infer_new.py

import torch, os, traceback, sys, warnings, shutil, numpy as np
import gradio as gr
import librosa
import asyncio
import rarfile
import edge_tts
import yt_dlp
import ffmpeg
import gdown
import subprocess
import wave
import soundfile as sf
from scipy.io import wavfile
from datetime import datetime
from urllib.parse import urlparse
from mega import Mega

import base64
import tempfile
import os

from pydub import AudioSegment





now_dir = os.getcwd()
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
split_model="htdemucs"
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from fairseq import checkpoint_utils
from vc_infer_pipeline import VC
from config import Config
config = Config()

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]

hubert_model = None

f0method_mode = ["pm", "harvest", "crepe"]
f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"

if os.path.isfile("rmvpe.pt"):
    f0method_mode.insert(2, "rmvpe")
    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)"

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()

load_hubert()

weight_root = "weights"
index_root = "weights/index"
weights_model = []
weights_index = []
for _, _, model_files in os.walk(weight_root):
    for file in model_files:
        if file.endswith(".pth"):
            weights_model.append(file)
for _, _, index_files in os.walk(index_root):
    for file in index_files:
        if file.endswith('.index') and "trained" not in file:
            weights_index.append(os.path.join(index_root, file))

def check_models():
    weights_model = []
    weights_index = []
    for _, _, model_files in os.walk(weight_root):
        for file in model_files:
            if file.endswith(".pth"):
                weights_model.append(file)
    for _, _, index_files in os.walk(index_root):
        for file in index_files:
            if file.endswith('.index') and "trained" not in file:
                weights_index.append(os.path.join(index_root, file))
    return (
        gr.Dropdown.update(choices=sorted(weights_model), value=weights_model[0]),
        gr.Dropdown.update(choices=sorted(weights_index))
    )

def clean():
    return (
        gr.Dropdown.update(value=""),
        gr.Slider.update(visible=False)
    )

def get_file_base_name(file_path):
    # Extract the base name (including extension)
    base_name = os.path.basename(file_path)
    # Split the base name into the name and extension, and return just the name
    file_name_without_extension, _ = os.path.splitext(base_name)
    return file_name_without_extension


def api_convert_voice(spk_id,voice_transform,input_audio_path):
    
    #split audio
    base_name = get_file_base_name(input_audio_path)
    cut_vocal_and_inst(input_audio_path,spk_id)
    print("audio splitting performed")
    #vocal_path = f"output/{split_model}/{spk_id}_input_audio/vocals.wav"
    #inst = f"output/{split_model}/{spk_id}_input_audio/no_vocals.wav"
    
    vocal_path = f"output/{split_model}/{base_name}/vocals.wav"
    inst = f"output/{split_model}/{base_name}/no_vocals.wav"

    output_path = convert_voice(spk_id, vocal_path, voice_transform)
    output_path1= combine_vocal_and_inst(output_path,inst)
    print(output_path1)
    return output_path1    
    
    





def convert_voice(spk_id, input_audio_path, voice_transform):
    get_vc(spk_id,0.5)
    output_audio_path = vc_single(
        sid=0,
        input_audio_path=input_audio_path,
        f0_up_key=voice_transform,  # Assuming voice_transform corresponds to f0_up_key
        f0_file=None ,
        f0_method="rmvpe",
        file_index=spk_id,  # Assuming file_index_path corresponds to file_index
        index_rate=0.75,
        filter_radius=3,
        resample_sr=0,
        rms_mix_rate=0.25,
        protect=0.33  # Adjusted from protect_rate to protect to match the function signature
    )
    print(output_audio_path)
    return output_audio_path


def vc_single(
    sid,
    input_audio_path,    
    f0_up_key,
    f0_file,
    f0_method,
    file_index,
    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, cpt
    
    try:
        logs = []
        print(f"Converting...")
        
        audio, sr = librosa.load(input_audio_path, sr=16000, mono=True)
        print(f"found audio ")
        f0_up_key = int(f0_up_key)
        times = [0, 0, 0]
        if hubert_model == None:
            load_hubert()
        print("loaded hubert")
        if_f0 = 1
        audio_opt = vc.pipeline(
            hubert_model,
            net_g,
            0,
            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."
        )
        print("writing to FS")
        output_file_path = os.path.join("output", f"converted_audio_{sid}.wav")  # Adjust path as needed
        
        os.makedirs(os.path.dirname(output_file_path), exist_ok=True)  # Create the output directory if it doesn't exist
        print("create dir")
        # Save the audio file using the target sampling rate
        sf.write(output_file_path, audio_opt, tgt_sr)
        
        print("wrote to FS")

