feat: minor update

.gitignore

@@ -9,8 +9,6 @@
 *.user
 *.userosscache
 *.sln.docstates
-load.py
-button.py
 
 # User-specific files (MonoDevelop/Xamarin Studio)
 *.userprefs

README.md

@@ -1,5 +1,5 @@
 ---
-title: RVC Inference
+title: RVC Genshin Impact
 emoji: 🎤
 colorFrom: red
 colorTo: purple

app-full.py

@@ -1,289 +0,0 @@
-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 infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
-    def vc_fn(
-        input_audio,
-        upload_audio,
-        upload_mode,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        tts_mode,
-        tts_text,
-        tts_voice
-    ):
-        try:
-            if tts_mode:
-                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)
-            else:
-                if upload_mode:
-                    if input_audio is None:
-                        return "You need to upload an audio", None
-                    sampling_rate, audio = upload_audio
-                    duration = audio.shape[0] / sampling_rate
-                    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)
-                else:
-                    audio, sr = librosa.load(input_audio, sr=16000, mono=True)
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                index_rate,
-                if_f0,
-                f0_file=None,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
-    if url != "":
-        if not os.path.exists("dl_audio"):
-            os.mkdir("dl_audio")
-        if audio_provider == "Youtube":
-            ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'dl_audio/youtube_audio',
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([url])
-            audio_path = "dl_audio/youtube_audio.wav"
-        else:
-            # Spotify doesnt work.
-            # Need to find other solution soon.
-            ''' 
-            command = f"spotdl download {url} --output dl_audio/.wav"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            audio_path = "dl_audio/spotify_audio.wav"
-            '''
-        if split_model == "htdemucs":
-            command = f"demucs --two-stems=vocals {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
-        else:
-            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
-    else:
-        raise gr.Error("URL Required!")
-        return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_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"
-    if split_model == "htdemucs":
-        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
-    else:
-        inst_path = "output/mdx_extra_q/youtube_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 [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -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_to_tts_mode(tts_mode, upload_mode):
-    if tts_mode:
-        return gr.Textbox.update(visible=False), gr.Audio.update(visible=False), gr.Checkbox.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        if upload_mode:
-            return gr.Textbox.update(visible=False), gr.Audio.update(visible=True), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-        else:
-            return gr.Textbox.update(visible=True), gr.Audio.update(visible=False), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-def change_to_upload_mode(upload_mode):
-    if upload_mode:
-        return gr.Textbox().update(visible=False), gr.Audio().update(visible=True)
-    else:
-        return gr.Textbox().update(visible=True), gr.Audio().update(visible=False)
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    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 model_name, info in models_info.items():
-            if not info['enable']:
-                continue
-            model_title = info['title']
-            model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{model_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{model_name}/{info['feature_retrieval_library']}"
-            cpt = torch.load(f"weights/{category_folder}/{model_name}/{model_name}.pth", 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)
-            if if_f0 == 1:
-                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-            else:
-                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            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: {model_name}")
-            models.append((model_name, model_title, model_author, model_cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index)))
-        categories.append([category_title, category_folder, description, models])
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> RVC Models\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "### <center> This project was inspired by [zomehwh](https://huggingface.co/spaces/zomehwh/rvc-models) and [ardha27](https://huggingface.co/spaces/ardha27/rvc-models)\n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
-        )
-        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 added the model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, vc_fn) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                gr.Markdown(
-                                    '<div align="center">'
-                                    f'<div>{title}</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():
-                                with gr.Column():
-                                    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, value="Youtube", info="Select provider [REQUIRED: UPLOAD MODE = OFF] (Default: Youtube)")
-                                    vc_link = gr.Textbox(label="Youtube URL", info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A")
-                                    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                    vc_split = gr.Button("Split Audio", variant="primary")
-                                    vc_vocal_preview = gr.Audio(label="Vocal Preview")
-                                    vc_inst_preview = gr.Audio(label="Instrumental Preview")
-                                    vc_audio_preview = gr.Audio(label="Audio Preview")
-                                with gr.Column():
-                                    upload_mode = gr.Checkbox(label="Upload mode", value=False, info="Enable to upload audio instead of audio path")
-                                    vc_input = gr.Textbox(label="Input audio path")
-                                    vc_upload = gr.Audio(label="Upload audio file", visible=False, interactive=True)
-                                    vc_transpose = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                    vc_f0method = gr.Radio(
-                                        label="Pitch extraction algorithm",
-                                        choices=["pm", "harvest"],
-                                        value="pm",
-                                        interactive=True,
-                                        info="PM is fast but Harvest is better for low frequencies. (Default: PM)"
-                                    )
-                                    vc_index_ratio = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label="Retrieval feature ratio",
-                                        value=0.6,
-                                        interactive=True,
-                                        info="(Default: 0.6)"
-                                    )
-                                    tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                                    tts_text = gr.Textbox(visible=False, label="TTS text")
-                                    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                    vc_submit = gr.Button("Convert", variant="primary")
-                                with gr.Column():
-                                    vc_volume = gr.Slider(
-                                        minimum=0,
-                                        maximum=10,
-                                        label="Vocal volume",
-                                        value=4,
-                                        interactive=True,
-                                        step=1,
-                                        info="Adjust vocal volume (Default: 4}"
-                                    )
-                                    vc_combined_output = gr.Audio(label="Output Combined Audio")
-                                    vc_combine =  gr.Button("Combine",variant="primary")
-                        vc_submit.click(vc_fn, [vc_input, vc_upload, upload_mode, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output])
-                        vc_split.click(cut_vocal_and_inst, [vc_link, vc_download_audio, vc_split_model], [vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input])
-                        vc_combine.click(combine_vocal_and_inst, [vc_output, vc_volume, vc_split_model], vc_combined_output)
-                        tts_mode.change(change_to_tts_mode, [tts_mode, upload_mode], [vc_input, vc_upload, upload_mode, tts_text, tts_voice])
-                        upload_mode.change(change_to_upload_mode, [upload_mode], [vc_input, vc_upload])
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)

