add support gpt-sovits

app.py

@@ -75,7 +75,7 @@ def xtts(text, speaker, language, server_url, stream_chunk_size) -> Iterator[byt
         return
 
     first = True
-    for chunk in res.iter_content(chunk_size=960):
+    for chunk in res.iter_content(chunk_size=960): #24K*20ms*2
         if first:
             end = time.perf_counter()
             print(f"xtts Time to first chunk: {end-start}s")
@@ -85,12 +85,20 @@ def xtts(text, speaker, language, server_url, stream_chunk_size) -> Iterator[byt
 
     print("xtts response.elapsed:", res.elapsed)
 
-def gpt_sovits(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
+def gpt_sovits(text, character, language, server_url, stream_chunk_size) -> Iterator[bytes]:
     start = time.perf_counter()
-    speaker["text"] = text
+    req={}
-    speaker["language"] = language
+    req["text"] = text
-    speaker["stream_chunk_size"] = stream_chunk_size  # you can reduce it to get faster response, but degrade quality
+    req["text_language"] = language
-    res = requests.get(f"{server_url}&text="+text,stream=True)
+    req["character"] = character
+    #req["emotion"] = emotion
+    #req["stream_chunk_size"] = stream_chunk_size  # you can reduce it to get faster response, but degrade quality
+    req["stream"] = True
+    res = requests.post(
+        f"{server_url}/tts",
+        json=req,
+        stream=True,
+    )
     end = time.perf_counter()
     print(f"gpt_sovits Time to make POST: {end-start}s")
 
@@ -99,7 +107,7 @@ def gpt_sovits(text, speaker, language, server_url, stream_chunk_size) -> Iterat
         return
 
     first = True
-    for chunk in res.iter_content(chunk_size=960):
+    for chunk in res.iter_content(chunk_size=1280): #32K*20ms*2
         if first:
             end = time.perf_counter()
             print(f"gpt_sovits Time to first chunk: {end-start}s")
@@ -109,7 +117,7 @@ def gpt_sovits(text, speaker, language, server_url, stream_chunk_size) -> Iterat
 
     print("gpt_sovits response.elapsed:", res.elapsed)
 
-def stream_xtts(audio_stream,render):
+def stream_tts(audio_stream,render):
     for chunk in audio_stream:
         if chunk is not None:
             render.push_audio(chunk)
@@ -121,19 +129,19 @@ def txt_to_audio(text_):
         t = time.time()
         asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
         print(f'-------edge tts time:{time.time()-t:.4f}s')
-    elif tts_type == "gpt": #gpt_sovits
+    elif tts_type == "gpt-sovits": #gpt_sovits
-        stream_xtts(
+        stream_tts(
             gpt_sovits(
                 text_,
-                gspeaker,
+                "test", #character
-                "zh-cn", #en args.language,
+                "zh", #en args.language,
-                "http://127.0.0.1:9880/tts_ava?ava=maimai&streaming_mode=true", #args.server_url,
+                "http://127.0.0.1:5000", #args.server_url,
                 "20" #args.stream_chunk_size
             ),
             nerfreal
         )
     else: #xtts
-        stream_xtts(
+        stream_tts(
             xtts(
                 text_,
                 gspeaker,
@@ -354,18 +362,18 @@ if __name__ == '__main__':
     parser.add_argument('--fullbody_offset_x', type=int, default=0)
     parser.add_argument('--fullbody_offset_y', type=int, default=0)
 
-    parser.add_argument('--tts', type=str, default='edgetts') #xtts
+    parser.add_argument('--tts', type=str, default='edgetts') #xtts gpt-sovits
     parser.add_argument('--ref_file', type=str, default=None)
-    parser.add_argument('--xtts_server', type=str, default='http://localhost:9000')
+    parser.add_argument('--tts_server', type=str, default='http://localhost:9000')
 
     opt = parser.parse_args()
     app.config.from_object(opt)
-    #print(app.config['xtts_server'])
+    #print(app.config['tts_server'])
 
     tts_type = opt.tts
     if tts_type == "xtts":
         print("Computing the latents for a new reference...")
-        gspeaker = get_speaker(opt.ref_file, opt.xtts_server)
+        gspeaker = get_speaker(opt.ref_file, opt.tts_server)
 
