MDX-NET-Inst_3.onnx\", \"UVR-MDX-NET-Inst_HQ_1.onnx\", \"UVR-MDX-NET-Inst_HQ_2.onnx\", \"UVR-MDX-NET-Inst_Main.onnx\", \"UVR_MDXNET_1_9703.onnx\", \"UVR_MDXNET_2_9682.onnx\", \"UVR_MDXNET_3_9662.onnx\", \"UVR_MDXNET_9482.onnx\", \"UVR_MDXNET_KARA.onnx\", \"UVR_MDXNET_KARA_2.onnx\", \"UVR_MDXNET_Main.onnx\", \"UVR-MDX-NET-Inst_HQ_3.onnx\", \"UVR-MDX-NET-Voc_FT.onnx\"]{allow-input: true}\n",
+        "    #@markdown process all: processes all tracks inside tracks/ folder instead. (filename will be ignored!)\n",
+        "    process_all = False  # @param{type:\"boolean\"}\n",
+        "\n",
+        "\n",
+        "    #@markdown ### Settings\n",
+        "    #@markdown invert: get difference between input and output (e.g get Instrumental out of Vocals)\n",
+        "    invert = True  # @param{type:\"boolean\"}\n",
+        "    #@markdown denoise: get rid of MDX noise. (This processes input track twice)\n",
+        "    denoise = True  # @param{type:\"boolean\"}\n",
+        "    #@markdown m_threads: like batch size, processes input wave in n threads. (beneficial for CPU)\n",
+        "    m_threads = 2 #@param {type:\"slider\", min:1, max:8, step:1}\n",
+        "\n",
+        "    #@markdown ### Custom model parameters (Only use this if you're using new/unofficial/custom models)\n",
+        "    #@markdown Use custom model parameters. (Default: unchecked, or auto)\n",
+        "    use_custom_parameter = False  # @param{type:\"boolean\"}\n",
+        "    #@markdown Output file suffix (usually the stem name e.g Vocals)\n",
+        "    suffix = \"Vocals_custom\" #@param [\"Vocals\", \"Drums\", \"Bass\", \"Other\"]{allow-input: true}\n",
+        "    suffix_invert = \"Instrumental_custom\" #@param [\"Instrumental\", \"Drumless\", \"Bassless\", \"Instruments\"]{allow-input: true}\n",
+        "    #@markdown Model parameters\n",
+        "    dim_f = 3072 #@param {type: \"integer\"}\n",
+        "    dim_t = 256 #@param {type: \"integer\"}\n",
+        "    n_fft = 6144 #@param {type: \"integer\"}\n",
+        "    #@markdown use custom compensation: only if you have your own compensation value for your model. this still apply even if you don't have use_custom_parameter checked (Default: unchecked, or auto)\n",
+        "    use_custom_compensation = False  # @param{type:\"boolean\"}\n",
+        "    compensation = 1.000 #@param {type: \"number\"}\n",
+        "\n",
+        "    #@markdown ### Extras\n",
+        "    #@markdown Deezer arl: paste your ARL here for deezer tracks directly!\n",
+        "    arl = \"\" #@param {type:\"string\"}\n",
+        "    #@markdown Track format: select track quality/format\n",
+        "    track_format = \"FLAC\" #@param [\"FLAC\",\"MP3_320\",\"MP3_128\"]\n",
+        "    #@markdown Print settings being used in the run\n",
+        "    print_settings = True  # @param{type:\"boolean\"}\n",
+        "\n",
+        "\n",
+        "\n",
+        "    onnx = id_to_ptm(onnx)\n",
+        "    compensation = compensation if use_custom_compensation or use_custom_parameter else None\n",
+        "    mdx_model = prepare_mdx(use_custom_parameter, dim_f, dim_t, n_fft, compensation=compensation)\n",
+        "\n",
+        "    filename_split = filename.split(';')\n",
+        "\n",
+        "    usable_files = []\n",
+        "\n",
+        "    if not process_all:\n",
+        "        for fn in filename_split:\n",
+        "            fn = fn.strip()\n",
+        "            if is_valid_url(fn):\n",
+        "                dm, ltype, lid = deemix.parseLink(fn)\n",
+        "                if ltype and lid:\n",
+        "                    usable_files += download_deezer(fn, arl, track_format)\n",
+        "                else:\n",
+        "                    print('downloading link...',end=' ')\n",
+        "                    usable_files+=[download_link(fn)]\n",
+        "                    print('done')\n",
+        "            else:\n",
+        "                usable_files.append(os.path.join('tracks',fn))\n",
+        "    else:\n",
+        "        for fn in glob.glob('tracks/*'):\n",
+        "            usable_files.append(fn)\n",
+        "    for filename in usable_files:\n",
+        "        suffix_naming = suffix if use_custom_parameter else None\n",
+        "        diff_suffix_naming = suffix_invert if use_custom_parameter else None\n",
+        "        run_mdx(onnx, mdx_model, filename, diff=invert,suffix=suffix_naming,diff_suffix=diff_suffix_naming,denoise=denoise)\n",
+        "\n",
+        "    if print_settings:\n",
+        "        print()\n",
+        "        print('[MDX-Net_Colab settings used]')\n",
+        "        print(f'Model used: {onnx}')\n",
+        "        print(f'Model MD5: {mdx.MDX.get_hash(onnx)}')\n",
+        "        print(f'Using de-noise: {denoise}')\n",
+        "        print(f'Model parameters:')\n",
+        "        print(f'    -dim_f: {mdx_model.dim_f}')\n",
+        "        print(f'    -dim_t: {mdx_model.dim_t}')\n",
+        "        print(f'    -n_fft: {mdx_model.n_fft}')\n",
+        "        print(f'    -compensation: {mdx_model.compensation}')\n",
+        "        print()\n",
+        "        print('[Input file]')\n",
+        "        print('filename(s): ')\n",
+        "        for filename in usable_files:\n",
+        "            print(f'    -{filename}')\n",
+        "\n",
+        "    del mdx_model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Guide\n",
+        "\n",
+        "This tutorial guide will walk you through the steps to use the features of this Colab notebook.\n",
+        "\n",
+        "## Mount Drive\n",
+        "\n",
+        "To mount your Google Drive, follow these steps:\n",
+        "\n",
+        "1. Check the box next to \"MountDrive\" if you want to mount Google Drive.\n",
+        "2. Modify the \"mounting_path\" if you want to specify a different path for the drive to be mounted. **Note:** Be cautious when modifying this path as it can cause issues if not done properly.\n",
+        "3. Check the box next to \"Force update and disregard local changes\" if you want to discard all local modifications in your repository and replace the files with the versions from the original commit.\n",
+        "4. Check the box next to \"Auto Update\" if you want to automatically update without discarding your changes. Leave it unchecked if you want to manually update.\n",
+        "\n",
+        "## Input Files\n",
+        "\n",
+        "To upload your songs, follow these steps:\n",
+        "\n",
+        "1. Specify the \"track filename\" for your songs. You can provide multiple links or files by separating them with a semicolon (;).\n",
+        "2. Upload your songs to the \"tracks\" folder.\n",
+        "\n",
+        "## ONNX Model\n",
+        "\n",
+        "If you have your own ONNX model, follow these steps:\n",
+        "\n",
+        "1. Upload your model to the \"models\" folder.\n",
+        "2. Specify the \"onnx\" filename for your model.\n",
+        "\n",
+        "## Processing\n",
+        "\n",
+        "To process your tracks, follow these steps:\n",
+        "\n",
+        "1. If you want to process all tracks inside the \"tracks\" folder, check the box next to \"process_all\" and ignore the \"filename\" field.\n",
+        "2. Specify any additional settings you want:\n",
+        "   - Check the box next to \"invert\" to get the difference between input and output (e.g., get Instrumental out of Vocals).\n",
+        "   - Check the box next to \"denoise\" to get rid of MDX noise. This processes the input track twice.\n",
+        "   - Specify custom model parameters only if you're using new/unofficial/custom models. Use the \"use_custom_parameter\" checkbox to enable this feature.\n",
+        "   - Specify the output file suffix, which is usually the stem name (e.g., Vocals). Use the \"suffix\" field to specify the suffix for normal processing and the \"suffix_invert\" field for inverted processing.\n",
+        "\n",
+        "## Model Parameters\n",
+        "\n",
+        "Specify the following custom model parameters if applicable:\n",
+        "\n",
+        "- \"dim_f\": The value for the `dim_f` parameter.\n",
+        "- \"dim_t\": The value for the `dim_t` parameter.\n",
+        "- \"n_fft\": The value for the `n_fft` parameter.\n",
+        "- Check the box next to \"use_custom_compensation\" if you have your own compensation value for your model. Specify the compensation value in the \"compensation\" field.\n",
+        "\n",
+        "## Extras\n",
+        "\n",
+        "If you're working with Deezer tracks, paste your ARL (Authentication Request Library) in the \"arl\" field to directly access the tracks.\n",
+        "\n",
+        "Specify the \"Track format\" by selecting the desired quality/format for the track.\n",
+        "\n",
+        "To print the settings being used in the run, check the box next to \"print_settings\".\n",
+        "\n",
+        "That's it! You're now ready to use this Colab notebook. Enjoy!\n",
+        "\n",
+        "## For more detailed guide, proceed to this <a href=\"https://docs.google.com/document/d/17fjNvJzj8ZGSer7c7OFe_CNfUKbAxEh_OBv94ZdRG5c\">link</a>.\n",
+        "credits: (discord) deton24"
+      ],
+      "metadata": {
+        "id": "tMVwX5RhZSRP"
+      }
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

