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import gc | |
import hashlib | |
import os | |
import shlex | |
import subprocess | |
import librosa | |
import torch | |
import numpy as np | |
import soundfile as sf | |
import gradio as gr | |
from rvc import Config, load_hubert, get_vc, rvc_infer | |
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
RVC_MODELS_DIR = os.path.join(BASE_DIR, 'rvc_models') | |
OUTPUT_DIR = os.path.join(BASE_DIR, 'song_output') | |
def get_rvc_model(voice_model): | |
model_dir = os.path.join(RVC_MODELS_DIR, voice_model) | |
rvc_model_path = next((os.path.join(model_dir, f) for f in os.listdir(model_dir) if f.endswith('.pth')), None) | |
rvc_index_path = next((os.path.join(model_dir, f) for f in os.listdir(model_dir) if f.endswith('.index')), None) | |
if rvc_model_path is None: | |
raise FileNotFoundError(f'There is no model file in the {model_dir} directory.') | |
return rvc_model_path, rvc_index_path | |
def convert_to_stereo(audio_path): | |
wave, sr = librosa.load(audio_path, mono=False, sr=44100) | |
if type(wave[0]) != np.ndarray: | |
stereo_path = 'Voice_stereo.wav' | |
command = shlex.split(f'ffmpeg -y -loglevel error -i "{audio_path}" -ac 2 -f wav "{stereo_path}"') | |
subprocess.run(command) | |
return stereo_path | |
return audio_path | |
def get_hash(filepath): | |
file_hash = hashlib.blake2b() | |
with open(filepath, 'rb') as f: | |
while chunk := f.read(8192): | |
file_hash.update(chunk) | |
return file_hash.hexdigest()[:11] | |
def display_progress(percent, message, progress=gr.Progress()): | |
progress(percent, desc=message) | |
def voice_change(voice_model, vocals_path, output_path, pitch_change, f0_method, index_rate, filter_radius, rms_mix_rate, protect, crepe_hop_length): | |
rvc_model_path, rvc_index_path = get_rvc_model(voice_model) | |
device = 'cpu' | |
config = Config(device, True) | |
hubert_model = load_hubert(device, config.is_half, os.path.join(RVC_MODELS_DIR, 'hubert_base.pt')) | |
cpt, version, net_g, tgt_sr, vc = get_vc(device, config.is_half, config, rvc_model_path) | |
rvc_infer(rvc_index_path, index_rate, vocals_path, output_path, pitch_change, f0_method, cpt, version, net_g, | |
filter_radius, tgt_sr, rms_mix_rate, protect, crepe_hop_length, vc, hubert_model) | |
del hubert_model, cpt, net_g, vc | |
gc.collect() | |
torch.cuda.empty_cache() | |
def song_cover_pipeline(uploaded_file, voice_model, pitch_change, index_rate=0.5, filter_radius=3, rms_mix_rate=0.25, f0_method='rmvpe', | |
crepe_hop_length=128, protect=0.33, output_format='mp3', progress=gr.Progress()): | |
if not uploaded_file or not voice_model: | |
raise ValueError('Make sure that the song input field and voice model field are filled in.') | |
display_progress(0, '[~] Starting the AI cover generation pipeline...', progress) | |
if not os.path.exists(uploaded_file): | |
raise FileNotFoundError(f'{uploaded_file} does not exist.') | |
song_id = get_hash(uploaded_file) | |
song_dir = os.path.join(OUTPUT_DIR, song_id) | |
os.makedirs(song_dir, exist_ok=True) | |
orig_song_path = convert_to_stereo(uploaded_file) | |
ai_cover_path = os.path.join(song_dir, f'Converted_Voice.{output_format}') | |
if os.path.exists(ai_cover_path): | |
os.remove(ai_cover_path) | |
display_progress(0.5, '[~] Converting vocals...', progress) | |
voice_change(voice_model, orig_song_path, ai_cover_path, pitch_change, f0_method, index_rate, | |
filter_radius, rms_mix_rate, protect, crepe_hop_length) | |
return ai_cover_path |