replace warn (#1255)

infer-web.py

@@ -389,14 +389,14 @@ def get_pretrained_models(path_str, f0_str, sr2):
         "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
     )
     if not if_pretrained_generator_exist:
-        logger.warn(
+        logger.warning(
             "assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
             path_str,
             f0_str,
             sr2,
         )
     if not if_pretrained_discriminator_exist:
-        logger.warn(
+        logger.warning(
             "assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
             path_str,
             f0_str,

infer/lib/train/data_utils.py

@@ -113,7 +113,7 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
             try:
                 spec = torch.load(spec_filename)
             except:
-                logger.warn("%s %s", spec_filename, traceback.format_exc())
+                logger.warning("%s %s", spec_filename, traceback.format_exc())
                 spec = spectrogram_torch(
                     audio_norm,
                     self.filter_length,
@@ -305,7 +305,7 @@ class TextAudioLoader(torch.utils.data.Dataset):
             try:
                 spec = torch.load(spec_filename)
             except:
-                logger.warn("%s %s", spec_filename, traceback.format_exc())
+                logger.warning("%s %s", spec_filename, traceback.format_exc())
                 spec = spectrogram_torch(
                     audio_norm,
                     self.filter_length,

infer/lib/train/utils.py

@@ -33,7 +33,7 @@ def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
             try:
                 new_state_dict[k] = saved_state_dict[k]
                 if saved_state_dict[k].shape != state_dict[k].shape:
-                    logger.warn(
+                    logger.warning(
                         "shape-%s-mismatch. need: %s, get: %s",
                         k,
                         state_dict[k].shape,
@@ -111,7 +111,7 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
         try:
             new_state_dict[k] = saved_state_dict[k]
             if saved_state_dict[k].shape != state_dict[k].shape:
-                logger.warn(
+                logger.warning(
                     "shape-%s-mismatch|need-%s|get-%s",
                     k,
                     state_dict[k].shape,
@@ -409,7 +409,7 @@ def get_hparams_from_file(config_path):
 def check_git_hash(model_dir):
     source_dir = os.path.dirname(os.path.realpath(__file__))
     if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
+        logger.warning(
             "{} is not a git repository, therefore hash value comparison will be ignored.".format(
                 source_dir
             )
@@ -422,7 +422,7 @@ def check_git_hash(model_dir):
     if os.path.exists(path):
         saved_hash = open(path).read()
         if saved_hash != cur_hash:
-            logger.warn(
+            logger.warning(
                 "git hash values are different. {}(saved) != {}(current)".format(
                     saved_hash[:8], cur_hash[:8]
                 )

infer/modules/train/train.py

@@ -99,7 +99,7 @@ def main():
         n_gpus = 1
     if n_gpus < 1:
         # patch to unblock people without gpus. there is probably a better way.
-        logger.warn("NO GPU DETECTED: falling back to CPU - this may take a while")
+        logger.warning("NO GPU DETECTED: falling back to CPU - this may take a while")
         n_gpus = 1
     os.environ["MASTER_ADDR"] = "localhost"
     os.environ["MASTER_PORT"] = str(randint(20000, 55555))

infer/modules/vc/modules.py

@@ -224,7 +224,7 @@ class VC:
             )
         except:
             info = traceback.format_exc()
-            logger.warn(info)
+            logger.warning(info)
             return info, (None, None)
 