MDXNet.py

@@ -0,0 +1,272 @@
+import soundfile as sf
+import torch, pdb, os, warnings, librosa
+import numpy as np
+import onnxruntime as ort
+from tqdm import tqdm
+import torch
+
+dim_c = 4
+
+
+class Conv_TDF_net_trim:
+    def __init__(
+        self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
+    ):
+        super(Conv_TDF_net_trim, self).__init__()
+
+        self.dim_f = dim_f
+        self.dim_t = 2**dim_t
+        self.n_fft = n_fft
+        self.hop = hop
+        self.n_bins = self.n_fft // 2 + 1
+        self.chunk_size = hop * (self.dim_t - 1)
+        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
+            device
+        )
+        self.target_name = target_name
+        self.blender = "blender" in model_name
+
+        out_c = dim_c * 4 if target_name == "*" else dim_c
+        self.freq_pad = torch.zeros(
+            [1, out_c, self.n_bins - self.dim_f, self.dim_t]
+        ).to(device)
+
+        self.n = L // 2
+
+    def stft(self, x):
+        x = x.reshape([-1, self.chunk_size])
+        x = torch.stft(
+            x,
+            n_fft=self.n_fft,
+            hop_length=self.hop,
+            window=self.window,
+            center=True,
+            return_complex=True,
+        )
+        x = torch.view_as_real(x)
+        x = x.permute([0, 3, 1, 2])
+        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
+            [-1, dim_c, self.n_bins, self.dim_t]
+        )
+        return x[:, :, : self.dim_f]
+
+    def istft(self, x, freq_pad=None):
+        freq_pad = (
+            self.freq_pad.repeat([x.shape[0], 1, 1, 1])
+            if freq_pad is None
+            else freq_pad
+        )
+        x = torch.cat([x, freq_pad], -2)
+        c = 4 * 2 if self.target_name == "*" else 2
+        x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
+            [-1, 2, self.n_bins, self.dim_t]
+        )
+        x = x.permute([0, 2, 3, 1])
+        x = x.contiguous()
+        x = torch.view_as_complex(x)
+        x = torch.istft(
+            x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
+        )
+        return x.reshape([-1, c, self.chunk_size])
+
+
+def get_models(device, dim_f, dim_t, n_fft):
+    return Conv_TDF_net_trim(
+        device=device,
+        model_name="Conv-TDF",
+        target_name="vocals",
+        L=11,
+        dim_f=dim_f,
+        dim_t=dim_t,
+        n_fft=n_fft,
+    )
+
+
+warnings.filterwarnings("ignore")
+cpu = torch.device("cpu")
+if torch.cuda.is_available():
+    device = torch.device("cuda:0")
+elif torch.backends.mps.is_available():
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+
+
+class Predictor:
+    def __init__(self, args):
+        self.args = args
+        self.model_ = get_models(
+            device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
+        )
+        self.model = ort.InferenceSession(
+            os.path.join(args.onnx, self.model_.target_name + ".onnx"),
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+        )
+        print("onnx load done")
+
+    def demix(self, mix):
+        samples = mix.shape[-1]
+        margin = self.args.margin
+        chunk_size = self.args.chunks * 44100
+        assert not margin == 0, "margin cannot be zero!"
+        if margin > chunk_size:
+            margin = chunk_size
+
+        segmented_mix = {}
+
+        if self.args.chunks == 0 or samples < chunk_size:
+            chunk_size = samples
+
+        counter = -1
+        for skip in range(0, samples, chunk_size):
+            counter += 1
+
+            s_margin = 0 if counter == 0 else margin
+            end = min(skip + chunk_size + margin, samples)
+
+            start = skip - s_margin
+
+            segmented_mix[skip] = mix[:, start:end].copy()
+            if end == samples:
+                break
+
+        sources = self.demix_base(segmented_mix, margin_size=margin)
+        """
+        mix:(2,big_sample)
+        segmented_mix:offset->(2,small_sample)
+        sources:(1,2,big_sample)
+        """
+        return sources
+
+    def demix_base(self, mixes, margin_size):
+        chunked_sources = []
+        progress_bar = tqdm(total=len(mixes))
+        progress_bar.set_description("Processing")
+        for mix in mixes:
+            cmix = mixes[mix]
+            sources = []
+            n_sample = cmix.shape[1]
+            model = self.model_
+            trim = model.n_fft // 2
+            gen_size = model.chunk_size - 2 * trim
+            pad = gen_size - n_sample % gen_size
+            mix_p = np.concatenate(
+                (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
+            )
+            mix_waves = []
+            i = 0
+            while i < n_sample + pad:
+                waves = np.array(mix_p[:, i : i + model.chunk_size])
+                mix_waves.append(waves)
+                i += gen_size
+            mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
+            with torch.no_grad():
+                _ort = self.model
+                spek = model.stft(mix_waves)
+                if self.args.denoise:
+                    spec_pred = (
+                        -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
+                        + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
+                    )
+                    tar_waves = model.istft(torch.tensor(spec_pred))
+                else:
+                    tar_waves = model.istft(
+                        torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
+                    )
+                tar_signal = (
+                    tar_waves[:, :, trim:-trim]
+                    .transpose(0, 1)
+                    .reshape(2, -1)
+                    .numpy()[:, :-pad]
+                )
+
+                start = 0 if mix == 0 else margin_size
+                end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
+                if margin_size == 0:
+                    end = None
+                sources.append(tar_signal[:, start:end])
+
+                progress_bar.update(1)
+
+            chunked_sources.append(sources)
+        _sources = np.concatenate(chunked_sources, axis=-1)
+        # del self.model
+        progress_bar.close()
+        return _sources
+
+    def prediction(self, m, vocal_root, others_root, format):
+        os.makedirs(vocal_root, exist_ok=True)
+        os.makedirs(others_root, exist_ok=True)
+        basename = os.path.basename(m)
+        mix, rate = librosa.load(m, mono=False, sr=44100)
+        if mix.ndim == 1:
+            mix = np.asfortranarray([mix, mix])
+        mix = mix.T
+        sources = self.demix(mix.T)
+        opt = sources[0].T
+        if format in ["wav", "flac"]:
+            sf.write(
+                "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
+            )
+            sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
+        else:
+            path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
+            path_other = "%s/%s_others.wav" % (others_root, basename)
+            sf.write(path_vocal, mix - opt, rate)
+            sf.write(path_other, opt, rate)
+            if os.path.exists(path_vocal):
+                os.system(
+                    "ffmpeg -i %s -vn %s -q:a 2 -y"
+                    % (path_vocal, path_vocal[:-4] + ".%s" % format)
+                )
+            if os.path.exists(path_other):
+                os.system(
+                    "ffmpeg -i %s -vn %s -q:a 2 -y"
+                    % (path_other, path_other[:-4] + ".%s" % format)
+                )
+
+
+class MDXNetDereverb:
+    def __init__(self, chunks):
+        self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy"
+        self.shifts = 10  #'Predict with randomised equivariant stabilisation'
+        self.mixing = "min_mag"  # ['default','min_mag','max_mag']
+        self.chunks = chunks
+        self.margin = 44100
+        self.dim_t = 9
+        self.dim_f = 3072
+        self.n_fft = 6144
+        self.denoise = True
+        self.pred = Predictor(self)
+
+    def _path_audio_(self, input, vocal_root, others_root, format):
+        self.pred.prediction(input, vocal_root, others_root, format)
+
+
+if __name__ == "__main__":
+    dereverb = MDXNetDereverb(15)
+    from time import time as ttime
+
+    t0 = ttime()
+    dereverb._path_audio_(
+        "雪雪伴奏对消HP5.wav",
+        "vocal",
+        "others",
+    )
+    t1 = ttime()
+    print(t1 - t0)
+
+
+"""
+
+runtime\python.exe MDXNet.py 
+
+6G:
+15/9:0.8G->6.8G
+14:0.8G->6.5G
+25:炸
+
+half15:0.7G->6.6G,22.69s
+fp32-15:0.7G->6.6G,20.85s
+
+"""

