fix

.gitignore

@@ -21,7 +21,6 @@ dist/
 downloads/
 eggs/
 .eggs/
-lib/
 lib64/
 parts/
 sdist/

app.py

@@ -16,7 +16,6 @@ from src.constants import ONNX_MODEL_PATH
 INPUT_FOLDER = "./datasets/input"
 OUTPUT_FOLDER = "./datasets/output"
 
-
 def main():
     # Set page configuration and theming
     st.set_page_config(
@@ -77,6 +76,17 @@ def main():
         # TODO: Is it encoding it wrong? Maybe fix it later.
         st.audio(data=vocals_array, format="audio/mpeg", sample_rate=samplerate)
 
+def test():
+    import soundfile
+    import numpy as np
+
+    audio, sr = soundfile.read(r'G:\AI-Personal-Projects\huggingface-repos\Vocal-Isolator\datasets\input\test-falsetto.wav')
+    mono_audio = np.mean(audio, axis=1)
+    print(audio)
+    print(len(audio))
+    print(mono_audio)
+    print(len(mono_audio))
 
 if __name__ == "__main__":
     main()
+    # test()

gradio_app.py

@@ -0,0 +1,678 @@
+import os
+import glob
+import json
+import traceback
+import logging
+import gradio as gr
+import numpy as np
+import librosa
+import torch
+import asyncio
+import edge_tts
+import yt_dlp
+import ffmpeg
+import subprocess
+import sys
+import io
+import wave
+from datetime import datetime
+from fairseq import checkpoint_utils
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from vc_infer_pipeline import VC
+from config import Config
+config = Config()
+logging.getLogger("numba").setLevel(logging.WARNING)
+spaces = os.getenv("SYSTEM") == "spaces"
+force_support = None
+if config.unsupported is False:
+    if config.device == "mps" or config.device == "cpu":
+        force_support = False
+else:
+    force_support = True
+
+audio_mode = []
+f0method_mode = []
+f0method_info = ""
+
+if force_support is False or spaces is True:
+    if spaces is True:
+        audio_mode = ["Upload audio", "TTS Audio"]
+    else:
+        audio_mode = ["Input path", "Upload audio", "TTS Audio"]
+    f0method_mode = ["pm", "harvest"]
+    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
+else:
+    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
+    f0method_mode = ["pm", "harvest", "crepe"]
+    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
+
+if os.path.isfile("rmvpe.pt"):
+    f0method_mode.insert(2, "rmvpe")
+
+def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
+    def vc_fn(
+        vc_audio_mode,
+        vc_input, 
+        vc_upload,
+        tts_text,
+        tts_voice,
+        f0_up_key,
+        f0_method,
+        index_rate,
+        filter_radius,
+        resample_sr,
+        rms_mix_rate,
+        protect,
+    ):
+        try:
+            logs = []
+            print(f"Converting using {model_name}...")
+            logs.append(f"Converting using {model_name}...")
+            yield "\n".join(logs), None
+            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
+                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
+            elif vc_audio_mode == "Upload audio":
+                if vc_upload is None:
+                    return "You need to upload an audio", None
+                sampling_rate, audio = vc_upload
+                duration = audio.shape[0] / sampling_rate
+                if duration > 20 and spaces:
+                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
+                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+                if len(audio.shape) > 1:
+                    audio = librosa.to_mono(audio.transpose(1, 0))
+                if sampling_rate != 16000:
+                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+            elif vc_audio_mode == "TTS Audio":
+                if len(tts_text) > 100 and spaces:
+                    return "Text is too long", None
+                if tts_text is None or tts_voice is None:
+                    return "You need to enter text and select a voice", None
+                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
+                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
+                vc_input = "tts.mp3"
+            times = [0, 0, 0]
+            f0_up_key = int(f0_up_key)
+            audio_opt = vc.pipeline(
+                hubert_model,
+                net_g,
+                0,
+                audio,
+                vc_input,
+                times,
+                f0_up_key,
+                f0_method,
+                file_index,
+                # file_big_npy,
+                index_rate,
+                if_f0,
+                filter_radius,
+                tgt_sr,
+                resample_sr,
+                rms_mix_rate,
+                version,
+                protect,
+                f0_file=None,
+            )
+            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
+            print(f"{model_name} | {info}")
+            logs.append(f"Successfully Convert {model_name}\n{info}")
+            yield "\n".join(logs), (tgt_sr, audio_opt)
+        except:
+            info = traceback.format_exc()
+            print(info)
+            yield info, None
+    return vc_fn
+
+def load_model():
+    categories = []
+    if os.path.isfile("weights/folder_info.json"):
+        with open("weights/folder_info.json", "r", encoding="utf-8") as f:
+            folder_info = json.load(f)
+        for category_name, category_info in folder_info.items():
+            if not category_info['enable']:
+                continue
+            category_title = category_info['title']
+            category_folder = category_info['folder_path']
+            description = category_info['description']
+            models = []
+            with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
+                models_info = json.load(f)
+            for character_name, info in models_info.items():
+                if not info['enable']:
+                    continue
+                model_title = info['title']
+                model_name = info['model_path']
+                model_author = info.get("author", None)
+                model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
+                model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
+                cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
+                tgt_sr = cpt["config"][-1]
+                cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+                if_f0 = cpt.get("f0", 1)
+                version = cpt.get("version", "v1")
+                if version == "v1":
+                    if if_f0 == 1:
