func -> call

rvc/wrapper/api/endpoints/inference.py

@@ -11,6 +11,7 @@ from base64 import b64encode
 from rvc.modules.vc.modules import VC
 import glob
 import os
+import torch
 
 router = APIRouter()
 from dotenv import load_dotenv
@@ -43,7 +44,6 @@ 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(
@@ -61,6 +61,10 @@ 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

@@ -1,18 +1,32 @@
-from fastapi import APIRouter, Response, UploadFile, responses
+from io import BytesIO
 
+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, 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"},
-    )
+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"),
+            }
+        )

rvc/wrapper/cli/handler/infer.py

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

rvc/wrapper/cli/handler/uvr5.py

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