Makefile

@@ -0,0 +1,63 @@
+.PHONY:
+.ONESHELL:
+
+help: ## Show this help and exit
+	@grep -hE '^[A-Za-z0-9_ \-]*?:.*##.*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
+
+install: ## Install dependencies (Do everytime you start up a paperspace machine)
+	apt-get -y install build-essential python3-dev ffmpeg
+	pip install --upgrade setuptools wheel
+	pip install --upgrade pip
+	pip install faiss-gpu fairseq gradio ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.1
+	pip install -r requirements.txt
+	pip install --upgrade lxml
+	apt-get update
+	apt -y install -qq aria2
+
+basev1: ## Download version 1 pre-trained models (Do only once after cloning the fork)
+	mkdir -p pretrained uvr5_weights
+	git pull
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d pretrained -o D32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d pretrained -o D40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d pretrained -o D48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d pretrained -o G32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d pretrained -o G40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d pretrained -o G48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -d pretrained -o f0D32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -d pretrained -o f0D40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -d pretrained -o f0D48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -d pretrained -o f0G32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -d pretrained -o f0G40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth -d pretrained -o f0G48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
+
+basev2: ## Download version 2 pre-trained models (Do only once after cloning the fork)
+	mkdir -p pretrained_v2 uvr5_weights
+	git pull
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D32k.pth -d pretrained_v2 -o D32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d pretrained_v2 -o D40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D48k.pth -d pretrained_v2 -o D48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G32k.pth -d pretrained_v2 -o G32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d pretrained_v2 -o G40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G48k.pth -d pretrained_v2 -o G48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D32k.pth -d pretrained_v2 -o f0D32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d pretrained_v2 -o f0D40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth -d pretrained_v2 -o f0D48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G32k.pth -d pretrained_v2 -o f0G32k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d pretrained_v2 -o f0G40k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth -d pretrained_v2 -o f0G48k.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
+	aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
+
+run-ui: ## Run the python GUI
+	python infer-web.py --paperspace --pycmd python
+
+run-cli: ## Run the python CLI
+	python infer-web.py --pycmd python --is_cli
+
+tensorboard: ## Start the tensorboard (Run on separate terminal)
+	echo https://tensorboard-$$(hostname).clg07azjl.paperspacegradient.com
+	tensorboard --logdir logs --bind_all

README.md

@@ -0,0 +1,11 @@
+---
+title: Ilaria RVC
+emoji: 🔥
+colorFrom: pink
+colorTo: pink
+sdk: gradio
+sdk_version: 3.43.2
+app_file: app.py
+pinned: false
+duplicated_from: TheStinger/Ilaria_RVC
+---

assets/hubert/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

assets/pretrained/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

assets/pretrained_v2/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

assets/rmvpe/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

assets/uvr5_weights/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

assets/weights/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

audioEffects.py

@@ -0,0 +1,37 @@
+from pedalboard import Pedalboard, Compressor, Reverb, NoiseGate
+from pedalboard.io import AudioFile
+import sys
+import os
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from i18n import I18nAuto
+i18n = I18nAuto()
+from pydub import AudioSegment
+import numpy as np
+import soundfile as sf
+from pydub.playback import play
+
+def process_audio(input_path, output_path, reverb_enabled, compressor_enabled, noise_gate_enabled, ):
+    print(reverb_enabled)
+    print(compressor_enabled)
+    print(noise_gate_enabled)
+    effects = []
+    if reverb_enabled:
+        effects.append(Reverb(room_size=0.01))
+    if compressor_enabled:
+        effects.append(Compressor(threshold_db=-10, ratio=25))
+    if noise_gate_enabled:
+        effects.append(NoiseGate(threshold_db=-16, ratio=1.5, release_ms=250))
+
+    board = Pedalboard(effects)
+
+    with AudioFile(input_path) as f:
+        with AudioFile(output_path, 'w', f.samplerate, f.num_channels) as o:
+            while f.tell() < f.frames:
+                chunk = f.read(f.samplerate)
+                effected = board(chunk, f.samplerate, reset=False)
+                o.write(effected)
+
+    result = i18n("Processed audio saved at: ") +  output_path
+    print(result)
+    return output_path

colab_for_mdx.py

@@ -0,0 +1,71 @@
+import json
+import os
+import gc
+import psutil
+import requests
+import subprocess
+import time
+import logging
+import sys
+import shutil
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+first_cell_executed = False
+file_folder = "Colab-for-MDX_B"
+def first_cell_ran():
+    global first_cell_executed
+    if first_cell_executed:
+        #print("The 'first_cell_ran' function has already been executed.")
+        return
+
+    
+
+    first_cell_executed = True
+    os.makedirs("tmp_models", exist_ok=True)
+
+
+
+    class hide_opt:  # hide outputs
+        def __enter__(self):
+            self._original_stdout = sys.stdout
+            sys.stdout = open(os.devnull, "w")
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            sys.stdout.close()
+            sys.stdout = self._original_stdout
+
+    def get_size(bytes, suffix="B"):  # read ram
+        global svmem
+        factor = 1024
+        for unit in ["", "K", "M", "G", "T", "P"]:
+            if bytes < factor:
+                return f"{bytes:.2f}{unit}{suffix}"
+            bytes /= factor
+        svmem = psutil.virtual_memory()
+
+
+    def use_uvr_without_saving():
+        print("Notice: files won't be saved to personal drive.")
+        print(f"Downloading {file_folder}...", end=" ")
+        with hide_opt():
+            #os.chdir(mounting_path)
+            items_to_move = ["demucs", "diffq","julius","model","separated","tracks","mdx.py","MDX-Net_Colab.ipynb"]
+            subprocess.run(["git", "clone", "https://github.com/NaJeongMo/Colab-for-MDX_B.git"])
+            for item_name in items_to_move:
+                item_path = os.path.join(file_folder, item_name)
+                if os.path.exists(item_path):
+                    if os.path.isfile(item_path):
+                        shutil.move(item_path, now_dir)
+                    elif os.path.isdir(item_path):
+                        shutil.move(item_path, now_dir)
+            try:
+                shutil.rmtree(file_folder)
+            except PermissionError:
+                print(f"No se pudo eliminar la carpeta {file_folder}. Puede estar relacionada con Git.")
+
+    
+    use_uvr_without_saving()
+    print("done!")
+    if not os.path.exists("tracks"):
+        os.mkdir("tracks")
+first_cell_ran()

configs/32k.json

@@ -0,0 +1,50 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3, 7, 11],
+    "resblock_dilation_sizes": [
+      [1, 3, 5],
+      [1, 3, 5],
+      [1, 3, 5]
+    ],
+    "upsample_rates": [10, 4, 2, 2, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16, 16, 4, 4, 4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/32k_v2.json

@@ -0,0 +1,50 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3, 7, 11],
+    "resblock_dilation_sizes": [
+      [1, 3, 5],
+      [1, 3, 5],
+      [1, 3, 5]
+    ],
+    "upsample_rates": [10, 8, 2, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [20, 16, 4, 4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/40k.json

@@ -0,0 +1,50 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 40000,
+    "filter_length": 2048,
+    "hop_length": 400,
+    "win_length": 2048,
+    "n_mel_channels": 125,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3, 7, 11],
+    "resblock_dilation_sizes": [
+      [1, 3, 5],
+      [1, 3, 5],
+      [1, 3, 5]
+    ],
+    "upsample_rates": [10, 10, 2, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16, 16, 4, 4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/48k.json

@@ -0,0 +1,50 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 11520,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3, 7, 11],
+    "resblock_dilation_sizes": [
+      [1, 3, 5],
+      [1, 3, 5],
+      [1, 3, 5]
+    ],
+    "upsample_rates": [10, 6, 2, 2, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16, 16, 4, 4, 4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/48k_v2.json

@@ -0,0 +1,50 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 17280,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3, 7, 11],
+    "resblock_dilation_sizes": [
+      [1, 3, 5],
+      [1, 3, 5],
+      [1, 3, 5]
+    ],
+    "upsample_rates": [12, 10, 2, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [24, 20, 4, 4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/config.json

