app.py

try:
    print("app.py is running! Congrats!")
    import io
    import os
    import torch
    
    # os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
    import gradio as gr
    import librosa
    import numpy as np
    import soundfile
    import logging
    from fairseq import checkpoint_utils
    from my_utils import load_audio
    from vc_infer_pipeline import VC
    import traceback
    from config import Config
    from infer_pack.models import (
        SynthesizerTrnMs256NSFsid,
        SynthesizerTrnMs256NSFsid_nono,
        SynthesizerTrnMs768NSFsid,
        SynthesizerTrnMs768NSFsid_nono,
    )
    from i18n import I18nAuto
    
    logging.getLogger("numba").setLevel(logging.WARNING)
    logging.getLogger("markdown_it").setLevel(logging.WARNING)
    logging.getLogger("urllib3").setLevel(logging.WARNING)
    logging.getLogger("matplotlib").setLevel(logging.WARNING)
    
    i18n = I18nAuto()
    i18n.print()
    
    config = Config()
    
    weight_root = "weights"
    weight_uvr5_root = "uvr5_weights"
    index_root = "logs"
    names = []
    hubert_model = None
    for name in os.listdir(weight_root):
        if name.endswith(".pth"):
            names.append(name)
    index_paths = []
    for root, dirs, files in os.walk(index_root, topdown=False):
        for name in files:
            if name.endswith(".index") and "trained" not in name:
                index_paths.append("%s/%s" % (root, name))
    
    
    def get_vc(sid):
        global n_spk, tgt_sr, net_g, vc, cpt, version
        if sid == "" or sid == []:
            global hubert_model
            if hubert_model != None:  # Considering polling, we need to add a check to see if the sid is switched from a model to a model-free state.
                print("clean_empty_cache")
                del net_g, n_spk, vc, hubert_model, tgt_sr  # ,cpt
                hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
                ###The downstairs won't be clean without this much trouble
                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"])
                elif version == "v2":
                    if if_f0 == 1:
                        net_g = SynthesizerTrnMs768NSFsid(
                            *cpt["config"], is_half=config.is_half
                        )
                    else:
                        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
                del net_g, cpt
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
                cpt = None
            return {"visible": False, "__type__": "update"}
        person = "%s/%s" % (weight_root, sid)
        print("loading %s" % person)
        cpt = torch.load(person, 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"])
        elif version == "v2":
            if if_f0 == 1:
                net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
            else:
                net_g = SynthesizerTrnMs768NSFsid_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)
        n_spk = cpt["config"][-3]
        return {"visible": True, "maximum": n_spk, "__type__": "update"}
    
    
    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 vc_single(
        sid,
        input_audio_path,
        f0_up_key,
        f0_file,
        f0_method,
        file_index,
        file_index2,
        # file_big_npy,
        index_rate,
        filter_radius,
        resample_sr,
        rms_mix_rate,
        protect,
    ):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
        global tgt_sr, net_g, vc, hubert_model, version
        if input_audio_path is None:
            return "You need to upload an audio", None
        f0_up_key = int(f0_up_key)
        try:
            audio = input_audio_path[1] / 32768.0
            if len(audio.shape) == 2:
                audio = np.mean(audio, -1)
            audio = librosa.resample(audio, orig_sr=input_audio_path[0], target_sr=16000)
            audio_max = np.abs(audio).max() / 0.95
            if audio_max > 1:
                audio /= audio_max
            times = [0, 0, 0]
            if hubert_model == None:
                load_hubert()
            if_f0 = cpt.get("f0", 1)
            file_index = (
                (
                    file_index.strip(" ")
                    .strip('"')
                    .strip("\n")
                    .strip('"')
                    .strip(" ")
                    .replace("trained", "added")
                )
                if file_index != ""
                else file_index2
            )  # Prevent newbies from making mistakes and automatically replace them for them
            # file_big_npy = (
            #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
            # )
            audio_opt = vc.pipeline(
                hubert_model,
                net_g,
                sid,
                audio,
                input_audio_path,
                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=f0_file,
            )
            if resample_sr >= 16000 and tgt_sr != resample_sr:
                tgt_sr = resample_sr
            index_info = (
                "Using index:%s." % file_index
                if os.path.exists(file_index)
                else "Index not used."
            )
            return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
                index_info,
                times[0],
                times[1],
                times[2],
            ), (tgt_sr, audio_opt)
        except:
            info = traceback.format_exc()
            print(info)
            return info, (None, None)
    
