Add MD

app.py

@@ -1,9 +1,83 @@
 import os
 import io
 import gradio as gr
+import librosa
+import numpy as np
+import logging
+import soundfile
+import asyncio
+import argparse
+import gradio.processing_utils as gr_processing_utils
+logging.getLogger('numba').setLevel(logging.WARNING)
+logging.getLogger('markdown_it').setLevel(logging.WARNING)
+logging.getLogger('urllib3').setLevel(logging.WARNING)
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
 
-def greet(name):
-    return "Hello " + name + "!!"
+limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+audio_postprocess_ori = gr.Audio.postprocess
+
+def audio_postprocess(self, y):
+    data = audio_postprocess_ori(self, y)
+    if data is None:
+        return None
+    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
+
+gr.Audio.postprocess = audio_postprocess
+def create_vc_fn(model, sid):
+    def vc_fn(input_audio, vc_transform, auto_f0):
+        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 > 20 and limitation:
+            return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", 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)
+        raw_path = io.BytesIO()
+        soundfile.write(raw_path, audio, 16000, format="wav")
+        raw_path.seek(0)
+        out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
+                                       auto_predict_f0=auto_f0,
+                                       )
+        return "Success", (44100, out_audio.cpu().numpy())
+    return vc_fn
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument('--api', action="store_true", default=False)
+    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
+    args = parser.parse_args()
+    models = []
+    voices = []
+    for f in os.listdir("models"):
+        name = f
+        # = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device)
+        #cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
+        #models.append((name, cover, create_vc_fn(model, name)))
+
+    # !svc infer {NAME}.wav -c config.json -m G_riri_220.pth
+    #  display(Audio(f"{NAME}.out.wav", autoplay=True))
+    with gr.Blocks() as app:
+        gr.Markdown(
+            "# <center> Sovits Chapay\n"
+            "## <center> The input audio should be clean and pure voice without background music.\n"
+        )
+        with gr.Tabs():
+            for (name, cover, vc_fn) in models:
+                with gr.TabItem(name):
+                    with gr.Row():
+                        with gr.Column():
+                            vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
+                            vc_transform = gr.Number(label="vc_transform", value=0)
+                            auto_f0 = gr.Checkbox(label="auto_f0", value=False)
+                            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_transform, auto_f0], [vc_output1, vc_output2])
+        app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)