@@ -0,0 +1,15 @@
+{
+  "pth_path": "assets/weights/kikiV1.pth",
+  "index_path": "logs/kikiV1.index",
+  "sg_input_device": "VoiceMeeter Output (VB-Audio Vo (MME)",
+  "sg_output_device": "VoiceMeeter Aux Input (VB-Audio (MME)",
+  "threhold": -45.0,
+  "pitch": 12.0,
+  "index_rate": 0.0,
+  "rms_mix_rate": 0.0,
+  "block_time": 0.25,
+  "crossfade_length": 0.04,
+  "extra_time": 2.0,
+  "n_cpu": 6.0,
+  "f0method": "rmvpe"
+}

configs/config.py

@@ -0,0 +1,265 @@
+import argparse
+import os
+import sys
+import json
+from multiprocessing import cpu_count
+
+import torch
+
+try:
+    import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+    if torch.xpu.is_available():
+        from infer.modules.ipex import ipex_init
+        ipex_init()
+except Exception:
+    pass
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+version_config_list = [
+    "v1/32k.json",
+    "v1/40k.json",
+    "v1/48k.json",
+    "v2/48k.json",
+    "v2/32k.json",
+]
+
+
+def singleton_variable(func):
+    def wrapper(*args, **kwargs):
+        if not wrapper.instance:
+            wrapper.instance = func(*args, **kwargs)
+        return wrapper.instance
+
+    wrapper.instance = None
+    return wrapper
+
+
+@singleton_variable
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.json_config = self.load_config_json()
+        self.gpu_mem = None
+        (
+            self.python_cmd,
+            self.listen_port,
+            self.iscolab,
+            self.noparallel,
+            self.noautoopen,
+            self.paperspace,
+            self.is_cli,
+            self.grtheme,
+            self.dml,
+        ) = self.arg_parse()
+        self.instead = ""
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    @staticmethod
+    def load_config_json() -> dict:
+        d = {}
+        for config_file in version_config_list:
+            with open(f"configs/{config_file}", "r") as f:
+                d[config_file] = json.load(f)
+        return d
+
+    @staticmethod
+    def arg_parse() -> tuple:
+        exe = sys.executable or "python"
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--port", type=int, default=7865, help="Listen port")
+        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
+        parser.add_argument("--colab", action="store_true", help="Launch in colab")
+        parser.add_argument(
+            "--noparallel", action="store_true", help="Disable parallel processing"
+        )
+        parser.add_argument(
+            "--noautoopen",
+            action="store_true",
+            help="Do not open in browser automatically",
+        )
+        parser.add_argument(  
+            "--paperspace",
+            action="store_true",
+            help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
+        )
+        parser.add_argument(  
+            "--is_cli",
+            action="store_true",
+            help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
+        )
+
+        parser.add_argument(
+                    "-t",
+                    "--theme",
+            help    = "Theme for Gradio. Format - `JohnSmith9982/small_and_pretty` (no backticks)",
+            default = "JohnSmith9982/small_and_pretty",
+            type    = str
+        )
+
+        parser.add_argument(
+            "--dml",
+            action="store_true",
+            help="Use DirectML backend instead of CUDA."
+        )
+        
+        cmd_opts = parser.parse_args()
+
+        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
+
+        return (
+            cmd_opts.pycmd,
+            cmd_opts.port,
+            cmd_opts.colab,
+            cmd_opts.noparallel,
+            cmd_opts.noautoopen,
+            cmd_opts.paperspace,
+            cmd_opts.is_cli,
+            cmd_opts.theme,
+            cmd_opts.dml,
+        )
+
+    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+    # check `getattr` and try it for compatibility
+    @staticmethod
+    def has_mps() -> bool:
+        if not torch.backends.mps.is_available():
+            return False
+        try:
+            torch.zeros(1).to(torch.device("mps"))
+            return True
+        except Exception:
+            return False
+        
+    @staticmethod
+    def has_xpu() -> bool:
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            return True
+        else:
+            return False
+
+    def use_fp32_config(self):
+        for config_file in version_config_list:
+            self.json_config[config_file]["train"]["fp16_run"] = False
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            if self.has_xpu():
+                self.device = self.instead = "xpu:0"
+                self.is_half = True
+            i_device = int(self.device.split(":")[-1])
+            self.gpu_name = torch.cuda.get_device_name(i_device)
+            if (
+                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
+                or "P40" in self.gpu_name.upper()
+                or "P10" in self.gpu_name.upper()
+                or "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                logger.info("Found GPU %s, force to fp32", self.gpu_name)
+                self.is_half = False
+                self.use_fp32_config()
+            else:
+                logger.info("Found GPU %s", self.gpu_name)
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+            if self.gpu_mem <= 4:
+                with open("infer/modules/train/preprocess.py", "r") as f:
+                    strr = f.read().replace("3.7", "3.0")
+                with open("infer/modules/train/preprocess.py", "w") as f:
+                    f.write(strr)
+        elif self.has_mps():
+            logger.info("No supported Nvidia GPU found")
+            self.device = self.instead = "mps"
+            self.is_half = False
+            self.use_fp32_config()
+        else:
+            logger.info("No supported Nvidia GPU found")
+            self.device = self.instead = "cpu"
+            self.is_half = False
+            self.use_fp32_config()
+
+        if self.n_cpu == 0:
+            self.n_cpu = cpu_count()
+
+        if self.is_half:
+            # 6G显存配置
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            # 5G显存配置
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+
+        if self.gpu_mem is not None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+        if self.dml:
+            logger.info("Use DirectML instead")
+            if (
+                os.path.exists(
+                    "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
+                )
+                == False
+            ):
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime",
+                        "runtime\Lib\site-packages\onnxruntime-cuda",
+                    )
+                except:
+                    pass
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime-dml",
+                        "runtime\Lib\site-packages\onnxruntime",
+                    )
+                except:
+                    pass
+            # if self.device != "cpu":
+            import torch_directml
+
+            self.device = torch_directml.device(torch_directml.default_device())
+            self.is_half = False
+        else:
+            if self.instead:
+                logger.info(f"Use {self.instead} instead")
+            if (
+                os.path.exists(
+                    "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
+                )
+                == False
+            ):
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime",
+                        "runtime\Lib\site-packages\onnxruntime-dml",
+                    )
+                except:
+                    pass
+                try:
+                    os.rename(
+                        "runtime\Lib\site-packages\onnxruntime-cuda",
+                        "runtime\Lib\site-packages\onnxruntime",
+                    )
+                except:
+                    pass
+        return x_pad, x_query, x_center, x_max

configs/v1/32k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,4,2,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v1/40k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 40000,
+    "filter_length": 2048,
+    "hop_length": 400,
+    "win_length": 2048,
+    "n_mel_channels": 125,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,10,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v1/48k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 11520,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,6,2,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v2/32k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 12800,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 32000,
+    "filter_length": 1024,
+    "hop_length": 320,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [10,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [20,16,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

configs/v2/48k.json

@@ -0,0 +1,46 @@
+{
+  "train": {
+    "log_interval": 200,
+    "seed": 1234,
+    "epochs": 20000,
+    "learning_rate": 1e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 4,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 17280,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "max_wav_value": 32768.0,
+    "sampling_rate": 48000,
+    "filter_length": 2048,
+    "hop_length": 480,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [12,10,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [24,20,4,4],
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "spk_embed_dim": 109
+  }
+}

demucs/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+__version__ = "2.0.3"

