myinfer_latest.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
from flask import Flask, request, jsonify, send_file,session,render_template
import base64
import tempfile
import threading
import hashlib
import os
import werkzeug
from pydub import AudioSegment
import uuid
from threading import Semaphore
from threading import Lock
from multiprocessing import Process, Queue, set_start_method





app = Flask(__name__)
app.secret_key = 'smjain_6789' 
now_dir = os.getcwd()
cpt={}
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"
convert_voice_lock = Lock()
# Define the maximum number of concurrent requests
MAX_CONCURRENT_REQUESTS = 2  # Adjust this number as needed

# Initialize the semaphore with the maximum number of concurrent requests
request_semaphore = Semaphore(MAX_CONCURRENT_REQUESTS)

set_start_method('spawn', force=True)
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)"

def hash_array(array):
    # Ensure the array is in a consistent byte format
    array_bytes = array.tobytes()
    # Create a hash object (using SHA256 for example)
    hash_obj = hashlib.sha256(array_bytes)
    # Get the hexadecimal digest of the array
    hash_hex = hash_obj.hexdigest()
    return hash_hex

def hash_array1(arr):
    arr_str = np.array2string(arr)
    return hashlib.md5(arr_str.encode()).hexdigest()

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)
    )
# Function to delete files
def cleanup_files(file_paths):
    for path in file_paths:
        try:
            os.remove(path)
            print(f"Deleted {path}")
        except Exception as e:
            print(f"Error deleting {path}: {e}")

processed_audio_storage = {}
@app.route('/convert_voice', methods=['POST'])
def api_convert_voice():
    acquired = request_semaphore.acquire(blocking=False)
    if not acquired:
        return jsonify({"error": "Too many requests, please try again later"}), 429
    try:
    
        #if session.get('submitted'):
        #    return jsonify({"error": "Form already submitted"}), 400

        # Process the form here...
        # Set the flag indicating the form has been submitted
        #session['submitted'] = True
        print(request.form)
        print(request.files)
        spk_id = request.form['spk_id']+'.pth'
        voice_transform = request.form['voice_transform']

        # The file part
        if 'file' not in request.files:
            return jsonify({"error": "No file part"}), 400
        file = request.files['file']
        if file.filename == '':
            return jsonify({"error": "No selected file"}), 400
        #created_files = []
        # Save the file to a temporary path
        unique_id = str(uuid.uuid4())
        print(unique_id)
        
        filename = werkzeug.utils.secure_filename(file.filename)
        input_audio_path = os.path.join(tmp, f"{spk_id}_input_audio_{unique_id}.{filename.split('.')[-1]}")
        file.save(input_audio_path)
        
        #created_files.append(input_audio_path)
        
        #split audio
        cut_vocal_and_inst(input_audio_path,spk_id,unique_id)
        print("audio splitting performed")
        vocal_path = f"output/{spk_id}_{unique_id}/{split_model}/{spk_id}_input_audio_{unique_id}/vocals.wav"
        inst = f"output/{spk_id}_{unique_id}/{split_model}/{spk_id}_input_audio_{unique_id}/no_vocals.wav"
        print("*****before making call to convert ", unique_id)
        output_queue = Queue()
    
    # Create and start the process
        p = Process(target=worker, args=(spk_id, vocal_path, voice_transform, unique_id, output_queue))
        p.start()
    
    # Wait for the process to finish and get the result
        p.join()
        output_path = output_queue.get()
        print("output path of converted voice",output_path)
        #output_path = convert_voice(spk_id, vocal_path, voice_transform,unique_id)
        output_path1= combine_vocal_and_inst(output_path,inst,unique_id)
        
        processed_audio_storage[unique_id] = output_path1
        session['processed_audio_id'] = unique_id 
        
        print(output_path1)
        
        #created_files.extend([vocal_path, inst, output_path])
        return jsonify({"message": "File processed successfully", "audio_id": unique_id}), 200
    finally:
        request_semaphore.release()
    #if os.path.exists(output_path1):
        
    #    return send_file(output_path1, as_attachment=True)
    #else:
    #    return jsonify({"error": "File not found."}), 404

def convert_voice_thread_safe(spk_id, vocal_path, voice_transform, unique_id):
    with convert_voice_lock:
        return convert_voice(spk_id, vocal_path, voice_transform, unique_id)



def get_vc_safe(sid, to_return_protect0):
    with convert_voice_lock:
        return get_vc(sid, to_return_protect0)

@app.route('/')
def upload_form():
    return render_template('ui.html')

