Delete app-full.py

app-full.py

@@ -1,254 +0,0 @@
-import os
-import json
-import argparse
-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 (
-    is_half,
-    device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
-    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 len(tts_text) > 100 and limitation:
-                    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)
-            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,
-                file_big_npy,
-                index_rate,
-                if_f0,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return "Success", (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def cut_vocal_and_inst(yt_url):
-    if yt_url != "":
-        if not os.path.exists("youtube_audio"):
-            os.mkdir("youtube_audio")
-        ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'youtube_audio/audio',
-        }
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            ydl.download([yt_url])
-        yt_audio_path = "youtube_audio/audio.wav"
-        command = f"demucs --two-stems=vocals {yt_audio_path}"
-        result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-        print(result.stdout.decode())
-        return ("separated/htdemucs/audio/vocals.wav", "separated/htdemucs/audio/no_vocals.wav", yt_audio_path, "separated/htdemucs/audio/vocals.wav")
-
-def combine_vocal_and_inst(audio_data, audio_volume):
-    print(audio_data)
-    if not os.path.exists("result"):
-        os.mkdir("result")
-    vocal_path = "result/output.wav"
-    inst_path = "separated/htdemucs/audio/no_vocals.wav"
-    output_path = "result/combine.mp3"
-    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)
-    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(device)
-    if 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__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--api', action="store_true", default=False)
-    parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
-    args, unknown = parser.parse_known_args()
-    load_hubert()
-    models = []
-    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/model_info.json", "r", encoding="utf-8") as f:
-        models_info = json.load(f)
-    for name, info in models_info.items():
-        if not info['enable']:
-            continue
-        title = info['title']
-        author = info.get("author", None)
-        cover = f"weights/{name}/{info['cover']}"
-        index = f"weights/{name}/{info['feature_retrieval_library']}"
-        npy = f"weights/{name}/{info['feature_file']}"
-        cpt = torch.load(f"weights/{name}/{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=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-        del net_g.enc_q
-        print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
-        net_g.eval().to(device)
-        if is_half:
-            net_g = net_g.half()
-        else:
-            net_g = net_g.float()
-        vc = VC(tgt_sr, device, is_half)
-        models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
-    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> More feature will be added soon... \n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hx6kKvIuv5XNY1Gai2PEuZhpO5z6xpVh?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)"
-        )
-        with gr.Tabs():
-            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_youtube = gr.Textbox(label="Youtube URL")
-                            vc_convert = gr.Button("Convert", 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():
-                            vc_input = gr.Textbox(label="Input audio path")
-                            vc_upload = gr.Audio(label="Upload audio file", visible=False, interactive=True)
-                            upload_mode = gr.Checkbox(label="Upload mode", value=False)
-                            vc_transpose = gr.Number(label="Transpose", value=0)
-                            vc_f0method = gr.Radio(
-                                label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
-                                choices=["pm", "harvest"],
-                                value="pm",
-                                interactive=True,
-                            )
-                            vc_index_ratio = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label="Retrieval feature ratio",
-                                value=0.6,
-                                interactive=True,
-                            )
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
-                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                            vc_output1 = gr.Textbox(label="Output Message")
-                            vc_output2 = gr.Audio(label="Output Audio")
-                            vc_submit = gr.Button("Generate", variant="primary")
-                        with gr.Column():
-                            vc_volume = gr.Slider(
-                                minimum=0,
-                                maximum=10,
-                                label="Vocal volume",
-                                value=4,
-                                interactive=True,
-                                step=1
-                            )
-                            vc_outputCombine = 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_output1, vc_output2])
-                vc_convert.click(cut_vocal_and_inst, vc_youtube, [vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input])
-                vc_combine.click(combine_vocal_and_inst, [vc_output2, vc_volume], vc_outputCombine)
-                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=args.api).launch(share=args.colab)