demucs/__main__.py

@@ -0,0 +1,317 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import math
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+
+import torch as th
+from torch import distributed, nn
+from torch.nn.parallel.distributed import DistributedDataParallel
+
+from .augment import FlipChannels, FlipSign, Remix, Scale, Shift
+from .compressed import get_compressed_datasets
+from .model import Demucs
+from .parser import get_name, get_parser
+from .raw import Rawset
+from .repitch import RepitchedWrapper
+from .pretrained import load_pretrained, SOURCES
+from .tasnet import ConvTasNet
+from .test import evaluate
+from .train import train_model, validate_model
+from .utils import (human_seconds, load_model, save_model, get_state,
+                    save_state, sizeof_fmt, get_quantizer)
+from .wav import get_wav_datasets, get_musdb_wav_datasets
+
+
+@dataclass
+class SavedState:
+    metrics: list = field(default_factory=list)
+    last_state: dict = None
+    best_state: dict = None
+    optimizer: dict = None
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    name = get_name(parser, args)
+    print(f"Experiment {name}")
+
+    if args.musdb is None and args.rank == 0:
+        print(
+            "You must provide the path to the MusDB dataset with the --musdb flag. "
+            "To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.",
+            file=sys.stderr)
+        sys.exit(1)
+
+    eval_folder = args.evals / name
+    eval_folder.mkdir(exist_ok=True, parents=True)
+    args.logs.mkdir(exist_ok=True)
+    metrics_path = args.logs / f"{name}.json"
+    eval_folder.mkdir(exist_ok=True, parents=True)
+    args.checkpoints.mkdir(exist_ok=True, parents=True)
+    args.models.mkdir(exist_ok=True, parents=True)
+
+    if args.device is None:
+        device = "cpu"
+        if th.cuda.is_available():
+            device = "cuda"
+    else:
+        device = args.device
+
+    th.manual_seed(args.seed)
+    # Prevents too many threads to be started when running `museval` as it can be quite
+    # inefficient on NUMA architectures.
+    os.environ["OMP_NUM_THREADS"] = "1"
+    os.environ["MKL_NUM_THREADS"] = "1"
+
+    if args.world_size > 1:
+        if device != "cuda" and args.rank == 0:
+            print("Error: distributed training is only available with cuda device", file=sys.stderr)
+            sys.exit(1)
+        th.cuda.set_device(args.rank % th.cuda.device_count())
+        distributed.init_process_group(backend="nccl",
+                                       init_method="tcp://" + args.master,
+                                       rank=args.rank,
+                                       world_size=args.world_size)
+
+    checkpoint = args.checkpoints / f"{name}.th"
+    checkpoint_tmp = args.checkpoints / f"{name}.th.tmp"
+    if args.restart and checkpoint.exists() and args.rank == 0:
+        checkpoint.unlink()
+
+    if args.test or args.test_pretrained:
+        args.epochs = 1
+        args.repeat = 0
+        if args.test:
+            model = load_model(args.models / args.test)
+        else:
+            model = load_pretrained(args.test_pretrained)
+    elif args.tasnet:
+        model = ConvTasNet(audio_channels=args.audio_channels,
+                           samplerate=args.samplerate, X=args.X,
+                           segment_length=4 * args.samples,
+                           sources=SOURCES)
+    else:
+        model = Demucs(
+            audio_channels=args.audio_channels,
+            channels=args.channels,
+            context=args.context,
+            depth=args.depth,
+            glu=args.glu,
+            growth=args.growth,
+            kernel_size=args.kernel_size,
+            lstm_layers=args.lstm_layers,
+            rescale=args.rescale,
+            rewrite=args.rewrite,
+            stride=args.conv_stride,
+            resample=args.resample,
+            normalize=args.normalize,
+            samplerate=args.samplerate,
+            segment_length=4 * args.samples,
+            sources=SOURCES,
+        )
+    model.to(device)
+    if args.init:
+        model.load_state_dict(load_pretrained(args.init).state_dict())
+
+    if args.show:
+        print(model)
+        size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))
+        print(f"Model size {size}")
+        return
+
+    try:
+        saved = th.load(checkpoint, map_location='cpu')
+    except IOError:
+        saved = SavedState()
+
+    optimizer = th.optim.Adam(model.parameters(), lr=args.lr)
+
+    quantizer = None
+    quantizer = get_quantizer(model, args, optimizer)
+
+    if saved.last_state is not None:
+        model.load_state_dict(saved.last_state, strict=False)
+    if saved.optimizer is not None:
+        optimizer.load_state_dict(saved.optimizer)
+
+    model_name = f"{name}.th"
+    if args.save_model:
+        if args.rank == 0:
+            model.to("cpu")
+            model.load_state_dict(saved.best_state)
+            save_model(model, quantizer, args, args.models / model_name)
+        return
+    elif args.save_state:
+        model_name = f"{args.save_state}.th"
+        if args.rank == 0:
+            model.to("cpu")
+            model.load_state_dict(saved.best_state)
+            state = get_state(model, quantizer)
+            save_state(state, args.models / model_name)
+        return
+
+    if args.rank == 0:
+        done = args.logs / f"{name}.done"
+        if done.exists():
+            done.unlink()
+
+    augment = [Shift(args.data_stride)]
+    if args.augment:
+        augment += [FlipSign(), FlipChannels(), Scale(),
+                    Remix(group_size=args.remix_group_size)]
+    augment = nn.Sequential(*augment).to(device)
+    print("Agumentation pipeline:", augment)
+
+    if args.mse:
+        criterion = nn.MSELoss()
+    else:
+        criterion = nn.L1Loss()
+
+    # Setting number of samples so that all convolution windows are full.
+    # Prevents hard to debug mistake with the prediction being shifted compared
+    # to the input mixture.
+    samples = model.valid_length(args.samples)
+    print(f"Number of training samples adjusted to {samples}")
+    samples = samples + args.data_stride
+    if args.repitch:
+        # We need a bit more audio samples, to account for potential
+        # tempo change.
+        samples = math.ceil(samples / (1 - 0.01 * args.max_tempo))
+
+    args.metadata.mkdir(exist_ok=True, parents=True)
+    if args.raw:
+        train_set = Rawset(args.raw / "train",
+                           samples=samples,
+                           channels=args.audio_channels,
+                           streams=range(1, len(model.sources) + 1),
+                           stride=args.data_stride)
+
+        valid_set = Rawset(args.raw / "valid", channels=args.audio_channels)
+    elif args.wav:
+        train_set, valid_set = get_wav_datasets(args, samples, model.sources)
+    elif args.is_wav:
+        train_set, valid_set = get_musdb_wav_datasets(args, samples, model.sources)
+    else:
+        train_set, valid_set = get_compressed_datasets(args, samples)
+
+    if args.repitch:
+        train_set = RepitchedWrapper(
+            train_set,
+            proba=args.repitch,
+            max_tempo=args.max_tempo)
+
+    best_loss = float("inf")
+    for epoch, metrics in enumerate(saved.metrics):
+        print(f"Epoch {epoch:03d}: "
+              f"train={metrics['train']:.8f} "
+              f"valid={metrics['valid']:.8f} "
+              f"best={metrics['best']:.4f} "
+              f"ms={metrics.get('true_model_size', 0):.2f}MB "
+              f"cms={metrics.get('compressed_model_size', 0):.2f}MB "
+              f"duration={human_seconds(metrics['duration'])}")
+        best_loss = metrics['best']
+
+    if args.world_size > 1:
+        dmodel = DistributedDataParallel(model,
+                                         device_ids=[th.cuda.current_device()],
+                                         output_device=th.cuda.current_device())
+    else:
+        dmodel = model
+
+    for epoch in range(len(saved.metrics), args.epochs):
+        begin = time.time()
+        model.train()
+        train_loss, model_size = train_model(
+            epoch, train_set, dmodel, criterion, optimizer, augment,
+            quantizer=quantizer,
+            batch_size=args.batch_size,
+            device=device,
+            repeat=args.repeat,
+            seed=args.seed,
+            diffq=args.diffq,
+            workers=args.workers,
+            world_size=args.world_size)
+        model.eval()
+        valid_loss = validate_model(
+            epoch, valid_set, model, criterion,
+            device=device,
+            rank=args.rank,
+            split=args.split_valid,
+            overlap=args.overlap,
+            world_size=args.world_size)
+
+        ms = 0
+        cms = 0
+        if quantizer and args.rank == 0:
+            ms = quantizer.true_model_size()
+            cms = quantizer.compressed_model_size(num_workers=min(40, args.world_size * 10))
+
+        duration = time.time() - begin
+        if valid_loss < best_loss and ms <= args.ms_target:
+            best_loss = valid_loss
+            saved.best_state = {
+                key: value.to("cpu").clone()
+                for key, value in model.state_dict().items()
+            }
+
+        saved.metrics.append({
+            "train": train_loss,
+            "valid": valid_loss,
+            "best": best_loss,
+            "duration": duration,
+            "model_size": model_size,
+            "true_model_size": ms,
+            "compressed_model_size": cms,
+        })
+        if args.rank == 0:
+            json.dump(saved.metrics, open(metrics_path, "w"))
+
+        saved.last_state = model.state_dict()
+        saved.optimizer = optimizer.state_dict()
+        if args.rank == 0 and not args.test:
+            th.save(saved, checkpoint_tmp)
+            checkpoint_tmp.rename(checkpoint)
+
+        print(f"Epoch {epoch:03d}: "
+              f"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} ms={ms:.2f}MB "
+              f"cms={cms:.2f}MB "
+              f"duration={human_seconds(duration)}")
+
+    if args.world_size > 1:
+        distributed.barrier()
+
+    del dmodel
+    model.load_state_dict(saved.best_state)
+    if args.eval_cpu:
+        device = "cpu"
+        model.to(device)
+    model.eval()
+    evaluate(model, args.musdb, eval_folder,
+             is_wav=args.is_wav,
+             rank=args.rank,
+             world_size=args.world_size,
+             device=device,
+             save=args.save,
+             split=args.split_valid,
+             shifts=args.shifts,
+             overlap=args.overlap,
+             workers=args.eval_workers)
+    model.to("cpu")
+    if args.rank == 0:
+        if not (args.test or args.test_pretrained):
+            save_model(model, quantizer, args, args.models / model_name)
+        print("done")
+        done.write_text("done")
+
+
+if __name__ == "__main__":
+    main()