@app.route('/get_processed_audio/<audio_id>')
def get_processed_audio(audio_id):
    # Retrieve the path from temporary storage or session
    if audio_id in processed_audio_storage:
        file_path = processed_audio_storage[audio_id]
        return send_file(file_path, as_attachment=True)
    return jsonify({"error": "File not found."}), 404

def worker(spk_id, input_audio_path, voice_transform, unique_id, output_queue):
    """
    Worker function to be executed in a separate process.
    """
    # Call the convert_voice function
    output_audio_path = convert_voice(spk_id, input_audio_path, voice_transform, unique_id)
    print("output in worker for audio file",output_audio_path)
    # Put the result in the queue to be retrieved by the main process
    output_queue.put(output_audio_path)
def convert_voice(spk_id, input_audio_path, voice_transform,unique_id):
    get_vc(spk_id,0.5)
    print("*****before makinf call to vc ", unique_id)

   
    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,
        unique_id=unique_id
    )
    print(output_audio_path)
    return output_audio_path

def cut_vocal_and_inst(audio_path,spk_id,unique_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/{spk_id}_{unique_id}"
    env = os.environ.copy()

# Add or modify the environment variable for this subprocess
    env["CUDA_VISIBLE_DEVICES"] = "0"
    
    
    
    #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, output_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 = f"output/result/{output_path}.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
    print(vocal_path)
    print(inst_path)
    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 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,
    unique_id
):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
    global tgt_sr, net_g, vc, hubert_model, version, cpt
    print("***** in vc ", unique_id)

    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
        )
        hash_val = hash_array1(audio_opt)
    
    # Get the current thread's name or ID
        thread_name = threading.current_thread().name
        print(f"Thread {thread_name}: Hash {hash_val}")
    
        
        sample_and_print(audio_opt,thread_name)
    # Print the hash and thread information
        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."
        )
        save_audio_with_thread_id(audio_opt,tgt_sr)
        print("writing to FS")
        #output_file_path = os.path.join("output", f"converted_audio_{sid}.wav")  # Adjust path as needed
        # Assuming 'unique_id' is passed to convert_voice function along with 'sid'
        print("***** before writing to file outout ", unique_id)
        output_file_path = os.path.join("output", f"converted_audio_{sid}_{unique_id}.wav")  # Adjust path as needed

        print("******* output file path ",output_file_path)
        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 save_audio_with_thread_id(audio_opt, tgt_sr,output_dir="output"):
    # Ensure the output directory exists
    os.makedirs(output_dir, exist_ok=True)
    
    # Get the current thread ID or name
    thread_id = threading.current_thread().name
    
    # Construct the filename using the thread ID
    filename = f"audio_{thread_id}.wav"
    output_path = os.path.join(output_dir, filename)
    
    # Assuming the target sample rate is defined elsewhere; replace as necessary
    #tgt_sr = 16000  # Example sample rate, adjust according to your needs
    
    # Write the audio file
    sf.write(output_path, audio_opt, tgt_sr)
    
    print(f"Saved {output_path}")

def sample_and_print(array, thread_name):
    # Ensure the array has more than 10 elements; otherwise, use the array length
    num_samples = 10 if len(array) > 10 else len(array)
    
    # Calculate indices to sample; spread them evenly across the array
    indices = np.linspace(0, len(array) - 1, num=num_samples, dtype=int)
    
    # Sample elements
    sampled_elements = array[indices]
    
    # Print sampled elements with thread ID
    print(f"Thread {thread_name}: Sampled Elements: {sampled_elements}")


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[sid].get("f0", 1)
            version = cpt[sid].get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(
                        *cpt[sid]["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt[sid]["config"])
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(
                        *cpt[sid]["config"], is_half=config.is_half
                    )
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt[sid]["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[sid] = torch.load(os.path.join(weight_root, sid), map_location="cpu")
    tgt_sr = cpt[sid]["config"][-1]
    cpt[sid]["config"][-3] = cpt[sid]["weight"]["emb_g.weight"].shape[0]
    if_f0 = cpt[sid].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[sid].get("version", "v1")
    if version == "v1":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs256NSFsid(*cpt[sid]["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt[sid]["config"])
    elif version == "v2":
        if if_f0 == 1:
            net_g = SynthesizerTrnMs768NSFsid(*cpt[sid]["config"], is_half=config.is_half)
        else:
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt[sid]["config"])
    del net_g.enc_q
    print(net_g.load_state_dict(cpt[sid]["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[sid]["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'
        )
    )



    

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