Merge pull request #103 from luoye2333/4.0

Added the TTS function

webUI.py

@@ -3,12 +3,18 @@ import os
 
 # os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
 import gradio as gr
+import gradio.processing_utils as gr_pu
 import librosa
 import numpy as np
 import soundfile
 from inference.infer_tool import Svc
 import logging
-import torch
+
+import subprocess
+import edge_tts
+import asyncio
+from scipy.io import wavfile
+import librosa
 
 logging.getLogger('numba').setLevel(logging.WARNING)
 logging.getLogger('markdown_it').setLevel(logging.WARNING)
@@ -18,10 +24,6 @@ logging.getLogger('multipart').setLevel(logging.WARNING)
 
 model = None
 spk = None
-cuda = []
-if torch.cuda.is_available():
-    for i in range(torch.cuda.device_count()):
-        cuda.append("cuda:{}".format(i))
 
 def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num):
     global model
@@ -36,13 +38,56 @@ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise
         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")
+        soundfile.write(temp_path, audio, model.target_sample, format="wav")
         _audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,pad_seconds,cl_num,lg_num,lgr_num)
         model.clear_empty()
         os.remove(temp_path)
         return "Success", (model.target_sample, _audio)
     except Exception as e:
         return "异常信息:"+str(e)+"\n请排障后重试",None
+    
+def tts_func(_text,_rate):
+    #使用edge-tts把文字转成音频
+    # voice = "zh-CN-XiaoyiNeural"#女性，较高音
+    voice = "zh-CN-YunxiNeural"#男性
+    output_file = _text[0:10]+".wav"
+    # communicate = edge_tts.Communicate(_text, voice)
+    # await communicate.save(output_file)
+    if _rate>=0:
+        ratestr="+{:.0%}".format(_rate)
+    elif _rate<0:
+        ratestr="{:.0%}".format(_rate)#减号自带
+
+    p=subprocess.Popen(["edge-tts",
+                        "--text",_text,
+                        "--write-media",output_file,
+                        "--voice",voice,
+                        "--rate="+ratestr]
+                        ,shell=True,
+                        stdout=subprocess.PIPE,
+                        stdin=subprocess.PIPE)
+    p.wait() 
+    return output_file
+
+def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate):
+    #使用edge-tts把文字转成音频
+    output_file=tts_func(text2tts,tts_rate)
+
+    #调整采样率
+    sr2=44100
+    wav, sr = librosa.load(output_file)
+    wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2)
+    save_path2= text2tts[0:10]+"_44k"+".wav"
+    wavfile.write(save_path2,sr2,
+                (wav2 * np.iinfo(np.int16).max).astype(np.int16)
+                )
+    
+    #读取音频
+    sample_rate, data=gr_pu.audio_from_file(save_path2)
+    vc_input=(sample_rate, data)
+
+    a,b=vc_fn(sid, vc_input, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num)
+    return a,b
 
 app = gr.Blocks()
 with app:
@@ -64,13 +109,17 @@ with app:
                 <font size=3>下面是聚类模型文件选择，没有可以不填：</font>
                 """)
             cluster_model_path = gr.File(label="聚类模型文件")
-            device = gr.Dropdown(label="推理设备，默认为自动选择cpu和gpu",choices=["Auto",*cuda,"cpu"],value="Auto")
+            device = gr.Dropdown(label="推理设备，留白则为自动选择cpu和gpu",choices=[None,"cuda","cpu"],value=None)
             gr.Markdown(value="""
                 <font size=3>全部上传完毕后(全部文件模块显示download),点击模型解析进行解析：</font>
                 """)
             model_analysis_button = gr.Button(value="模型解析")
             sid = gr.Dropdown(label="音色（说话人）")
             sid_output = gr.Textbox(label="Output Message")
+
+            text2tts=gr.Textbox(label="在此输入要转译的文字")
+            tts_rate = gr.Number(label="tts语速", value=0)
+
             vc_input3 = gr.Audio(label="上传音频")
             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="聚类模型混合比例，0-1之间，默认为0不启用聚类，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)
@@ -81,19 +130,26 @@ with app:
             pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
             lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0)
             lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75,interactive=True)
-            vc_submit = gr.Button("转换", variant="primary")
+            vc_submit = gr.Button("音频直接转换", variant="primary")
+            vc_submit2 = gr.Button("文字转音频+转换", variant="primary")
             vc_output1 = gr.Textbox(label="Output Message")
             vc_output2 = gr.Audio(label="Output Audio")
             def modelAnalysis(model_path,config_path,cluster_model_path,device):
-                try:
+                global model
-                    global model
+                debug=False
-                    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 "")
+                if debug:
+                    model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
                     spks = list(model.spk2id.keys())
-                    device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
+                    return sid.update(choices = spks,value=spks[0]),"ok"
-                    return sid.update(choices = spks,value=spks[0]),"ok,模型被加载到了设备{}之上".format(device_name)
+                else:
-                except Exception as e:
+                    try:
-                    return "","异常信息:"+str(e)+"\n请排障后重试"
+                        model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
+                        spks = list(model.spk2id.keys())
+                        return sid.update(choices = spks,value=spks[0]),"ok"
+                    except Exception as e:
+                        return "","异常信息:"+str(e)+"\n请排障后重试"
         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], [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], [vc_output1, vc_output2])
         model_analysis_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device],[sid,sid_output])
     app.launch()