demucs/audio.py

@@ -0,0 +1,172 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import json
+import subprocess as sp
+from pathlib import Path
+
+import julius
+import numpy as np
+import torch
+
+from .utils import temp_filenames
+
+
+def _read_info(path):
+    stdout_data = sp.check_output([
+        'ffprobe', "-loglevel", "panic",
+        str(path), '-print_format', 'json', '-show_format', '-show_streams'
+    ])
+    return json.loads(stdout_data.decode('utf-8'))
+
+
+class AudioFile:
+    """
+    Allows to read audio from any format supported by ffmpeg, as well as resampling or
+    converting to mono on the fly. See :method:`read` for more details.
+    """
+    def __init__(self, path: Path):
+        self.path = Path(path)
+        self._info = None
+
+    def __repr__(self):
+        features = [("path", self.path)]
+        features.append(("samplerate", self.samplerate()))
+        features.append(("channels", self.channels()))
+        features.append(("streams", len(self)))
+        features_str = ", ".join(f"{name}={value}" for name, value in features)
+        return f"AudioFile({features_str})"
+
+    @property
+    def info(self):
+        if self._info is None:
+            self._info = _read_info(self.path)
+        return self._info
+
+    @property
+    def duration(self):
+        return float(self.info['format']['duration'])
+
+    @property
+    def _audio_streams(self):
+        return [
+            index for index, stream in enumerate(self.info["streams"])
+            if stream["codec_type"] == "audio"
+        ]
+
+    def __len__(self):
+        return len(self._audio_streams)
+
+    def channels(self, stream=0):
+        return int(self.info['streams'][self._audio_streams[stream]]['channels'])
+
+    def samplerate(self, stream=0):
+        return int(self.info['streams'][self._audio_streams[stream]]['sample_rate'])
+
+    def read(self,
+             seek_time=None,
+             duration=None,
+             streams=slice(None),
+             samplerate=None,
+             channels=None,
+             temp_folder=None):
+        """
+        Slightly more efficient implementation than stempeg,
+        in particular, this will extract all stems at once
+        rather than having to loop over one file multiple times
+        for each stream.
+
+        Args:
+            seek_time (float):  seek time in seconds or None if no seeking is needed.
+            duration (float): duration in seconds to extract or None to extract until the end.
+            streams (slice, int or list): streams to extract, can be a single int, a list or
+                a slice. If it is a slice or list, the output will be of size [S, C, T]
+                with S the number of streams, C the number of channels and T the number of samples.
+                If it is an int, the output will be [C, T].
+            samplerate (int): if provided, will resample on the fly. If None, no resampling will
+                be done. Original sampling rate can be obtained with :method:`samplerate`.
+            channels (int): if 1, will convert to mono. We do not rely on ffmpeg for that
+                as ffmpeg automatically scale by +3dB to conserve volume when playing on speakers.
+                See https://sound.stackexchange.com/a/42710.
+                Our definition of mono is simply the average of the two channels. Any other
+                value will be ignored.
+            temp_folder (str or Path or None): temporary folder to use for decoding.
+
+
+        """
+        streams = np.array(range(len(self)))[streams]
+        single = not isinstance(streams, np.ndarray)
+        if single:
+            streams = [streams]
+
+        if duration is None:
+            target_size = None
+            query_duration = None
+        else:
+            target_size = int((samplerate or self.samplerate()) * duration)
+            query_duration = float((target_size + 1) / (samplerate or self.samplerate()))
+
+        with temp_filenames(len(streams)) as filenames:
+            command = ['ffmpeg', '-y']
+            command += ['-loglevel', 'panic']
+            if seek_time:
+                command += ['-ss', str(seek_time)]
+            command += ['-i', str(self.path)]
+            for stream, filename in zip(streams, filenames):
+                command += ['-map', f'0:{self._audio_streams[stream]}']
+                if query_duration is not None:
+                    command += ['-t', str(query_duration)]
+                command += ['-threads', '1']
+                command += ['-f', 'f32le']
+                if samplerate is not None:
+                    command += ['-ar', str(samplerate)]
+                command += [filename]
+
+            sp.run(command, check=True)
+            wavs = []
+            for filename in filenames:
+                wav = np.fromfile(filename, dtype=np.float32)
+                wav = torch.from_numpy(wav)
+                wav = wav.view(-1, self.channels()).t()
+                if channels is not None:
+                    wav = convert_audio_channels(wav, channels)
+                if target_size is not None:
+                    wav = wav[..., :target_size]
+                wavs.append(wav)
+        wav = torch.stack(wavs, dim=0)
+        if single:
+            wav = wav[0]
+        return wav
+
+
+def convert_audio_channels(wav, channels=2):
+    """Convert audio to the given number of channels."""
+    *shape, src_channels, length = wav.shape
+    if src_channels == channels:
+        pass
+    elif channels == 1:
+        # Case 1:
+        # The caller asked 1-channel audio, but the stream have multiple
+        # channels, downmix all channels.
+        wav = wav.mean(dim=-2, keepdim=True)
+    elif src_channels == 1:
+        # Case 2:
+        # The caller asked for multiple channels, but the input file have
+        # one single channel, replicate the audio over all channels.
+        wav = wav.expand(*shape, channels, length)
+    elif src_channels >= channels:
+        # Case 3:
+        # The caller asked for multiple channels, and the input file have
+        # more channels than requested. In that case return the first channels.
+        wav = wav[..., :channels, :]
+    else:
+        # Case 4: What is a reasonable choice here?
+        raise ValueError('The audio file has less channels than requested but is not mono.')
+    return wav
+
+
+def convert_audio(wav, from_samplerate, to_samplerate, channels):
+    wav = convert_audio_channels(wav, channels)
+    return julius.resample_frac(wav, from_samplerate, to_samplerate)

demucs/augment.py

@@ -0,0 +1,106 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import torch as th
+from torch import nn
+
+
+class Shift(nn.Module):
+    """
+    Randomly shift audio in time by up to `shift` samples.
+    """
+    def __init__(self, shift=8192):
+        super().__init__()
+        self.shift = shift
+
+    def forward(self, wav):
+        batch, sources, channels, time = wav.size()
+        length = time - self.shift
+        if self.shift > 0:
+            if not self.training:
+                wav = wav[..., :length]
+            else:
+                offsets = th.randint(self.shift, [batch, sources, 1, 1], device=wav.device)
+                offsets = offsets.expand(-1, -1, channels, -1)
+                indexes = th.arange(length, device=wav.device)
+                wav = wav.gather(3, indexes + offsets)
+        return wav
+
+
+class FlipChannels(nn.Module):
+    """
+    Flip left-right channels.
+    """
+    def forward(self, wav):
+        batch, sources, channels, time = wav.size()
+        if self.training and wav.size(2) == 2:
+            left = th.randint(2, (batch, sources, 1, 1), device=wav.device)
+            left = left.expand(-1, -1, -1, time)
+            right = 1 - left
+            wav = th.cat([wav.gather(2, left), wav.gather(2, right)], dim=2)
+        return wav
+
+
+class FlipSign(nn.Module):
+    """
+    Random sign flip.
+    """
+    def forward(self, wav):
+        batch, sources, channels, time = wav.size()
+        if self.training:
+            signs = th.randint(2, (batch, sources, 1, 1), device=wav.device, dtype=th.float32)
+            wav = wav * (2 * signs - 1)
+        return wav
+
+
+class Remix(nn.Module):
+    """
+    Shuffle sources to make new mixes.
+    """
+    def __init__(self, group_size=4):
+        """
+        Shuffle sources within one batch.
+        Each batch is divided into groups of size `group_size` and shuffling is done within
+        each group separatly. This allow to keep the same probability distribution no matter
+        the number of GPUs. Without this grouping, using more GPUs would lead to a higher
+        probability of keeping two sources from the same track together which can impact
+        performance.
+        """
+        super().__init__()
+        self.group_size = group_size
+
+    def forward(self, wav):
+        batch, streams, channels, time = wav.size()
+        device = wav.device
+
+        if self.training:
+            group_size = self.group_size or batch
+            if batch % group_size != 0:
+                raise ValueError(f"Batch size {batch} must be divisible by group size {group_size}")
+            groups = batch // group_size
+            wav = wav.view(groups, group_size, streams, channels, time)
+            permutations = th.argsort(th.rand(groups, group_size, streams, 1, 1, device=device),
+                                      dim=1)
+            wav = wav.gather(1, permutations.expand(-1, -1, -1, channels, time))
+            wav = wav.view(batch, streams, channels, time)
+        return wav
+
+
+class Scale(nn.Module):
+    def __init__(self, proba=1., min=0.25, max=1.25):
+        super().__init__()
+        self.proba = proba
+        self.min = min
+        self.max = max
+
+    def forward(self, wav):
+        batch, streams, channels, time = wav.size()
+        device = wav.device
+        if self.training and random.random() < self.proba:
+            scales = th.empty(batch, streams, 1, 1, device=device).uniform_(self.min, self.max)
+            wav *= scales
+        return wav