+                        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+                    else:
+                        net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+                    model_version = "V1"
+                elif version == "v2":
+                    if if_f0 == 1:
+                        net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+                    else:
+                        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+                    model_version = "V2"
+                del net_g.enc_q
+                print(net_g.load_state_dict(cpt["weight"], strict=False))
+                net_g.eval().to(config.device)
+                if config.is_half:
+                    net_g = net_g.half()
+                else:
+                    net_g = net_g.float()
+                vc = VC(tgt_sr, config)
+                print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
+                models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
+            categories.append([category_title, category_folder, description, models])
+    else:
+        categories = []
+    return categories
+
+def download_audio(url, audio_provider):
+    logs = []
+    if url == "":
+        raise gr.Error("URL Required!")
+        return "URL Required"
+    if not os.path.exists("dl_audio"):
+        os.mkdir("dl_audio")
+    if audio_provider == "Youtube":
+        logs.append("Downloading the audio...")
+        yield None, "\n".join(logs)
+        ydl_opts = {
+            'noplaylist': True,
+            'format': 'bestaudio/best',
+            'postprocessors': [{
+                'key': 'FFmpegExtractAudio',
+                'preferredcodec': 'wav',
+            }],
+            "outtmpl": 'dl_audio/audio',
+        }
+        audio_path = "dl_audio/audio.wav"
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([url])
+        logs.append("Download Complete.")
+        yield audio_path, "\n".join(logs)
+
+def cut_vocal_and_inst(split_model):
+    logs = []
+    logs.append("Starting the audio splitting process...")
+    yield "\n".join(logs), None, None, None, None
+    command = f"demucs --two-stems=vocals -n {split_model} dl_audio/audio.wav -o output"
+    result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
+    for line in result.stdout:
+        logs.append(line)
+        yield "\n".join(logs), None, None, None, None
+    print(result.stdout)
+    vocal = f"output/{split_model}/audio/vocals.wav"
+    inst = f"output/{split_model}/audio/no_vocals.wav"
+    logs.append("Audio splitting complete.")
+    yield "\n".join(logs), vocal, inst, vocal
+
+def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume, split_model):
+    if not os.path.exists("output/result"):
+        os.mkdir("output/result")
+    vocal_path = "output/result/output.wav"
+    output_path = "output/result/combine.mp3"
+    inst_path = f"output/{split_model}/audio/no_vocals.wav"
+    with wave.open(vocal_path, "w") as wave_file:
+        wave_file.setnchannels(1) 
+        wave_file.setsampwidth(2)
+        wave_file.setframerate(audio_data[0])
+        wave_file.writeframes(audio_data[1].tobytes())
+    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}'
+    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+    print(result.stdout.decode())
+    return output_path
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+def change_audio_mode(vc_audio_mode):
+    if vc_audio_mode == "Input path":
+        return (
+            # Input & Upload
+            gr.Textbox.update(visible=True),
+            gr.Checkbox.update(visible=False),
+            gr.Audio.update(visible=False),
+            # Youtube
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Button.update(visible=False),
+            # Splitter
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Button.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Button.update(visible=False),
+            # TTS
+            gr.Textbox.update(visible=False),
+            gr.Dropdown.update(visible=False)
+        )
+    elif vc_audio_mode == "Upload audio":
+        return (
+            # Input & Upload
+            gr.Textbox.update(visible=False),
+            gr.Checkbox.update(visible=True),
+            gr.Audio.update(visible=True),
+            # Youtube
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Button.update(visible=False),
+            # Splitter
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Button.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Button.update(visible=False),
+            # TTS
+            gr.Textbox.update(visible=False),
+            gr.Dropdown.update(visible=False)
+        )
+    elif vc_audio_mode == "Youtube":
+        return (
+            # Input & Upload
+            gr.Textbox.update(visible=False),
+            gr.Checkbox.update(visible=False),
+            gr.Audio.update(visible=False),
+            # Youtube
+            gr.Dropdown.update(visible=True),
+            gr.Textbox.update(visible=True),
+            gr.Textbox.update(visible=True),
+            gr.Button.update(visible=True),
+            # Splitter
+            gr.Dropdown.update(visible=True),
+            gr.Textbox.update(visible=True),
+            gr.Button.update(visible=True),
+            gr.Audio.update(visible=True),
+            gr.Audio.update(visible=True),
+            gr.Audio.update(visible=True),
+            gr.Slider.update(visible=True),
+            gr.Slider.update(visible=True),
+            gr.Audio.update(visible=True),
+            gr.Button.update(visible=True),
+            # TTS
+            gr.Textbox.update(visible=False),
+            gr.Dropdown.update(visible=False)
+        )
+    elif vc_audio_mode == "TTS Audio":
+        return (
+            # Input & Upload
+            gr.Textbox.update(visible=False),
+            gr.Checkbox.update(visible=False),
+            gr.Audio.update(visible=False),
+            # Youtube
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Button.update(visible=False),
+            # Splitter
+            gr.Dropdown.update(visible=False),
+            gr.Textbox.update(visible=False),
+            gr.Button.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Slider.update(visible=False),
+            gr.Audio.update(visible=False),
+            gr.Button.update(visible=False),
+            # TTS
+            gr.Textbox.update(visible=True),
+            gr.Dropdown.update(visible=True)
+        )
+
+def use_microphone(microphone):
+    if microphone == True:
+        return gr.Audio.update(source="microphone")
+    else:
+        return gr.Audio.update(source="upload")
+
+if __name__ == '__main__':
+    load_hubert()
+    categories = load_model()
+    tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
+    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
+    with gr.Blocks() as app:
+        gr.Markdown(
+            "<div align='center'>\n\n"+
+            "# RVC Genshin Impact\n\n"+
+            "### Recommended to use Google Colab to use other character and feature.\n\n"+
+            "[![Colab](https://img.shields.io/badge/Colab-RVC%20Genshin%20Impact-blue?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"+
+            "</div>\n\n"+
+            "[![Repository](https://img.shields.io/badge/Github-Multi%20Model%20RVC%20Inference-blue?style=for-the-badge&logo=github)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)"
+        )
+        if categories == []:
+            gr.Markdown(
+                "<div align='center'>\n\n"+
+                "## No model found, please add the model into weights folder\n\n"+
+                "</div>"
+            )
+        for (folder_title, folder, description, models) in categories:
+            with gr.TabItem(folder_title):
+                if description:
+                    gr.Markdown(f"### <center> {description}")
+                with gr.Tabs():
+                    if not models:
+                        gr.Markdown("# <center> No Model Loaded.")
+                        gr.Markdown("## <center> Please add the model or fix your model path.")
+                        continue
+                    for (name, title, author, cover, model_version, vc_fn) in models:
+                        with gr.TabItem(name):
+                            with gr.Row():
+                                gr.Markdown(
+                                    '<div align="center">'
+                                    f'<div>{title}</div>\n'+
+                                    f'<div>RVC {model_version} Model</div>\n'+
+                                    (f'<div>Model author: {author}</div>' if author else "")+
+                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
+                                    '</div>'
+                                )
+                            with gr.Row():
+                                if spaces is False:
+                                    with gr.TabItem("Input"):
+                                        with gr.Row():
+                                            with gr.Column():
+                                                vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
+                                                # Input
+                                                vc_input = gr.Textbox(label="Input audio path", visible=False)
+                                                # Upload
+                                                vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
+                                                vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
+                                                # Youtube
+                                                vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
+                                                vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
+                                                vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                                vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
+                                                vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
+                                                # TTS
+                                                tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
+                                                tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
+                                            with gr.Column():
+                                                vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
+                                                vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                                vc_split = gr.Button("Split Audio", variant="primary", visible=False)
+                                                vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
+                                                vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
+                                    with gr.TabItem("Convert"):
+                                        with gr.Row():
+                                            with gr.Column():
+                                                vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
+                                                f0method0 = gr.Radio(
+                                                    label="Pitch extraction algorithm",
+                                                    info=f0method_info,
+                                                    choices=f0method_mode,
+                                                    value="pm",
+                                                    interactive=True
+                                                )
+                                                index_rate1 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=1,
+                                                    label="Retrieval feature ratio",
+                                                    info="(Default: 0.7)",
+                                                    value=0.7,
+                                                    interactive=True,
+                                                )
+                                                filter_radius0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=7,
+                                                    label="Apply Median Filtering",
+                                                    info="The value represents the filter radius and can reduce breathiness.",
+                                                    value=3,
+                                                    step=1,
+                                                    interactive=True,
+                                                )
+                                                resample_sr0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=48000,
+                                                    label="Resample the output audio",
