Revert "func -> call"

This reverts commit 043fa4d.

rvc/modules/uvr5/modules.py

@@ -20,7 +20,7 @@ def __init__(self):
         self.need_reformat: bool = True
         self.config: Config = Config()
 
-    def __call__(
+    def uvr_wrapper(
         self,
         audio_path: Path,
         agg: int = 10,

rvc/wrapper/api/endpoints/inference.py

@@ -11,7 +11,6 @@
 from rvc.modules.vc.modules import VC
 import glob
 import os
-import torch
 
 router = APIRouter()
 from dotenv import load_dotenv
@@ -44,6 +43,7 @@ def inference(
     rms_mix_rate: float = 0.25,
     protect: float = 0.33,
 ):
+    print(res_type)
     vc = VC()
     vc.get_vc(modelpath)
     tgt_sr, audio_opt, times, _ = vc.vc_inference(
@@ -61,10 +61,6 @@ def inference(
     )
     wavfile.write(wv := BytesIO(), tgt_sr, audio_opt)
     print(times)
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
     if res_type == "blob":
         return responses.StreamingResponse(
             wv,

rvc/wrapper/api/endpoints/uvr.py

@@ -1,32 +1,18 @@
-from io import BytesIO
+from fastapi import APIRouter, Response, UploadFile, responses
 
-from fastapi import APIRouter, UploadFile, responses, Query
-from fastapi.responses import JSONResponse
 from rvc.modules.uvr5.modules import UVR
-from base64 import b64encode
-from scipy.io import wavfile
 
 router = APIRouter()
 
 
 @router.post("/inference")
-def uvr(
-    inputpath,
-    outputpath,
-    modelname,
-    res_type: str = Query("blob", enum=["blob", "json"]),
-):
-    arries = [i for i in UVR()(inputpath, outputpath, model_name=modelname)]
-    wavfile.write(wv := BytesIO(), tgt_sr, audio_opt)
-    if res_type == "blob":
-        return responses.StreamingResponse(
-            wv,
-            media_type="audio/wav",
-            headers={"Content-Disposition": "attachment; filename=inference.wav"},
-        )
-    else:
-        return JSONResponse(
-            {
-                "audio": b64encode(wv.read()).decode("utf-8"),
-            }
-        )
+def uvr(inputpath, outputpath, modelname, format):
+    uvr_module = UVR()
+    uvr_module.uvr_wrapper(
+        inputpath, outputpath, model_name=modelname, export_format=format
+    )
+    return responses.StreamingResponse(
+        audio,
+        media_type="audio/wav",
+        headers={"Content-Disposition": "attachment; filename=inference.wav"},
+    )

rvc/wrapper/cli/handler/infer.py

@@ -4,7 +4,6 @@
 import click
 from dotenv import load_dotenv
 from scipy.io import wavfile
-import torch
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 
@@ -130,6 +129,4 @@ def infer(
         wavfile.write(outputpath, tgt_sr, audio_opt)
         click.echo(times)
         click.echo(f"Finish inference. Check {outputpath}")
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
+    return tgt_sr, audio_opt, times

rvc/wrapper/cli/handler/uvr5.py

@@ -38,5 +38,8 @@
     help="output Format",
 )
 def uvr(modelname, inputpath, outputpath, format):
-    UVR()(inputpath, outputpath, model_name=modelname, export_format=format)
+    uvr_module = UVR()
+    uvr_module.uvr_wrapper(
+        inputpath, outputpath, model_name=modelname, export_format=format
+    )
     click.echo(f"Finish uvr5. Check {outputpath}")