Update app.py

app.py

@@ -24,34 +24,23 @@ def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
         input_audio,
         f0_up_key,
         f0_method,
-        index_rate,
-        tts_mode,
-        tts_text,
-        tts_voice
+        index_rate
     ):
         try:
-            if tts_mode:
-                if len(tts_text) > 1000000 :
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
+            if args.files:
+                audio, sr = librosa.load(input_audio, sr=16000, mono=True)
             else:
-                if args.files:
-                    audio, sr = librosa.load(input_audio, sr=16000, mono=True)
-                else:
-                    if input_audio is None:
-                        return "You need to upload an audio", None
-                    sampling_rate, audio = input_audio
-                    duration = audio.shape[0] / sampling_rate
-                    if duration > 10000000 :
-                        return "no", None
-                    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 input_audio is None:
+                    return "You need to upload an audio", None
+                sampling_rate, audio = input_audio
+                duration = audio.shape[0] / sampling_rate
+                if duration > 10000000:
+                    return "no", None
+                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)
             times = [0, 0, 0]
             f0_up_key = int(f0_up_key)
             audio_opt = vc.pipeline(
@@ -91,14 +80,6 @@ def load_hubert():
         hubert_model = hubert_model.float()
     hubert_model.eval()
 
-def change_to_tts_mode(tts_mode):
-    if tts_mode:
-        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-def save_audio(audio):
-    return audio
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--api', action="store_true", default=False)
@@ -107,35 +88,31 @@ if __name__ == '__main__':
     args, unknown = parser.parse_known_args()
     load_hubert()
     models = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
     with open("weights/model_info.json", "r", encoding="utf-8") as f:
         models_info = json.load(f)
     for name, info in models_info.items():
         if not info['enable']:
             continue
         title = info['title']
-        author = info.get("author", None)
         cover = f"weights/{name}/{info['cover']}"
         index = f"weights/{name}/{info['feature_retrieval_library']}"
         npy = f"weights/{name}/{info['feature_file']}"
         cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
         tgt_sr = cpt["config"][-1]
-        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
         if_f0 = cpt.get("f0", 1)
         if if_f0 == 1:
             net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
         else:
             net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
         del net_g.enc_q
-        print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
+        print(net_g.load_state_dict(cpt["weight"], strict=False))
         net_g.eval().to(device)
         if is_half:
             net_g = net_g.half()
         else:
             net_g = net_g.float()
         vc = VC(tgt_sr, device, is_half)
-        models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
+        models.append((name, title, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
     with gr.Blocks() as app:
         gr.Markdown(
             "# <center> RVC generator\n"
@@ -143,13 +120,12 @@ if __name__ == '__main__':
             "[![buymeacoffee](https://badgen.net/badge/icon/buymeacoffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/spark808)\n\n"
         )
         with gr.Tabs():
-            for (name, title, author, cover, vc_fn) in models:
+            for (name, title, cover, vc_fn) in models:
                 with gr.TabItem(name):
                     with gr.Row():
                         gr.Markdown(
                             '<div align="center">'
                             f'<div>{title}</div>\n'+
-                            (f'<div>Model author: {author}</div>' if author else "")+
                             (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
                             '</div>'
                         )
@@ -173,22 +149,9 @@ if __name__ == '__main__':
                                 value=0.6,
                                 interactive=True,
                             )
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False,label="TTS text (10000000 words limitation)" "TTS text")
-                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
                             vc_submit = gr.Button("Generate", variant="primary")
                         with gr.Column():
                             vc_output1 = gr.Textbox(label="Output Message")
                             vc_output2 = gr.Audio(label="Output Audio")
-                vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
-                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
-        app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
-iface = gr.Interface(
-    fn=save_audio, 
-    inputs=gr.inputs.Audio(source="microphone", type="file"), 
-    outputs="audio",
-    title="Voice Recorder",
-    description="Press the Record button to record your voice, and then press Stop when you're done.",
-)
-
-iface.launch()
+                vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio], [vc_output1, vc_output2])
+        app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)