Update app.py

app.py

@@ -3,46 +3,30 @@ import json
 import argparse
 import traceback
 import logging
+from datetime import datetime
+
 import gradio as gr
 import numpy as np
 import librosa
 import torch
-import asyncio
-import edge_tts
-from datetime import datetime
+
 from fairseq import checkpoint_utils
 from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
 from vc_infer_pipeline import VC
-from config import (
-    is_half,
-    device
-)
+from config import is_half, device
+
 logging.getLogger("numba").setLevel(logging.WARNING)
 
+
 def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
-    def vc_fn(
-        input_audio,
-        f0_up_key,
-        f0_method,
-        index_rate
-    ):
+    def vc_fn(vc_transpose, vc_f0method, vc_index_ratio):
         try:
-            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)
+            # Get the recorded audio from the microphone
+            audio, sr = vc_microphone.record(num_frames=16000)  # Adjust the sample rate if needed
+
+            # Your existing processing logic for audio
             times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
+            f0_up_key = int(vc_transpose)
             audio_opt = vc.pipeline(
                 hubert_model,
                 net_g,
@@ -50,12 +34,13 @@ def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
                 audio,
                 times,
                 f0_up_key,
-                f0_method,
+                vc_f0method,
                 file_index,
                 file_big_npy,
-                index_rate,
+                vc_index_ratio,
                 if_f0,
             )
+
             print(
                 f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
             )
@@ -64,8 +49,10 @@ def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
             info = traceback.format_exc()
             print(info)
             return info, (None, None)
+
     return vc_fn
 
+
 def load_hubert():
     global hubert_model
     models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
@@ -80,6 +67,7 @@ def load_hubert():
         hubert_model = hubert_model.float()
     hubert_model.eval()
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--api', action="store_true", default=False)
@@ -113,6 +101,7 @@ if __name__ == '__main__':
             net_g = net_g.float()
         vc = VC(tgt_sr, device, is_half)
         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"
@@ -125,16 +114,14 @@ if __name__ == '__main__':
                     with gr.Row():
                         gr.Markdown(
                             '<div align="center">'
-                            f'<div>{title}</div>\n'+
-                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
+                            f'<div>{title}</div>\n' +
+                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "") +
                             '</div>'
                         )
                     with gr.Row():
                         with gr.Column():
-                            if args.files:
-                                vc_input = gr.Textbox(label="Input audio path")
-                            else:
-                                vc_input = gr.Audio(label="Input audio")
+                            # Use microphone instead of file upload
+                            vc_microphone = gr.Microphone(label="Record your voice")
                             vc_transpose = gr.Number(label="Transpose", value=0)
                             vc_f0method = gr.Radio(
                                 label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
@@ -153,5 +140,6 @@ if __name__ == '__main__':
                         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], [vc_output1, vc_output2])
-        app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
+
+                vc_submit.click(vc_fn, [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)