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/') 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="#
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'##
{selected_model}\n'+ f'###
RVC {version} Model' ) ) if __name__ == '__main__': app.run(debug=False, port=5000,host='0.0.0.0')