修复非流式长文本推理的bug

webui.py

@@ -167,25 +167,69 @@ def generate_audio(tts_text, mode_checkbox_group, sft_dropdown, prompt_text, pro
     if mode_checkbox_group == '预训练音色':
         logging.info('get sft inference request')
         set_all_random_seed(seed)
-        for i in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed,new_dropdown=new_dropdown):
-            yield (target_sr, i['tts_speech'].numpy().flatten())
+
+        if stream:
+
+            for i in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed,new_dropdown=new_dropdown):
+                yield (target_sr, i['tts_speech'].numpy().flatten())
+        else:
+
+            tts_speeches = []
+            for i in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed,new_dropdown=new_dropdown):
+                tts_speeches.append(i['tts_speech'])
+            audio_data = torch.concat(tts_speeches, dim=1)
+            yield (target_sr, audio_data.numpy().flatten())
+
+
     elif mode_checkbox_group == '3s极速复刻':
         logging.info('get zero_shot inference request')
         prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
         set_all_random_seed(seed)
-        for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed):
-            yield (target_sr, i['tts_speech'].numpy().flatten())
+
+        if stream:
+
+            for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed):
+                yield (target_sr, i['tts_speech'].numpy().flatten())
+        else:
+
+            tts_speeches = []
+            for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed):
+                tts_speeches.append(i['tts_speech'])
+            audio_data = torch.concat(tts_speeches, dim=1)
+            yield (target_sr, audio_data.numpy().flatten())
+
     elif mode_checkbox_group == '跨语种复刻':
         logging.info('get cross_lingual inference request')
         prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
         set_all_random_seed(seed)
-        for i in cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k, stream=stream, speed=speed):
-            yield (target_sr, i['tts_speech'].numpy().flatten())
+
+        if stream:
+
+            for i in cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k, stream=stream, speed=speed):
+                yield (target_sr, i['tts_speech'].numpy().flatten())
+        else:
+
+            tts_speeches = []
+            for i in cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k, stream=stream, speed=speed):
+                tts_speeches.append(i['tts_speech'])
+            audio_data = torch.concat(tts_speeches, dim=1)
+            yield (target_sr, audio_data.numpy().flatten())
+
     else:
         logging.info('get instruct inference request')
         set_all_random_seed(seed)
-        for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed,new_dropdown=new_dropdown):
-            yield (target_sr, i['tts_speech'].numpy().flatten())
+        if stream:
+
+            for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed,new_dropdown=new_dropdown):
+                yield (target_sr, i['tts_speech'].numpy().flatten())
+        else:
+
+            tts_speeches = []
+            for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed,new_dropdown=new_dropdown):
+                tts_speeches.append(i['tts_speech'])
+            audio_data = torch.concat(tts_speeches, dim=1)
+            yield (target_sr, audio_data.numpy().flatten())
+
 
 
 def main():