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, SimpleQueue, set_start_method,get_context from queue import Empty from pydub import AudioSegment from flask_dance.contrib.google import make_google_blueprint, google import io from space import ensure_model_in_weights_dir,upload_to_do import boto3 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() #concurrent= os.getenv('concurrent', '') # Define the maximum number of concurrent requests MAX_CONCURRENT_REQUESTS=10 # Initialize the semaphore with the maximum number of concurrent requests request_semaphore = Semaphore(MAX_CONCURRENT_REQUESTS) task_status_tracker = {} os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1" # ONLY FOR TESTING, REMOVE IN PRODUCTION os.environ["OAUTHLIB_RELAX_TOKEN_SCOPE"] = "1" app.config["GOOGLE_OAUTH_CLIENT_ID"] = "144930881143-n3e3ubers3vkq7jc9doe4iirasgimdt2.apps.googleusercontent.com" app.config["GOOGLE_OAUTH_CLIENT_SECRET"] = "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" google_blueprint = make_google_blueprint(scope=["profile", "email"]) app.register_blueprint(google_blueprint, url_prefix="/login") ACCESS_ID = os.getenv('ACCESS_ID', '') SECRET_KEY = os.getenv('SECRET_KEY', '') #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)" @app.route("/") def index(): # Check if user is logged in return render_template("ui.html") #if google.authorized: # return render_template("index.html", logged_in=True) #else: # return render_template("index.html", logged_in=False) 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}") @app.route("/create_song") def create_song(): if not google.authorized: return redirect(url_for("google.login")) resp = google.get("/oauth2/v2/userinfo") assert resp.ok, resp.text email = resp.json()["email"] user_info = resp.json() user_id = user_info.get("id") name = user_info.get("name") #if not user_exists(email): # user_data = {'user_id': user_id, 'user_name': name, 'email': email, 'model_created': 'No', 'time_used': '0','model_id':''} # add_user(user_data) #models = get_user_models(email) # Assuming we're interested in whether any model has been created #model_exists = len(models) > 0 return render_template("ui.html", email=email) @app.route('/download/', methods=['GET']) def download_file(filename): # Configure the client with your credentials session = boto3.session.Session() client = session.client('s3', region_name='nyc3', endpoint_url='https://nyc3.digitaloceanspaces.com', aws_access_key_id=ACCESS_ID, aws_secret_access_key=SECRET_KEY) # Define the bucket and object key bucket_name = 'sing' # Your bucket name object_key = f'{filename}' # Construct the object key # Define the local path to save the file local_file_path = os.path.join('weights', filename) # Download the file from the bucket try: client.download_file(bucket_name, object_key, local_file_path) except client.exceptions.NoSuchKey: return jsonify({'error': 'File not found in the bucket'}), 404 except Exception as e: return jsonify({'error': str(e)}), 500 # Optional: Send the file directly to the client # return send_file(local_file_path, as_attachment=True) return jsonify({'success': True, 'message': 'File downloaded successfully', 'file_path': local_file_path}) @app.route('/list-weights', methods=['GET']) def list_weights(): directory = 'weights' files = os.listdir(directory) email = request.args.get('email', default='') if not email: return jsonify({"error": "Email parameter is required"}), 400 list_models(email) # Extract filenames without their extensions filenames = [os.path.splitext(file)[0] for file in files if os.path.isfile(os.path.join(directory, file))] return jsonify(filenames) @app.route("/logout") def logout(): # Clear the session session.clear() #if "google_oauth_token" in session: # del session["google_oauth_token"] return redirect(url_for("index")) @app.route('/status/', methods=['GET']) def get_status(audio_id): # Retrieve the task status using the unique ID print(audio_id) status_info = task_status_tracker.get(audio_id, {"status": "Unknown ID", "percentage": 0}) return jsonify({"audio_id": audio_id, "status": status_info["status"], "percentage": status_info["percentage"]}) 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 #task_status_tracker[unique_id] = {"status": "Starting", "percentage": 0} 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) print("accessing spk_id") spk_id = request.form['spk_id']+'.pth' print("speaker id path=",spk_id) voice_transform = request.form['voice_transform'] print("before file access") # 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 if file.content_length > 10 * 1024 * 1024: return jsonify({"error": "File size exceeds 6 MB"}), 400 print("after file access") print("check if model is there in weights dir or not") filename_without_extension = os.path.splitext(file.filename)[0] unique_id = filename_without_extension ensure_model_in_weights_dir(spk_id) print("checking done for the model") content_type_format_map = { 'audio/mpeg': 'mp3', 'audio/wav': 'wav', 'audio/x-wav': 'wav', 'audio/mp4': 'mp4', 'audio/x-m4a': 'mp4', } # Default to 'mp3' if content type is unknown (or adjust as needed) audio_format = content_type_format_map.get(file.content_type, 'mp3') # Convert the uploaded file to an audio segment audio = AudioSegment.from_file(io.BytesIO(file.read()), format=audio_format) #audio = AudioSegment.from_file(io.BytesIO(file.read()), format="mp3") # Adjust format as necessary file.seek(0) # Reset file pointer after reading # Calculate audio length in minutes audio_length_minutes = len(audio) / 60000.0 # pydub returns length in milliseconds if audio_length_minutes > 5: return jsonify({"error": "Audio length exceeds 5 minutes"}), 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 task_status_tracker[unique_id] = {"status": "Processing: Step 1", "percentage": 30} 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) #task_status_tracker[unique_id] = "Processing: Step 2" #output_queue = SimpleQueue() ctx = get_context('spawn') output_queue = ctx.Queue() # Create and start the process p = ctx.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() print("*******waiting for process to complete ") output_path = output_queue.get() task_status_tracker[unique_id] = {"status": "Processing: Step 2", "percentage": 80} #if isinstance(output_path, Exception): # print("Exception in worker:", output_path) #else: # 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 task_status_tracker[unique_id] = {"status": "Finalizing", "percentage": 100} print(output_path1) #created_files.extend([vocal_path, inst, output_path]) task_status_tracker[unique_id]["status"] = "Completed" upload_to_do(output_path1) print("file uploaded to Digital ocean space") 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): try: output_audio_path = convert_voice(spk_id, input_audio_path, voice_transform, unique_id) print("output in worker for audio file", output_audio_path) output_queue.put(output_audio_path) print("added to output queue") except Exception as e: print("exception in adding to queue") output_queue.put(e) # Send the exception to the main process for debugging 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 ) # Get the current thread's name or ID 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 # 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 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')