    
    app = gr.Blocks()
    with app:
        with gr.Tabs():
            with gr.TabItem("Online demo"):
                gr.Markdown(
                    value="""
                    RVC Online demo
                    """
                )
                sid = gr.Dropdown(label=i18n("Mystery Tone"), choices=sorted(names))
                with gr.Column():
                    spk_item = gr.Slider(
                        minimum=0,
                        maximum=2333,
                        step=1,
                        label=i18n("Please select the speaker id"),
                        value=0,
                        visible=False,
                        interactive=True,
                    )
                sid.change(
                    fn=get_vc,
                    inputs=[sid],
                    outputs=[spk_item],
                )
                gr.Markdown(
                    value=i18n("For male to female, +12key is recommended, for female to male, -12key is recommended. If the sound range explodes and causes timbre distortion, you can adjust it to the appropriate range yourself.")
                )
                vc_input3 = gr.Audio(label="Upload audio (less than 90 seconds in length)")
                vc_transform0 = gr.Number(label=i18n("Transpose(integer, number of semitones, octave up 12 octave down -12)"), value=0)
                f0method0 = gr.Radio(
                    label=i18n("Select the pitch extraction algorithm. You can use pm to speed up the input singing voice. Harvest has good bass but is extremely slow. Crepe has good effect but consumes GPU."),
                    choices=["pm", "harvest", "crepe"],
                    value="pm",
                    interactive=True,
                )
                filter_radius0 = gr.Slider(
                    minimum=0,
                    maximum=7,
                    label=i18n(">=3, use median filtering on the result of harvest pitch recognition, the value is the filter radius, which can reduce mute"),
                    value=3,
                    step=1,
                    interactive=True,
                )
                with gr.Column():
                    file_index1 = gr.Textbox(
                        label=i18n("Feature retrieval library file path, if empty, use the drop-down selection result"),
                        value="",
                        interactive=False,
                        visible=False,
                    )
                file_index2 = gr.Dropdown(
                    label=i18n("Automatically detect index path, drop-down selection"),
                    choices=sorted(index_paths),
                    interactive=True,
                )
                index_rate1 = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Search feature proportion"),
                    value=0.88,
                    interactive=True,
                )
                resample_sr0 = gr.Slider(
                    minimum=0,
                    maximum=48000,
                    label=i18n("Post-processing resampling to the final sampling rate, 0 means no resampling"),
                    value=0,
                    step=1,
                    interactive=True,
                )
                rms_mix_rate0 = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("The input source volume envelope replaces the output volume envelope blending ratio. The closer it is to 1, the more the output envelope is used."),
                    value=1,
                    interactive=True,
                )
                protect0 = gr.Slider(
                    minimum=0,
                    maximum=0.5,
                    label=i18n("Protects clear consonants and breathing sounds, and prevents electronic music tearing and other artifacts. It is not enabled when it is set to 0.5. It is more effective when it is lowered, but the indexing effect may be reduced."),
                    value=0.33,
                    step=0.01,
                    interactive=True,
                )
                f0_file = gr.File(label=i18n("F0 curve file, optional, one line per pitch, replaces the default F0 and sharp and flat tones"))
                but0 = gr.Button(i18n("Convert"), variant="primary")
                vc_output1 = gr.Textbox(label=i18n("Output information"))
                vc_output2 = gr.Audio(label=i18n("Output audio (three dots in the lower right corner, click to download)"))
                but0.click(
                    vc_single,
                    [
                        spk_item,
                        vc_input3,
                        vc_transform0,
                        f0_file,
                        f0method0,
                        file_index1,
                        file_index2,
                        # file_big_npy1,
                        index_rate1,
                        filter_radius0,
                        resample_sr0,
                        rms_mix_rate0,
                        protect0,
                    ],
                    [vc_output1, vc_output2],
                )
    
    
    app.launch(server_name="0.0.0.0", server_port=7860)
except Exception as e:
    print("Startup error:", e)