demucs/compressed.py

@@ -0,0 +1,115 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+from fractions import Fraction
+from concurrent import futures
+
+import musdb
+from torch import distributed
+
+from .audio import AudioFile
+
+
+def get_musdb_tracks(root, *args, **kwargs):
+    mus = musdb.DB(root, *args, **kwargs)
+    return {track.name: track.path for track in mus}
+
+
+class StemsSet:
+    def __init__(self, tracks, metadata, duration=None, stride=1,
+                 samplerate=44100, channels=2, streams=slice(None)):
+
+        self.metadata = []
+        for name, path in tracks.items():
+            meta = dict(metadata[name])
+            meta["path"] = path
+            meta["name"] = name
+            self.metadata.append(meta)
+            if duration is not None and meta["duration"] < duration:
+                raise ValueError(f"Track {name} duration is too small {meta['duration']}")
+        self.metadata.sort(key=lambda x: x["name"])
+        self.duration = duration
+        self.stride = stride
+        self.channels = channels
+        self.samplerate = samplerate
+        self.streams = streams
+
+    def __len__(self):
+        return sum(self._examples_count(m) for m in self.metadata)
+
+    def _examples_count(self, meta):
+        if self.duration is None:
+            return 1
+        else:
+            return int((meta["duration"] - self.duration) // self.stride + 1)
+
+    def track_metadata(self, index):
+        for meta in self.metadata:
+            examples = self._examples_count(meta)
+            if index >= examples:
+                index -= examples
+                continue
+            return meta
+
+    def __getitem__(self, index):
+        for meta in self.metadata:
+            examples = self._examples_count(meta)
+            if index >= examples:
+                index -= examples
+                continue
+            streams = AudioFile(meta["path"]).read(seek_time=index * self.stride,
+                                                   duration=self.duration,
+                                                   channels=self.channels,
+                                                   samplerate=self.samplerate,
+                                                   streams=self.streams)
+            return (streams - meta["mean"]) / meta["std"]
+
+
+def _get_track_metadata(path):
+    # use mono at 44kHz as reference. For any other settings data won't be perfectly
+    # normalized but it should be good enough.
+    audio = AudioFile(path)
+    mix = audio.read(streams=0, channels=1, samplerate=44100)
+    return {"duration": audio.duration, "std": mix.std().item(), "mean": mix.mean().item()}
+
+
+def _build_metadata(tracks, workers=10):
+    pendings = []
+    with futures.ProcessPoolExecutor(workers) as pool:
+        for name, path in tracks.items():
+            pendings.append((name, pool.submit(_get_track_metadata, path)))
+    return {name: p.result() for name, p in pendings}
+
+
+def _build_musdb_metadata(path, musdb, workers):
+    tracks = get_musdb_tracks(musdb)
+    metadata = _build_metadata(tracks, workers)
+    path.parent.mkdir(exist_ok=True, parents=True)
+    json.dump(metadata, open(path, "w"))
+
+
+def get_compressed_datasets(args, samples):
+    metadata_file = args.metadata / "musdb.json"
+    if not metadata_file.is_file() and args.rank == 0:
+        _build_musdb_metadata(metadata_file, args.musdb, args.workers)
+    if args.world_size > 1:
+        distributed.barrier()
+    metadata = json.load(open(metadata_file))
+    duration = Fraction(samples, args.samplerate)
+    stride = Fraction(args.data_stride, args.samplerate)
+    train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"),
+                         metadata,
+                         duration=duration,
+                         stride=stride,
+                         streams=slice(1, None),
+                         samplerate=args.samplerate,
+                         channels=args.audio_channels)
+    valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"),
+                         metadata,
+                         samplerate=args.samplerate,
+                         channels=args.audio_channels)
+    return train_set, valid_set

demucs/model.py

@@ -0,0 +1,202 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import julius
+from torch import nn
+
+from .utils import capture_init, center_trim
+
+
+class BLSTM(nn.Module):
+    def __init__(self, dim, layers=1):
+        super().__init__()
+        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
+        self.linear = nn.Linear(2 * dim, dim)
+
+    def forward(self, x):
+        x = x.permute(2, 0, 1)
+        x = self.lstm(x)[0]
+        x = self.linear(x)
+        x = x.permute(1, 2, 0)
+        return x
+
+
+def rescale_conv(conv, reference):
+    std = conv.weight.std().detach()
+    scale = (std / reference)**0.5
+    conv.weight.data /= scale
+    if conv.bias is not None:
+        conv.bias.data /= scale
+
+
+def rescale_module(module, reference):
+    for sub in module.modules():
+        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
+            rescale_conv(sub, reference)
+
+
+class Demucs(nn.Module):
+    @capture_init
+    def __init__(self,
+                 sources,
+                 audio_channels=2,
+                 channels=64,
+                 depth=6,
+                 rewrite=True,
+                 glu=True,
+                 rescale=0.1,
+                 resample=True,
+                 kernel_size=8,
+                 stride=4,
+                 growth=2.,
+                 lstm_layers=2,
+                 context=3,
+                 normalize=False,
+                 samplerate=44100,
+                 segment_length=4 * 10 * 44100):
+        """
+        Args:
+            sources (list[str]): list of source names
+            audio_channels (int): stereo or mono
+            channels (int): first convolution channels
+            depth (int): number of encoder/decoder layers
+            rewrite (bool): add 1x1 convolution to each encoder layer
+                and a convolution to each decoder layer.
+                For the decoder layer, `context` gives the kernel size.
+            glu (bool): use glu instead of ReLU
+            resample_input (bool): upsample x2 the input and downsample /2 the output.
+            rescale (int): rescale initial weights of convolutions
+                to get their standard deviation closer to `rescale`
+            kernel_size (int): kernel size for convolutions
+            stride (int): stride for convolutions
+            growth (float): multiply (resp divide) number of channels by that
+                for each layer of the encoder (resp decoder)
+            lstm_layers (int): number of lstm layers, 0 = no lstm
+            context (int): kernel size of the convolution in the
+                decoder before the transposed convolution. If > 1,
+                will provide some context from neighboring time
+                steps.
+            samplerate (int): stored as meta information for easing
+                future evaluations of the model.
+            segment_length (int): stored as meta information for easing
+                future evaluations of the model. Length of the segments on which
+                the model was trained.
+        """
+
+        super().__init__()
+        self.audio_channels = audio_channels
+        self.sources = sources
+        self.kernel_size = kernel_size
+        self.context = context
+        self.stride = stride
+        self.depth = depth
+        self.resample = resample
+        self.channels = channels
+        self.normalize = normalize
+        self.samplerate = samplerate
+        self.segment_length = segment_length
+
+        self.encoder = nn.ModuleList()
+        self.decoder = nn.ModuleList()
+
+        if glu:
+            activation = nn.GLU(dim=1)
+            ch_scale = 2
+        else:
+            activation = nn.ReLU()
+            ch_scale = 1
+        in_channels = audio_channels
+        for index in range(depth):
+            encode = []
+            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
+            if rewrite:
+                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
+            self.encoder.append(nn.Sequential(*encode))
+
+            decode = []
+            if index > 0:
+                out_channels = in_channels
+            else:
+                out_channels = len(self.sources) * audio_channels
+            if rewrite:
+                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
+            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
+            if index > 0:
+                decode.append(nn.ReLU())
+            self.decoder.insert(0, nn.Sequential(*decode))
+            in_channels = channels
+            channels = int(growth * channels)
+
+        channels = in_channels
+
+        if lstm_layers:
+            self.lstm = BLSTM(channels, lstm_layers)
+        else:
+            self.lstm = None
+
+        if rescale:
+            rescale_module(self, reference=rescale)
+
+    def valid_length(self, length):
+        """
+        Return the nearest valid length to use with the model so that
+        there is no time steps left over in a convolutions, e.g. for all
+        layers, size of the input - kernel_size % stride = 0.
+
+        If the mixture has a valid length, the estimated sources
+        will have exactly the same length when context = 1. If context > 1,
+        the two signals can be center trimmed to match.
+
+        For training, extracts should have a valid length.For evaluation
+        on full tracks we recommend passing `pad = True` to :method:`forward`.
+        """
+        if self.resample:
+            length *= 2
+        for _ in range(self.depth):
+            length = math.ceil((length - self.kernel_size) / self.stride) + 1
+            length = max(1, length)
+            length += self.context - 1
+        for _ in range(self.depth):
+            length = (length - 1) * self.stride + self.kernel_size
+
+        if self.resample:
+            length = math.ceil(length / 2)
+        return int(length)
+
+    def forward(self, mix):
+        x = mix
+
+        if self.normalize:
+            mono = mix.mean(dim=1, keepdim=True)
+            mean = mono.mean(dim=-1, keepdim=True)
+            std = mono.std(dim=-1, keepdim=True)
+        else:
+            mean = 0
+            std = 1
+
+        x = (x - mean) / (1e-5 + std)
+
+        if self.resample:
+            x = julius.resample_frac(x, 1, 2)
+
+        saved = []
+        for encode in self.encoder:
+            x = encode(x)
+            saved.append(x)
+        if self.lstm:
+            x = self.lstm(x)
+        for decode in self.decoder:
+            skip = center_trim(saved.pop(-1), x)
+            x = x + skip
+            x = decode(x)
+
+        if self.resample:
+            x = julius.resample_frac(x, 2, 1)
+        x = x * std + mean
+        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
+        return x