     # assert test mode
     opt.test = True

asrreal.py

@@ -314,10 +314,13 @@ class ASR:
 
     def push_audio(self,buffer): #push audio pcm from tts
         print(f'[INFO] push_audio {len(buffer)}')
-        if self.opt.tts == "xtts":
+        if self.opt.tts == "xtts" or self.opt.tts == "gpt-sovits":
             if len(buffer)>0:            
                 stream = np.frombuffer(buffer, dtype=np.int16).astype(np.float32) / 32767
-                stream = resampy.resample(x=stream, sr_orig=24000, sr_new=self.sample_rate)
+                if self.opt.tts == "xtts":
+                    stream = resampy.resample(x=stream, sr_orig=24000, sr_new=self.sample_rate)
+                else:
+                    stream = resampy.resample(x=stream, sr_orig=32000, sr_new=self.sample_rate)
                 #byte_stream=BytesIO(buffer)
                 #stream = self.__create_bytes_stream(byte_stream)
                 streamlen = stream.shape[0]

tts/README.md

@@ -1,27 +1,51 @@
-一、采用gpt-sovits方案，bert-sovits适合长音频训练，gpt-sovits运行短音频快速推理
+# 采用gpt-sovits方案，bert-sovits适合长音频训练，gpt-sovits运行短音频快速推理
-下载tts服务端代码
+## 部署tts推理
-https://github.com/yanyuxiyangzk/GPT-SoVITS/tree/fast_inference_
+git clone https://github.com/X-T-E-R/GPT-SoVITS-Inference.git
-api_v2.py即启动的服务端代码，也可以打开声音克隆界面进行训练，可以训练带感情语气等
+
-
+1. 安装依赖库
-1、启动
+```
-python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml 
+conda create -n GPTSoVits python=3.9
-
+conda activate GPTSoVits
-
+bash install.sh
-http://127.0.0.1:9880/set_sovits_weights?weights_path=SoVITS_weights/maimai_e55_s1210.pth
+```
-http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_weights/maimai-e21.ckpt
+从 [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) 下载预训练模型，并将它们放置在 `GPT_SoVITS\pretrained_models` 中
-
+
-
+2. Model Folder Format
-2、接口测试
+模型文件下载地址 https://www.yuque.com/xter/zibxlp/gsximn7ditzgispg  
-http://127.0.0.1:9880/set_ava?ava=maimai
+下载的模型文件放到trained目录下, 如 `trained/Character1/`  
-
+Put the pth / ckpt / wav files in it, the wav should be named as the prompt text  
-http://127.0.0.1:9880/tts_ava?ava=maimai&text=我是一个粉刷匠，粉刷本领强。我要把那新房子，刷得更漂亮。刷了房顶又刷墙，刷子像飞一样。哎呀我的小鼻子，变呀变了样
+Like :
-
+
-http://127.0.0.1:9880/tts_ava?ava=maimai&text=我是一个粉刷匠，粉刷本领强。我要把那新房子，刷得更漂亮。刷了房顶又刷墙，刷子像飞一样。哎呀我的小鼻子，变呀变了样&streaming_mode=true
+```
-
+trained
-http://127.0.0.1:9880/tts_ava?ava=maimai&text=我是一个粉刷匠，粉刷本领强。我要把那新房子，刷得更漂亮。刷了房顶又刷墙，刷子像飞一样。哎呀我的小鼻子，变呀变了样。&text_lang=zh&ref_audio_path=mengpai.wav&prompt_lang=zh&prompt_text=呜哇好生气啊！不要把我跟一斗相提并论！&text_split_method=cut5&batch_size=1&media_type=wav&streaming_mode=true
+--hutao
-
+----hutao-e75.ckpt
-3、使用
+----hutao_e60_s3360.pth
-设置角色
+----hutao said something.wav
-http://127.0.0.1:9880/set_ava?ava=maimai
+```
-tts接口
+
-http://127.0.0.1:9880/tts_ava?ava=maimai&text=我是一个粉刷匠，粉刷本领强。我要把那新房子，刷得更漂亮。刷了房顶又刷墙，刷子像飞一样。哎呀我的小鼻子，变呀变了样&streaming_mode=true
+3. 启动
+后端接口: python Inference/src/tts_backend.py  
+如果有错误提示找不到cmudict，从这下载https://github.com/nltk/nltk_data，将packages改名为nltk_data放到home目录下  
+管理页面: python Inference/src/TTS_Webui.py, 浏览器打开可以管理character和emotion
+
+
+4. 接口测试  
+  Character and Emotion List  
+To obtain the supported characters and their corresponding emotions, please visit the following URL:
+- URL: `http://127.0.0.1:5000/character_list`
+- Returns: A JSON format list of characters and corresponding emotions
+- Method: `GET`
+
+```
+{
+    "Hanabi": [
+        "default",
+        "Normal",
+        "Yandere",
+    ],
+    "Hutao": [
+        "default"
+    ]
+}
+```