        # Return the path to the saved file along with any other information
        
        return output_file_path
           
            
    except:
        info = traceback.format_exc()
        
        return info, (None, None)

def get_vc(sid, to_return_protect0):
    global n_spk, tgt_sr, net_g, vc, cpt, version, weights_index
    if sid == "" or sid == []:
        global hubert_model
        if hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
            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()
            ###楼下不这么折腾清理不干净
            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 (
            gr.Slider.update(maximum=2333, visible=False),
            gr.Slider.update(visible=True),
            gr.Dropdown.update(choices=sorted(weights_index), value=""),
            gr.Markdown.update(value="# <center> No model selected")
        )
    print(f"Loading {sid} model...")
    selected_model = sid[:-4]
    cpt = torch.load(os.path.join(weight_root, sid), map_location="cpu")
    tgt_sr = cpt["config"][-1]
    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
    if_f0 = cpt.get("f0", 1)
    if if_f0 == 0:
        to_return_protect0 = {
            "visible": False,
            "value": 0.5,
            "__type__": "update",
        }
    else:
        to_return_protect0 = {
            "visible": True,
            "value": to_return_protect0,
            "__type__": "update",
        }
    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]
    weights_index = []
    for _, _, index_files in os.walk(index_root):
        for file in index_files:
            if file.endswith('.index') and "trained" not in file:
                weights_index.append(os.path.join(index_root, file))
    if weights_index == []:
        selected_index = gr.Dropdown.update(value="")
    else:
        selected_index = gr.Dropdown.update(value=weights_index[0])
    for index, model_index in enumerate(weights_index):
        if selected_model in model_index:
            selected_index = gr.Dropdown.update(value=weights_index[index])
            break
    return (
        gr.Slider.update(maximum=n_spk, visible=True),
        to_return_protect0,
        selected_index,
        gr.Markdown.update(
            f'## <center> {selected_model}\n'+
            f'### <center> RVC {version} Model'
        )
    )






def cut_vocal_and_inst(audio_path,spk_id):
    
    #vocal_path = "output/result/audio.wav"
    os.makedirs("output/result", exist_ok=True)
    #wavfile.write(vocal_path, audio_data[0], audio_data[1])
    #logs.append("Starting the audio splitting process...")
    #yield "\n".join(logs), None, None
    print("before executing splitter")
    command = f"demucs --two-stems=vocals -n {split_model} {audio_path} -o output"
    #result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
    result = subprocess.run(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    if result.returncode != 0:
        print("Demucs process failed:", result.stderr)
    else:
        print("Demucs process completed successfully.")
    print("after executing splitter")
    #for line in result.stdout:
    #    logs.append(line)
    #    yield "\n".join(logs), None, None
    
    print(result.stdout)
    vocal = f"output/{split_model}/{spk_id}_input_audio/vocals.wav"
    inst = f"output/{split_model}/{spk_id}_input_audio/no_vocals.wav"
    #logs.append("Audio splitting complete.")


def combine_vocal_and_inst(vocal_path, inst_path):
    
    vocal_volume=1
    inst_volume=1
    os.makedirs("output/result", exist_ok=True)
    # Assuming vocal_path and inst_path are now directly passed as arguments
    output_path = "output/result/combine.mp3"
    #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}"'
    #command=f'ffmpeg -y -i "{inst_path}" -i "{vocal_path}" -filter_complex "amix=inputs=2:duration=longest" -b:a 320k -c:a libmp3lame "{output_path}"'
    # Load the audio files
    vocal = AudioSegment.from_file(vocal_path)
    instrumental = AudioSegment.from_file(inst_path)

# Overlay the vocal track on top of the instrumental track
    combined = vocal.overlay(instrumental)

# Export the result
    combined.export(output_path, format="mp3")

    #result = subprocess.run(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    return output_path
    
#def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume):
#    os.makedirs("output/result", exist_ok=True)
 ##  output_path = "output/result/combine.mp3"
   # inst_path = f"output/{split_model}/audio/no_vocals.wav"
    #wavfile.write(vocal_path, audio_data[0], audio_data[1])
    #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

if __name__ == '__main__':
    app.run(debug=False, port=5000,host='0.0.0.0')