demucs/parser.py

@@ -0,0 +1,244 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import os
+from pathlib import Path
+
+
+def get_parser():
+    parser = argparse.ArgumentParser("demucs", description="Train and evaluate Demucs.")
+    default_raw = None
+    default_musdb = None
+    if 'DEMUCS_RAW' in os.environ:
+        default_raw = Path(os.environ['DEMUCS_RAW'])
+    if 'DEMUCS_MUSDB' in os.environ:
+        default_musdb = Path(os.environ['DEMUCS_MUSDB'])
+    parser.add_argument(
+        "--raw",
+        type=Path,
+        default=default_raw,
+        help="Path to raw audio, can be faster, see python3 -m demucs.raw to extract.")
+    parser.add_argument("--no_raw", action="store_const", const=None, dest="raw")
+    parser.add_argument("-m",
+                        "--musdb",
+                        type=Path,
+                        default=default_musdb,
+                        help="Path to musdb root")
+    parser.add_argument("--is_wav", action="store_true",
+                        help="Indicate that the MusDB dataset is in wav format (i.e. MusDB-HQ).")
+    parser.add_argument("--metadata", type=Path, default=Path("metadata/"),
+                        help="Folder where metadata information is stored.")
+    parser.add_argument("--wav", type=Path,
+                        help="Path to a wav dataset. This should contain a 'train' and a 'valid' "
+                             "subfolder.")
+    parser.add_argument("--samplerate", type=int, default=44100)
+    parser.add_argument("--audio_channels", type=int, default=2)
+    parser.add_argument("--samples",
+                        default=44100 * 10,
+                        type=int,
+                        help="number of samples to feed in")
+    parser.add_argument("--data_stride",
+                        default=44100,
+                        type=int,
+                        help="Stride for chunks, shorter = longer epochs")
+    parser.add_argument("-w", "--workers", default=10, type=int, help="Loader workers")
+    parser.add_argument("--eval_workers", default=2, type=int, help="Final evaluation workers")
+    parser.add_argument("-d",
+                        "--device",
+                        help="Device to train on, default is cuda if available else cpu")
+    parser.add_argument("--eval_cpu", action="store_true", help="Eval on test will be run on cpu.")
+    parser.add_argument("--dummy", help="Dummy parameter, useful to create a new checkpoint file")
+    parser.add_argument("--test", help="Just run the test pipeline + one validation. "
+                                       "This should be a filename relative to the models/ folder.")
+    parser.add_argument("--test_pretrained", help="Just run the test pipeline + one validation, "
+                                                  "on a pretrained model. ")
+
+    parser.add_argument("--rank", default=0, type=int)
+    parser.add_argument("--world_size", default=1, type=int)
+    parser.add_argument("--master")
+
+    parser.add_argument("--checkpoints",
+                        type=Path,
+                        default=Path("checkpoints"),
+                        help="Folder where to store checkpoints etc")
+    parser.add_argument("--evals",
+                        type=Path,
+                        default=Path("evals"),
+                        help="Folder where to store evals and waveforms")
+    parser.add_argument("--save",
+                        action="store_true",
+                        help="Save estimated for the test set waveforms")
+    parser.add_argument("--logs",
+                        type=Path,
+                        default=Path("logs"),
+                        help="Folder where to store logs")
+    parser.add_argument("--models",
+                        type=Path,
+                        default=Path("models"),
+                        help="Folder where to store trained models")
+    parser.add_argument("-R",
+                        "--restart",
+                        action='store_true',
+                        help='Restart training, ignoring previous run')
+
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("-e", "--epochs", type=int, default=180, help="Number of epochs")
+    parser.add_argument("-r",
+                        "--repeat",
+                        type=int,
+                        default=2,
+                        help="Repeat the train set, longer epochs")
+    parser.add_argument("-b", "--batch_size", type=int, default=64)
+    parser.add_argument("--lr", type=float, default=3e-4)
+    parser.add_argument("--mse", action="store_true", help="Use MSE instead of L1")
+    parser.add_argument("--init", help="Initialize from a pre-trained model.")
+
+    # Augmentation options
+    parser.add_argument("--no_augment",
+                        action="store_false",
+                        dest="augment",
+                        default=True,
+                        help="No basic data augmentation.")
+    parser.add_argument("--repitch", type=float, default=0.2,
+                        help="Probability to do tempo/pitch change")
+    parser.add_argument("--max_tempo", type=float, default=12,
+                        help="Maximum relative tempo change in %% when using repitch.")
+
+    parser.add_argument("--remix_group_size",
+                        type=int,
+                        default=4,
+                        help="Shuffle sources using group of this size. Useful to somewhat "
+                        "replicate multi-gpu training "
+                        "on less GPUs.")
+    parser.add_argument("--shifts",
+                        type=int,
+                        default=10,
+                        help="Number of random shifts used for the shift trick.")
+    parser.add_argument("--overlap",
+                        type=float,
+                        default=0.25,
+                        help="Overlap when --split_valid is passed.")
+
+    # See model.py for doc
+    parser.add_argument("--growth",
+                        type=float,
+                        default=2.,
+                        help="Number of channels between two layers will increase by this factor")
+    parser.add_argument("--depth",
+                        type=int,
+                        default=6,
+                        help="Number of layers for the encoder and decoder")
+    parser.add_argument("--lstm_layers", type=int, default=2, help="Number of layers for the LSTM")
+    parser.add_argument("--channels",
+                        type=int,
+                        default=64,
+                        help="Number of channels for the first encoder layer")
+    parser.add_argument("--kernel_size",
+                        type=int,
+                        default=8,
+                        help="Kernel size for the (transposed) convolutions")
+    parser.add_argument("--conv_stride",
+                        type=int,
+                        default=4,
+                        help="Stride for the (transposed) convolutions")
+    parser.add_argument("--context",
+                        type=int,
+                        default=3,
+                        help="Context size for the decoder convolutions "
+                        "before the transposed convolutions")
+    parser.add_argument("--rescale",
+                        type=float,
+                        default=0.1,
+                        help="Initial weight rescale reference")
+    parser.add_argument("--no_resample", action="store_false",
+                        default=True, dest="resample",
+                        help="No Resampling of the input/output x2")
+    parser.add_argument("--no_glu",
+                        action="store_false",
+                        default=True,
+                        dest="glu",
+                        help="Replace all GLUs by ReLUs")
+    parser.add_argument("--no_rewrite",
+                        action="store_false",
+                        default=True,
+                        dest="rewrite",
+                        help="No 1x1 rewrite convolutions")
+    parser.add_argument("--normalize", action="store_true")
+    parser.add_argument("--no_norm_wav", action="store_false", dest='norm_wav', default=True)
+
+    # Tasnet options
+    parser.add_argument("--tasnet", action="store_true")
+    parser.add_argument("--split_valid",
+                        action="store_true",
+                        help="Predict chunks by chunks for valid and test. Required for tasnet")
+    parser.add_argument("--X", type=int, default=8)
+
+    # Other options
+    parser.add_argument("--show",
+                        action="store_true",
+                        help="Show model architecture, size and exit")
+    parser.add_argument("--save_model", action="store_true",
+                        help="Skip traning, just save final model "
+                             "for the current checkpoint value.")
+    parser.add_argument("--save_state",
+                        help="Skip training, just save state "
+                             "for the current checkpoint value. You should "
+                             "provide a model name as argument.")
+
+    # Quantization options
+    parser.add_argument("--q-min-size", type=float, default=1,
+                        help="Only quantize layers over this size (in MB)")
+    parser.add_argument(
+        "--qat", type=int, help="If provided, use QAT training with that many bits.")
+
+    parser.add_argument("--diffq", type=float, default=0)
+    parser.add_argument(
+        "--ms-target", type=float, default=162,
+        help="Model size target in MB, when using DiffQ. Best model will be kept "
+             "only if it is smaller than this target.")
+
+    return parser
+
+
+def get_name(parser, args):
+    """
+    Return the name of an experiment given the args. Some parameters are ignored,
+    for instance --workers, as they do not impact the final result.
+    """
+    ignore_args = set([
+        "checkpoints",
+        "deterministic",
+        "eval",
+        "evals",
+        "eval_cpu",
+        "eval_workers",
+        "logs",
+        "master",
+        "rank",
+        "restart",
+        "save",
+        "save_model",
+        "save_state",
+        "show",
+        "workers",
+        "world_size",
+    ])
+    parts = []
+    name_args = dict(args.__dict__)
+    for name, value in name_args.items():
+        if name in ignore_args:
+            continue
+        if value != parser.get_default(name):
+            if isinstance(value, Path):
+                parts.append(f"{name}={value.name}")
+            else:
+                parts.append(f"{name}={value}")
+    if parts:
+        name = " ".join(parts)
+    else:
+        name = "default"
+    return name