     def vc_multi(

modules.py

@@ -0,0 +1,307 @@
+import traceback
+import logging
+
+logger = logging.getLogger(__name__)
+
+import numpy as np
+import soundfile as sf
+import torch
+from io import BytesIO
+
+from infer.lib.audio import load_audio, wav2
+from infer.lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from infer.modules.vc.pipeline import Pipeline
+from infer.modules.vc.utils import *
+
+
+class VC:
+    def __init__(self, config):
+        self.n_spk = None
+        self.tgt_sr = None
+        self.net_g = None
+        self.pipeline = None
+        self.cpt = None
+        self.version = None
+        self.if_f0 = None
+        self.version = None
+        self.hubert_model = None
+
+        self.config = config
+
+    def get_vc(self, sid, *to_return_protect):
+        logger.info("Get sid: " + sid)
+
+        to_return_protect0 = {
+            "visible": self.if_f0 != 0,
+            "value": to_return_protect[0]
+            if self.if_f0 != 0 and to_return_protect
+            else 0.5,
+            "__type__": "update",
+        }
+        to_return_protect1 = {
+            "visible": self.if_f0 != 0,
+            "value": to_return_protect[1]
+            if self.if_f0 != 0 and to_return_protect
+            else 0.33,
+            "__type__": "update",
+        }
+
+        if sid == "" or sid == []:
+            if self.hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+                logger.info("Clean model cache")
+                del (
+                    self.net_g,
+                    self.n_spk,
+                    self.vc,
+                    self.hubert_model,
+                    self.tgt_sr,
+                )  # ,cpt
+                self.hubert_model = (
+                    self.net_g
+                ) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+                ###楼下不这么折腾清理不干净
+                self.if_f0 = self.cpt.get("f0", 1)
+                self.version = self.cpt.get("version", "v1")
+                if self.version == "v1":
+                    if self.if_f0 == 1:
+                        self.net_g = SynthesizerTrnMs256NSFsid(
+                            *self.cpt["config"], is_half=self.config.is_half
+                        )
+                    else:
+                        self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
+                elif self.version == "v2":
+                    if self.if_f0 == 1:
+                        self.net_g = SynthesizerTrnMs768NSFsid(
+                            *self.cpt["config"], is_half=self.config.is_half
+                        )
+                    else:
+                        self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
+                del self.net_g, self.cpt
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+            return (
+                {"visible": False, "__type__": "update"},
+                {
+                    "visible": True,
+                    "value": to_return_protect0,
+                    "__type__": "update",
+                },
+                {
+                    "visible": True,
+                    "value": to_return_protect1,
+                    "__type__": "update",
+                },
+                "",
+                "",
+            )
+        person = f'{os.getenv("weight_root")}/{sid}'
+        logger.info(f"Loading: {person}")
+
+        self.cpt = torch.load(person, map_location="cpu")
+        self.tgt_sr = self.cpt["config"][-1]
+        self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+        self.if_f0 = self.cpt.get("f0", 1)
+        self.version = self.cpt.get("version", "v1")
+
+        synthesizer_class = {
+            ("v1", 1): SynthesizerTrnMs256NSFsid,
+            ("v1", 0): SynthesizerTrnMs256NSFsid_nono,
+            ("v2", 1): SynthesizerTrnMs768NSFsid,
+            ("v2", 0): SynthesizerTrnMs768NSFsid_nono,
+        }
+
+        self.net_g = synthesizer_class.get(
+            (self.version, self.if_f0), SynthesizerTrnMs256NSFsid
+        )(*self.cpt["config"], is_half=self.config.is_half)
+
+        del self.net_g.enc_q
+
+        self.net_g.load_state_dict(self.cpt["weight"], strict=False)
+        self.net_g.eval().to(self.config.device)
+        if self.config.is_half:
+            self.net_g = self.net_g.half()
+        else:
+            self.net_g = self.net_g.float()
+
+        self.pipeline = Pipeline(self.tgt_sr, self.config)
+        n_spk = self.cpt["config"][-3]
+        index = {"value": get_index_path_from_model(sid), "__type__": "update"}
+        logger.info("Select index: " + index["value"])
+
+        return (
+            (
+                {"visible": True, "maximum": n_spk, "__type__": "update"},
+                to_return_protect0,
+                to_return_protect1,
+                index,
+                index,
+            )
+            if to_return_protect
+            else {"visible": True, "maximum": n_spk, "__type__": "update"}
+        )
+
+    def vc_single(
+        self,
+        sid,
+        input_audio_path,
+        f0_up_key,
+        f0_file,
+        f0_method,
+        file_index,
+        file_index2,
+        index_rate,
+        filter_radius,
+        resample_sr,
+        rms_mix_rate,
+        protect,
+    ):
+        if input_audio_path is None:
+            return "You need to upload an audio", None
+        f0_up_key = int(f0_up_key)
+        try:
+            audio = load_audio(input_audio_path, 16000)
+            audio_max = np.abs(audio).max() / 0.95
+            if audio_max > 1:
+                audio /= audio_max
+            times = [0, 0, 0]
+
+            if self.hubert_model is None:
+                self.hubert_model = load_hubert(self.config)
+
+            file_index = (
+                (
+                    file_index.strip(" ")
+                    .strip('"')
+                    .strip("\n")
+                    .strip('"')
+                    .strip(" ")
+                    .replace("trained", "added")
+                )
+                if file_index != ""
+                else file_index2
+            )  # 防止小白写错，自动帮他替换掉
+
+            audio_opt = self.pipeline.pipeline(
+                self.hubert_model,
+                self.net_g,
+                sid,
+                audio,
+                input_audio_path,
+                times,
+                f0_up_key,
+                f0_method,
+                file_index,
+                index_rate,
+                self.if_f0,
+                filter_radius,
+                self.tgt_sr,
+                resample_sr,
+                rms_mix_rate,
+                self.version,
+                protect,
+                f0_file,
+            )
+            if self.tgt_sr != resample_sr >= 16000:
+                tgt_sr = resample_sr
+            else:
+                tgt_sr = self.tgt_sr
+            index_info = (
+                "Index:\n%s." % file_index
+                if os.path.exists(file_index)
+                else "Index not used."
+            )
+            return (
+                "Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
+                % (index_info, *times),
+                (tgt_sr, audio_opt),
+            )
+        except:
+            info = traceback.format_exc()
+            logger.warning(info)
+            return info, (None, None)
+
+    def vc_multi(
+        self,
+        sid,
+        dir_path,
+        opt_root,
+        paths,
+        f0_up_key,
+        f0_method,
+        file_index,
+        file_index2,
+        index_rate,
+        filter_radius,
+        resample_sr,
+        rms_mix_rate,
+        protect,
+        format1,
+    ):
+        try:
+            dir_path = (
+                dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+            )  # 防止小白拷路径头尾带了空格和"和回车
+            opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+            os.makedirs(opt_root, exist_ok=True)
+            try:
+                if dir_path != "":
+                    paths = [
+                        os.path.join(dir_path, name) for name in os.listdir(dir_path)
+                    ]
+                else:
+                    paths = [path.name for path in paths]
+            except:
+                traceback.print_exc()
+                paths = [path.name for path in paths]
+            infos = []
+            for path in paths:
+                info, opt = self.vc_single(
+                    sid,
+                    path,
+                    f0_up_key,
+                    None,
+                    f0_method,
+                    file_index,
+                    file_index2,
+                    # file_big_npy,
+                    index_rate,
+                    filter_radius,
+                    resample_sr,
+                    rms_mix_rate,
+                    protect,
+                )
+                if "Success" in info:
+                    try:
+                        tgt_sr, audio_opt = opt
+                        if format1 in ["wav", "flac"]:
+                            sf.write(
+                                "%s/%s.%s"
+                                % (opt_root, os.path.basename(path), format1),
+                                audio_opt,
+                                tgt_sr,
+                            )
+                        else:
+                            path = "%s/%s.%s" % (
+                                opt_root,
+                                os.path.basename(path),
+                                format1,
+                            )
+                            with BytesIO() as wavf:
+                                sf.write(wavf, audio_opt, tgt_sr, format="wav")
+                                wavf.seek(0, 0)
+                                with open(path, "wb") as outf:
+                                    wav2(wavf, outf, format1)
+                    except:
+                        info += traceback.format_exc()
+                infos.append("%s->%s" % (os.path.basename(path), info))
+                yield "\n".join(infos)
+            yield "\n".join(infos)
+        except:
+            yield traceback.format_exc()

tools/infer/train-index-v2.py

@@ -46,7 +46,7 @@ if big_npy.shape[0] > 2e5:
         )
     except:
         info = traceback.format_exc()
-        logger.warn(info)
+        logger.warning(info)
 
 np.save("tools/infer/big_src_feature_mi.npy", big_npy)
 

tools/rvc_for_realtime.py

@@ -140,7 +140,7 @@ class RVC:
             if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
                 self.model_rmvpe = last_rvc.model_rmvpe
         except:
-            logger.warn(traceback.format_exc())
+            logger.warning(traceback.format_exc())
 
     def change_key(self, new_key):
         self.f0_up_key = new_key
@@ -326,10 +326,10 @@ class RVC:
                     + (1 - self.index_rate) * feats[0][-leng_replace_head:]
                 )
             else:
-                logger.warn("Index search FAILED or disabled")
+                logger.warning("Index search FAILED or disabled")
         except:
             traceback.print_exc()
-            logger.warn("Index search FAILED")
+            logger.warning("Index search FAILED")
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         t3 = ttime()
         if self.if_f0 == 1: