import sys,os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from vits.models import SynthesizerInfer from omegaconf import OmegaConf import torchcrepe import torch import io import os import gradio as gr import librosa import numpy as np import soundfile import logging logging.getLogger('numba').setLevel(logging.WARNING) logging.getLogger('markdown_it').setLevel(logging.WARNING) logging.getLogger('urllib3').setLevel(logging.WARNING) logging.getLogger('matplotlib').setLevel(logging.WARNING) def load_svc_model(checkpoint_path, model): assert os.path.isfile(checkpoint_path) checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") saved_state_dict = checkpoint_dict["model_g"] state_dict = model.state_dict() new_state_dict = {} for k, v in state_dict.items(): new_state_dict[k] = saved_state_dict[k] model.load_state_dict(new_state_dict) return model def compute_f0_nn(filename, device): audio, sr = librosa.load(filename, sr=16000) assert sr == 16000 # Load audio audio = torch.tensor(np.copy(audio))[None] # Here we'll use a 20 millisecond hop length hop_length = 320 # Provide a sensible frequency range for your domain (upper limit is 2006 Hz) # This would be a reasonable range for speech fmin = 50 fmax = 1000 # Select a model capacity--one of "tiny" or "full" model = "full" # Pick a batch size that doesn't cause memory errors on your gpu batch_size = 512 # Compute pitch using first gpu pitch, periodicity = torchcrepe.predict( audio, sr, hop_length, fmin, fmax, model, batch_size=batch_size, device=device, return_periodicity=True, ) pitch = np.repeat(pitch, 2, -1) # 320 -> 160 * 2 periodicity = np.repeat(periodicity, 2, -1) # 320 -> 160 * 2 # CREPE was not trained on silent audio. some error on silent need filter. periodicity = torchcrepe.filter.median(periodicity, 9) pitch = torchcrepe.filter.mean(pitch, 3) pitch[periodicity < 0.1] = 0 pitch = pitch.squeeze(0) return pitch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") hp = OmegaConf.load("configs/base.yaml") model = SynthesizerInfer( hp.data.filter_length // 2 + 1, hp.data.segment_size // hp.data.hop_length, hp) load_svc_model("vits_pretrain/sovits5.0_bigvgan_mix.pth", model) model.eval() model.to(device) def svc_change(argswave, argsspk): argsppg = "svc_tmp.ppg.npy" os.system(f"python whisper/inference.py -w {argswave} -p {argsppg}") argsvec = "svc_tmp.vec.npy" os.system(f"python hubert/inference.py -w {argswave} -v {argsvec}") spk = np.load(argsspk) spk = torch.FloatTensor(spk) ppg = np.load(argsppg) ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2 ppg = torch.FloatTensor(ppg) vec = np.load(argsvec) vec = np.repeat(vec, 2, 0) # 320 PPG -> 160 * 2 vec = torch.FloatTensor(vec) pit = compute_f0_nn(argswave, device) pit = torch.FloatTensor(pit) len_pit = pit.size()[0] len_vec = vec.size()[0] len_ppg = ppg.size()[0] len_min = min(len_pit, len_vec) len_min = min(len_min, len_ppg) pit = pit[:len_min] vec = vec[:len_min, :] ppg = ppg[:len_min, :] with torch.no_grad(): spk = spk.unsqueeze(0).to(device) source = pit.unsqueeze(0).to(device) source = model.pitch2source(source) hop_size = hp.data.hop_length all_frame = len_min hop_frame = 10 out_chunk = 2500 # 25 S out_index = 0 out_audio = [] has_audio = False while (out_index + out_chunk < all_frame): has_audio = True if (out_index == 0): # start frame cut_s = out_index cut_s_out = 0 else: cut_s = out_index - hop_frame cut_s_out = hop_frame * hop_size if (out_index + out_chunk + hop_frame > all_frame): # end frame cut_e = out_index + out_chunk cut_e_out = 0 else: cut_e = out_index + out_chunk + hop_frame cut_e_out = -1 * hop_frame * hop_size sub_ppg = ppg[cut_s:cut_e, :].unsqueeze(0).to(device) sub_vec = vec[cut_s:cut_e, :].unsqueeze(0).to(device) sub_pit = pit[cut_s:cut_e].unsqueeze(0).to(device) sub_len = torch.LongTensor([cut_e - cut_s]).to(device) sub_har = source[:, :, cut_s * hop_size:cut_e * hop_size].to(device) sub_out = model.inference(sub_ppg, sub_vec, sub_pit, spk, sub_len, sub_har) sub_out = sub_out[0, 0].data.cpu().detach().numpy() sub_out = sub_out[cut_s_out:cut_e_out] out_audio.extend(sub_out) out_index = out_index + out_chunk if (out_index < all_frame): if (has_audio): cut_s = out_index - hop_frame cut_s_out = hop_frame * hop_size else: cut_s = 0 cut_s_out = 0 sub_ppg = ppg[cut_s:, :].unsqueeze(0).to(device) sub_vec = vec[cut_s:, :].unsqueeze(0).to(device) sub_pit = pit[cut_s:].unsqueeze(0).to(device) sub_len = torch.LongTensor([all_frame - cut_s]).to(device) sub_har = source[:, :, cut_s * hop_size:].to(device) sub_out = model.inference(sub_ppg, sub_vec, sub_pit, spk, sub_len, sub_har) sub_out = sub_out[0, 0].data.cpu().detach().numpy() sub_out = sub_out[cut_s_out:] out_audio.extend(sub_out) out_audio = np.asarray(out_audio) return out_audio def svc_main(sid, input_audio): if input_audio is None: return "You need to upload an audio", None sampling_rate, audio = input_audio audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) if len(audio.shape) > 1: audio = librosa.to_mono(audio.transpose(1, 0)) if sampling_rate != 16000: audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) if (len(audio) > 16000*100): audio = audio[:16000*100] wav_path = "temp.wav" soundfile.write(wav_path, audio, 16000, format="wav") out_audio = svc_change(wav_path, f"configs/singers/singer00{sid}.npy") return "Success", (32000, out_audio) app = gr.Blocks() with app: with gr.Tabs(): with gr.TabItem("sovits 5.0"): gr.Markdown(value=""" 最终版本，基于开源数据:Multi-Singer https://github.com/Multi-Singer/Multi-Singer.github.io mix_encoder: whisper + hubert, 提升跨语言能力和纯对白语音训练的效果 [轻度伴奏可以无需去伴奏]就能直接进行歌声转换的SVC库 """) sid = gr.Dropdown(label="音色", choices=[ "22", "33", "47", "51"], value="47") vc_input3 = gr.Audio(label="上传音频") vc_submit = gr.Button("转换", variant="primary") vc_output1 = gr.Textbox(label="状态信息") vc_output2 = gr.Audio(label="转换音频") vc_submit.click(svc_main, [sid, vc_input3], [vc_output1, vc_output2]) app.launch()