app.py

@@ -138,12 +138,14 @@ if __name__ == '__main__':
         categories.append([category_title, category_folder, description, models])
     with gr.Blocks() as app:
         gr.Markdown(
-            "# <center> RVC Models\n"
+            "# <center> RVC Genshin Impact\n"
             "## <center> The input audio should be clean and pure voice without background music.\n"
-            "## <center> [Recommended to use google colab for more feature](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n"
+            "## <center> [Recommended to use google colab to use all genshin model & feature](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n"
+            "### <center> I limit the number of models to 15 due to an error caused by exceeding the available memory. (16 GB limit)\n"
             "### <center> This project was inspired by [zomehwh](https://huggingface.co/spaces/zomehwh/rvc-models) and [ardha27](https://huggingface.co/spaces/ardha27/rvc-models)\n"
             "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
+            "[![Original RVC Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
+            "[![RVC Inference Repo](https://badgen.net/badge/icon/github?icon=github&label)](https://github.com/ArkanDash/rvc-inference)"
         )
         for (folder_title, folder, description, models) in categories:
             with gr.TabItem(folder_title):

requirements-full.txt

@@ -1,49 +0,0 @@
-numba==0.56.4
-numpy==1.23.5
-scipy==1.9.3
-librosa==0.9.2
-llvmlite==0.39.0
-fairseq==0.12.2
-faiss-cpu==1.7.0; sys_platform == "darwin"
-faiss-cpu==1.7.2; sys_platform != "darwin"
-gradio
-Cython
-future>=0.18.3
-pydub>=0.25.1
-soundfile>=0.12.1
-ffmpeg-python>=0.2.0
-tensorboardX
-functorch>=2.0.0
-Jinja2>=3.1.2
-json5>=0.9.11
-Markdown
-matplotlib>=3.7.1
-matplotlib-inline>=0.1.6
-praat-parselmouth>=0.4.3
-Pillow>=9.1.1
-pyworld>=0.3.2
-resampy>=0.4.2
-scikit-learn>=1.2.2
-starlette>=0.26.1
-tensorboard
-tensorboard-data-server
-tensorboard-plugin-wit
-torchgen>=0.0.1
-tqdm>=4.65.0
-tornado>=6.2
-Werkzeug>=2.2.3
-uc-micro-py>=1.0.1
-sympy>=1.11.1
-tabulate>=0.9.0
-PyYAML>=6.0
-pyasn1>=0.4.8
-pyasn1-modules>=0.2.8
-fsspec>=2023.3.0
-absl-py>=1.4.0
-audioread
-uvicorn>=0.21.1
-colorama>=0.4.6
-edge-tts
-demucs
-yt_dlp
-ffmpeg

weights/genshin-impact/fischl-jp/added_IVF1225_Flat_nprobe_1.index

@@ -1,3 +0,0 @@
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-size 50599099

weights/genshin-impact/fischl-jp/fischl-jp.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:28c04e4cd0ddb1abc8a3d0ad0b7aea94fba37b9ef5903e2f4e8c2e62015844f5
-size 55027589

weights/genshin-impact/kaeya-jp/added_IVF1655_Flat_nprobe_1.index

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1afba8e77f937bf76bd10f54fcbb4ca119dc3b2c599dc4cbf0843d618c26ad15
-size 68325763

weights/genshin-impact/kaeya-jp/kaeya-jp.pth

@@ -1,3 +0,0 @@
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-oid sha256:6492cc522ecaef80436bae3ebdb6e6bd9e6242dc6be74c4e6283c7825285d480
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weights/genshin-impact/model_info.json

@@ -95,14 +95,6 @@
 		"feature_retrieval_library": "added_IVF1036_Flat_nprobe_1.index",
 		"author":"ArkanDash"
 	},
-	"fischl-jp": {
-		"enable": true,
-		"name": "fischl-jp",
-        "title": "Genshin Impact - Fischl",
-        "cover": "cover.png",
-		"feature_retrieval_library": "added_IVF1225_Flat_nprobe_1.index",
-		"author":"ArkanDash"
-	},
 	"ayato-jp": {
 		"enable": true,
 		"name": "ayato-jp",
@@ -126,13 +118,5 @@
 		"cover": "cover.png",
 		"feature_retrieval_library": "added_IVF1672_Flat_nprobe_1.index",
 		"author":"ArkanDash"
-	},
-	"kaeya-jp": {
-		"enable": true,
-		"name": "kaeya-jp",
-		"title": "Genshin Impact - Kaeya",
-		"cover": "cover.png",
-		"feature_retrieval_library": "added_IVF1655_Flat_nprobe_1.index",
-		"author":"ArkanDash"
-    }
+	}
 }