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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 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
import io
import runpod
import boto3
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)
task_status_tracker = {}
os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1" # ONLY FOR TESTING, REMOVE IN PRODUCTION
os.environ["OAUTHLIB_RELAX_TOKEN_SCOPE"] = "1"
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)"
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}")
def upload_file(local_file_path,bucket_name):
# 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
filename = os.path.basename(local_file_path)
object_key = f'{filename}' # Construct the object key
# Define the local path to save the file
try:
response=client.upload_file(local_file_path, bucket_name, filename)
except client.exceptions.NoSuchKey:
return "error: File not found in the bucket"
except Exception as e:
return "error: File not found in the bucket"
# Optional: Send the file directly to the client
# return send_file(local_file_path, as_attachment=True)
return "success"
def download_file(filename,bucket_name):
# 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
object_key = f'{filename}' # Construct the object key
# Define the local path to save the file
local_file_path = os.path.join('downloads', filename)
# Check if the 'downloads' directory exists, create it if not
if not os.path.exists(os.path.dirname(local_file_path)):
os.makedirs(os.path.dirname(local_file_path))
# Download the file from the bucket
try:
client.download_file(bucket_name, object_key, local_file_path)
except client.exceptions.NoSuchKey:
return "file not in buecket"
except Exception as e:
return "exception"
# Optional: Send the file directly to the client
# return send_file(local_file_path, as_attachment=True)
return "success"
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 "status"
processed_audio_storage = {}
def api_convert_voice(filename,spk_id1,unique_id):
acquired = request_semaphore.acquire(blocking=False)
if not acquired:
return "error in lock"
#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
spk_id = spk_id1+'.pth'
print("speaker id path=",spk_id)
voice_transform = 0
local_file_path = os.path.join('downloads', filename)
# The file part
file_size = os.path.getsize(local_file_path)
if file_size > 10 * 1024 * 1024: # 10 MB limit
return json.dumps({"error": "File size exceeds 10 MB"}), 400
content_type_format_map = {
'.mp3': 'mp3',
'.wav': 'wav',
'.mp4': 'mp4',
'.m4a': 'mp4',
}
_, file_extension = os.path.splitext(local_file_path)
audio_format = content_type_format_map.get(file_extension.lower(), 'mp3')
# 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(local_file_path, format=audio_format)
# Calculate audio length in minutes
audio_length_minutes = len(audio) / 60000.0 # pydub returns length in milliseconds
if audio_length_minutes > 5:
return json.dumps({"error": "Audio length exceeds 5 minutes"}), 400
#created_files = []
# Save the file to a temporary path
#unique_id = str(uuid.uuid4())
print(unique_id)
base_filename = os.path.basename(local_file_path)
filename = werkzeug.utils.secure_filename(base_filename)
input_audio_path = os.path.join(tmp, f"{spk_id}_input_audio_{unique_id}.{filename.split('.')[-1]}")
#file.save(input_audio_path)
os.rename(local_file_path, 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
output_path=worker(spk_id, vocal_path, voice_transform, unique_id)
#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)
upload_file(output_path1,"sing")
print("file uploaded")
#created_files.extend([vocal_path, inst, output_path])
task_status_tracker[unique_id]["status"] = "Completed"
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)
def worker(spk_id, input_audio_path, voice_transform, unique_id):
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)
return output_audio_path
except Exception as e:
print("exception in adding to queue")
return "error in converting voice"
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="# <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'
)
)
def handler(job):
job_input = job["input"] # Access the input from the request.
filename=job_input["filename"]
spk_id=job_input["spk_id"]
unique_id=job_input["unique_id"]
download_file(filename,"sing")
api_convert_voice(filename,spk_id,unique_id)
# Add your custom code here.
return "Your job results"
runpod.serverless.start({"handler": handler}) # Required.
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