diff --git a/webUI.py b/webUI.py index e3a8cad..4f3ee9f 100644 --- a/webUI.py +++ b/webUI.py @@ -15,6 +15,7 @@ import numpy as np import soundfile import torch from scipy.io import wavfile +from pathlib import Path from compress_model import removeOptimizer from inference.infer_tool import Svc @@ -81,7 +82,6 @@ def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_ device = cuda[device] if "CUDA" in device else device cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster" fr = ".pkl" in cluster_filepath[1] - #model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance) model = Svc(model_path.name, config_path.name, device=device if device != "Auto" else None, @@ -127,24 +127,30 @@ def modelUnload(): torch.cuda.empty_cache() return sid.update(choices = [],value=""),"模型卸载完毕!" -def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment): +def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment): global model try: if input_audio is None: return "You need to upload an audio", None if model is None: return "You need to upload an model", None - print(input_audio) - sampling_rate, audio = input_audio - print(audio.shape,sampling_rate) - audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) - print(audio.dtype) + if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False: + if cluster_ratio != 0: + return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None + #print(input_audio) + audio, sampling_rate = soundfile.read(input_audio) + #print(audio.shape,sampling_rate) + if np.issubdtype(audio.dtype, np.integer): + audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) + #print(audio.dtype) if len(audio.shape) > 1: audio = librosa.to_mono(audio.transpose(1, 0)) - temp_path = "temp.wav" - soundfile.write(temp_path, audio, sampling_rate, format="wav") + # 未知原因Gradio上传的filepath会有一个奇怪的固定后缀，这里去掉 + truncated_basename = Path(input_audio).stem[:-6] + processed_audio = os.path.join("raw", f"{truncated_basename}.wav") + soundfile.write(processed_audio, audio, sampling_rate, format="wav") _audio = model.slice_inference( - temp_path, + processed_audio, sid, vc_transform, slice_db, @@ -164,13 +170,19 @@ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise loudness_envelope_adjustment ) model.clear_empty() - os.remove(temp_path) + #os.remove(temp_path) #构建保存文件的路径，并保存到results文件夹内 timestamp = str(int(time.time())) if not os.path.exists("results"): os.makedirs("results") - output_file = os.path.join("results", sid + "_" + timestamp + ".wav") - soundfile.write(output_file, _audio, model.target_sample, format="wav") + key = "auto" if auto_f0 else f"{int(vc_transform)}key" + cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_" + isdiffusion = "sovits" + if model.shallow_diffusion : isdiffusion = "sovdiff" + if model.only_diffusion : isdiffusion = "diff" + output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}' + output_file = os.path.join("results", output_file_name) + soundfile.write(output_file, _audio, model.target_sample, format=output_format) return "Success", output_file except Exception as e: if debug: @@ -291,6 +303,7 @@ with gr.Blocks( vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0) cluster_ratio = gr.Number(label="聚类模型/特征检索混合比例，0-1之间，0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0) slice_db = gr.Number(label="切片阈值", value=-40) + output_format = gr.Radio(label="音频输出格式", choices=["wav", "flac", "mp3"], value = "wav") noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4) k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000) with gr.Column(): @@ -305,7 +318,7 @@ with gr.Blocks( use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False) with gr.Tabs(): with gr.TabItem("音频转音频"): - vc_input3 = gr.Audio(label="选择音频") + vc_input3 = gr.Audio(label="选择音频", type="filepath") vc_submit = gr.Button("音频转换", variant="primary") with gr.TabItem("文字转音频"): text2tts=gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪") @@ -371,7 +384,7 @@ with gr.Blocks( WebUI设置 """) debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug) - vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2]) + vc_submit.click(vc_fn, [sid, vc_input3, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2]) vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2]) debug_button.change(debug_change,[],[]) model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix],[sid,sid_output])