+                                                    info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
+                                                    value=0,
+                                                    step=1,
+                                                    interactive=True,
+                                                )
+                                                rms_mix_rate0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=1,
+                                                    label="Volume Envelope",
+                                                    info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
+                                                    value=1,
+                                                    interactive=True,
+                                                )
+                                                protect0 = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=0.5,
+                                                    label="Voice Protection",
+                                                    info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
+                                                    value=0.5,
+                                                    step=0.01,
+                                                    interactive=True,
+                                                )
+                                            with gr.Column():
+                                                vc_log = gr.Textbox(label="Output Information", interactive=False)
+                                                vc_output = gr.Audio(label="Output Audio", interactive=False)
+                                                vc_convert = gr.Button("Convert", variant="primary")
+                                                vc_vocal_volume = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=10,
+                                                    label="Vocal volume",
+                                                    value=1,
+                                                    interactive=True,
+                                                    step=1,
+                                                    info="Adjust vocal volume (Default: 1}",
+                                                    visible=False
+                                                )
+                                                vc_inst_volume = gr.Slider(
+                                                    minimum=0,
+                                                    maximum=10,
+                                                    label="Instrument volume",
+                                                    value=1,
+                                                    interactive=True,
+                                                    step=1,
+                                                    info="Adjust instrument volume (Default: 1}",
+                                                    visible=False
+                                                )
+                                                vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
+                                                vc_combine =  gr.Button("Combine",variant="primary", visible=False)
+                                else:
+                                    with gr.Column():
+                                        vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
+                                        # Input
+                                        vc_input = gr.Textbox(label="Input audio path", visible=False)
+                                        # Upload
+                                        vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
+                                        vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
+                                        # Youtube
+                                        vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
+                                        vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
+                                        vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                        vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
+                                        vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
+                                        # Splitter
+                                        vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
+                                        vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
+                                        vc_split = gr.Button("Split Audio", variant="primary", visible=False)
+                                        vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
+                                        vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
+                                        # TTS
+                                        tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
+                                        tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
+                                    with gr.Column():
+                                        vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
+                                        f0method0 = gr.Radio(
+                                            label="Pitch extraction algorithm",
+                                            info=f0method_info,
+                                            choices=f0method_mode,
+                                            value="pm",
+                                            interactive=True
+                                        )
+                                        index_rate1 = gr.Slider(
+                                            minimum=0,
+                                            maximum=1,
+                                            label="Retrieval feature ratio",
+                                            info="(Default: 0.7)",
+                                            value=0.7,
+                                            interactive=True,
+                                        )
+                                        filter_radius0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=7,
+                                            label="Apply Median Filtering",
+                                            info="The value represents the filter radius and can reduce breathiness.",
+                                            value=3,
+                                            step=1,
+                                            interactive=True,
+                                        )
+                                        resample_sr0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=48000,
+                                            label="Resample the output audio",
+                                            info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
+                                            value=0,
+                                            step=1,
+                                            interactive=True,
+                                        )
+                                        rms_mix_rate0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=1,
+                                            label="Volume Envelope",
+                                            info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
+                                            value=1,
+                                            interactive=True,
+                                        )
+                                        protect0 = gr.Slider(
+                                            minimum=0,
+                                            maximum=0.5,
+                                            label="Voice Protection",
+                                            info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
+                                            value=0.5,
+                                            step=0.01,
+                                            interactive=True,
+                                        )
+                                    with gr.Column():
+                                        vc_log = gr.Textbox(label="Output Information", interactive=False)
+                                        vc_output = gr.Audio(label="Output Audio", interactive=False)
+                                        vc_convert = gr.Button("Convert", variant="primary")
+                                        vc_vocal_volume = gr.Slider(
+                                            minimum=0,
+                                            maximum=10,
+                                            label="Vocal volume",
+                                            value=1,
+                                            interactive=True,
+                                            step=1,
+                                            info="Adjust vocal volume (Default: 1}",
+                                            visible=False
+                                        )
+                                        vc_inst_volume = gr.Slider(
+                                            minimum=0,
+                                            maximum=10,
+                                            label="Instrument volume",
+                                            value=1,
+                                            interactive=True,
+                                            step=1,
+                                            info="Adjust instrument volume (Default: 1}",
+                                            visible=False
+                                        )
+                                        vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
+                                        vc_combine =  gr.Button("Combine",variant="primary", visible=False)
+                        vc_convert.click(
+                            fn=vc_fn, 
+                            inputs=[
+                                vc_audio_mode,
+                                vc_input,
+                                vc_upload,
+                                tts_text,
+                                tts_voice,
+                                vc_transform0,
+                                f0method0,
+                                index_rate1,
+                                filter_radius0,
+                                resample_sr0,
+                                rms_mix_rate0,
+                                protect0,
+                            ], 
+                            outputs=[vc_log ,vc_output]
+                        )
+                        vc_download_button.click(
+                            fn=download_audio, 
+                            inputs=[vc_link, vc_download_audio], 
+                            outputs=[vc_audio_preview, vc_log_yt]
+                        )
+                        vc_split.click(
+                            fn=cut_vocal_and_inst, 
+                            inputs=[vc_split_model], 
+                            outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input]
+                        )
+                        vc_combine.click(
+                            fn=combine_vocal_and_inst,
+                            inputs=[vc_output, vc_vocal_volume, vc_inst_volume, vc_split_model],
+                            outputs=[vc_combined_output]
+                        )
+                        vc_microphone_mode.change(
+                            fn=use_microphone,
+                            inputs=vc_microphone_mode,
+                            outputs=vc_upload
+                        )
+                        vc_audio_mode.change(
+                            fn=change_audio_mode,
+                            inputs=[vc_audio_mode],
+                            outputs=[
+                                vc_input,
+                                vc_microphone_mode,
+                                vc_upload,
+                                vc_download_audio,
+                                vc_link,
+                                vc_log_yt,
+                                vc_download_button,
+                                vc_split_model,
+                                vc_split_log,
+                                vc_split,
+                                vc_audio_preview,
+                                vc_vocal_preview,
+                                vc_inst_preview,
+                                vc_vocal_volume,
+                                vc_inst_volume,
+                                vc_combined_output,
+                                vc_combine,
+                                tts_text,
+                                tts_voice
+                            ]
+                        )
+        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)

requirements.txt

@@ -1,8 +1,15 @@
 torch~=2.0.1
 onnxruntime~=1.15.1
 onnxruntime-gpu~=1.15.1
-librosa~=0.10.0.post2
+librosa~=0.9.1
 soundfile~=0.12.1
 numpy~=1.24.4
 scipy~=1.11.1
-streamlit~=1.25.0
+streamlit~=1.25.0
+fairseq~=0.12.2
+praat-parselmouth~=0.4.3
+pyworld~=0.3.4
+faiss-cpu~=1.7.4
+torchcrepe~=0.0.21
+chardet~=5.2.0
+ffmpeg-python~=0.2.0

src/Sound_Feature_Extraction/__init__.py

@@ -0,0 +1 @@
+# src.Sound_Feature_Extraction package

src/__init__.py

@@ -0,0 +1 @@
+# src package

src/config.py

@@ -0,0 +1,99 @@
+import argparse
+import sys
+import torch
+from multiprocessing import cpu_count
+
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.gpu_mem = None
+        (
+            self.colab,
+            self.api,
+            self.unsupported
+        ) = self.arg_parse()
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    @staticmethod
+    def arg_parse() -> tuple:
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--colab", action="store_true", help="Launch in colab")
+        parser.add_argument("--api", action="store_true", help="Launch with api")
+        parser.add_argument("--unsupported", action="store_true", help="Enable unsupported feature")
+        cmd_opts = parser.parse_args()
+
+        return (
+            cmd_opts.colab,
+            cmd_opts.api,
+            cmd_opts.unsupported
+        )
+
+    # 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
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            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 "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                print("INFO: Found GPU", self.gpu_name, ", force to fp32")
+                self.is_half = False
+            else:
+                print("INFO: Found GPU", self.gpu_name)
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+        elif self.has_mps():
+            print("INFO: No supported Nvidia GPU found, use MPS instead")
+            self.device = "mps"
+            self.is_half = False
+        else:
+            print("INFO: No supported Nvidia GPU found, use CPU instead")
+            self.device = "cpu"
+            self.is_half = False
+
+        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 != None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+
+        return x_pad, x_query, x_center, x_max

src/infer.py

@@ -1,20 +0,0 @@
-# Standard Library Imports
-import os
-import subprocess
-
-# Third Party Imports
-import torch
-import onnxruntime as ort
-
-# Local Imports
-from models.MDX_net.mdx_net import Conv_TDF_net_trimm
-from loader import Loader
-
-vocal_path = "./datasets/output/vocals.wav"
-
-# Global Variables
-COMPUTATION_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def main():
-    print(COMPUTATION_DEVICE)

src/loader.py

@@ -16,11 +16,13 @@ class Loader:
     def __init__(self, INPUT_FOLDER, OUTPUT_FOLDER):
         self.input = INPUT_FOLDER
         self.output = OUTPUT_FOLDER
-    def load_wav(self, name) -> Tuple[ndarray, int]:
+
+    def load_wav(self, name) -> Tuple[torch.Tensor, int]:
         music_array, samplerate = librosa.load(
             os.path.join(self.input, name + ".wav"), mono=False, sr=44100
         )
-        return music_array, samplerate
+        music_tensor = torch.tensor(music_array, dtype=torch.float32)
+        return music_tensor, samplerate
 
     def prepare_uploaded_file(self, uploaded_file) -> Tuple[torch.Tensor, int]:
         music_array, samplerate = librosa.load(uploaded_file, mono=False, sr=44100)

src/models/MDX_net/__init__.py

@@ -0,0 +1 @@
+# src.models.MDX_net package

src/models/MDX_net/kimvocal.py

@@ -43,7 +43,9 @@ class KimVocal:
         )
 
         # Start running the session for the model
-        ort_session = ort.InferenceSession(ONNX_MODEL_PATH, providers=EXECUTION_PROVIDER_LIST)
+        ort_session = ort.InferenceSession(
+            ONNX_MODEL_PATH, providers=EXECUTION_PROVIDER_LIST
+        )
 
         # TODO: any way to optimize against silence? I think that's what skips are for, gotta double check.
         # process one chunk at a time (batch_size=1)

src/models/Pitch_Feature_Extraction/__init__.py

@@ -0,0 +1 @@
+# src.models.Pitch_Feature_Extraction package

src/models/__init__.py

@@ -0,0 +1 @@
+# src.models package