chore(format): run black on dev (#2090)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

infer-web.py

@@ -847,10 +847,7 @@ with gr.Blocks(title="RVC WebUI") as app:
                                 value=0,
                             )
                             input_audio0 = gr.File(
-                                label=i18n(
-                                    "待处理音频文件"
-                                ),
-                                file_types=["audio"]
+                                label=i18n("待处理音频文件"), file_types=["audio"]
                             )
                             file_index2 = gr.Dropdown(
                                 label=i18n("自动检测index路径,下拉式选择(dropdown)"),
@@ -990,9 +987,7 @@ with gr.Blocks(title="RVC WebUI") as app:
                             interactive=True,
                         )
                         file_index3 = gr.File(
-                            label=i18n(
-                                "特征检索库文件路径,为空则使用下拉的选择结果"
-                            ),
+                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
                         )
 
                         refresh_button.click(
@@ -1099,7 +1094,14 @@ with gr.Blocks(title="RVC WebUI") as app:
                 sid0.change(
                     fn=vc.get_vc,
                     inputs=[sid0, protect0, protect1],
-                    outputs=[spk_item, protect0, protect1, file_index2, file_index4, modelinfo],
+                    outputs=[
+                        spk_item,
+                        protect0,
+                        protect1,
+                        file_index2,
+                        file_index4,
+                        modelinfo,
+                    ],
                     api_name="infer_change_voice",
                 )
         with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):

infer/lib/train/process_ckpt.py

@@ -10,6 +10,7 @@ from infer.modules.vc import model_hash_ckpt, hash_id
 
 i18n = I18nAuto()
 
+
 # add author sign
 def save_small_model(ckpt, sr, if_f0, name, epoch, version, hps):
     try:

infer/modules/vc/hash.py

@@ -7,6 +7,7 @@ from pybase16384 import encode_to_string, decode_from_string
 
 if __name__ == "__main__":
     import os, sys
+
     now_dir = os.getcwd()
     sys.path.append(now_dir)
 
@@ -17,6 +18,7 @@ from .utils import load_hubert
 
 from infer.lib.audio import load_audio
 
+
 class TorchSeedContext:
     def __init__(self, seed):
         self.seed = seed
@@ -29,27 +31,38 @@ class TorchSeedContext:
     def __exit__(self, type, value, traceback):
         torch.random.set_rng_state(self.state)
 
+
 half_hash_len = 512
 expand_factor = 65536 * 8
 
+
 @singleton_variable
 def original_audio_time_minus():
-    __original_audio = load_audio(str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000)
+    __original_audio = load_audio(
+        str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000
+    )
     np.divide(__original_audio, np.abs(__original_audio).max(), __original_audio)
     return -__original_audio
 
+
 @singleton_variable
 def original_audio_freq_minus():
-    __original_audio = load_audio(str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000)
+    __original_audio = load_audio(
+        str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000
+    )
     np.divide(__original_audio, np.abs(__original_audio).max(), __original_audio)
     __original_audio = fft(__original_audio)
     return -__original_audio
 
+
 def _cut_u16(n):
-    if n > 16384: n = 16384 + 16384*(1-np.exp((16384-n)/expand_factor))
-    elif n < -16384: n = -16384 - 16384*(1-np.exp((n+16384)/expand_factor))
+    if n > 16384:
+        n = 16384 + 16384 * (1 - np.exp((16384 - n) / expand_factor))
+    elif n < -16384:
+        n = -16384 - 16384 * (1 - np.exp((n + 16384) / expand_factor))
     return n
 
+
 # wave_hash will change time_field, use carefully
 def wave_hash(time_field):
     np.divide(time_field, np.abs(time_field).max(), time_field)
@@ -60,7 +73,7 @@ def wave_hash(time_field):
         raise Exception("freq not hashable")
     np.add(time_field, original_audio_time_minus(), out=time_field)
     np.add(freq_field, original_audio_freq_minus(), out=freq_field)
-    hash = np.zeros(half_hash_len//2*2, dtype='>i2')
+    hash = np.zeros(half_hash_len // 2 * 2, dtype=">i2")
     d = 375 * 512 // half_hash_len
     for i in range(half_hash_len // 4):
         a = i * 2
@@ -70,22 +83,43 @@ def wave_hash(time_field):
         s = np.average(freq_field[i * d : (i + 1) * d])
         hash[a] = np.int16(_cut_u16(round(32768 * np.real(s))))
         hash[b] = np.int16(_cut_u16(round(32768 * np.imag(s))))
-        hash[x] = np.int16(_cut_u16(round(32768*np.sum(time_field[i*d:i*d+d//2]))))
-        hash[y] = np.int16(_cut_u16(round(32768*np.sum(time_field[i*d+d//2:(i+1)*d]))))
+        hash[x] = np.int16(
+            _cut_u16(round(32768 * np.sum(time_field[i * d : i * d + d // 2])))
+        )
+        hash[y] = np.int16(
+            _cut_u16(round(32768 * np.sum(time_field[i * d + d // 2 : (i + 1) * d])))
+        )
     return encode_to_string(hash.tobytes())
 
+
 def audio_hash(file):
     return wave_hash(load_audio(file, 16000))
 
+
 def model_hash(config, tgt_sr, net_g, if_f0, version):
     pipeline = Pipeline(tgt_sr, config)
     audio = load_audio(str(pathlib.Path(__file__).parent / "lgdsng.mp3"), 16000)
     audio_max = np.abs(audio).max() / 0.95
     if audio_max > 1:
         np.divide(audio, audio_max, audio)
-    audio_opt = pipeline.pipeline(load_hubert(config.device, config.is_half), net_g, 0, audio,
-                      [0, 0, 0], 6, "rmvpe", "", 0, if_f0, 3, tgt_sr, 16000, 0.25,
-                      version, 0.33)
+    audio_opt = pipeline.pipeline(
+        load_hubert(config.device, config.is_half),
+        net_g,
+        0,
+        audio,
+        [0, 0, 0],
+        6,
+        "rmvpe",
+        "",
+        0,
+        if_f0,
+        3,
+        tgt_sr,
+        16000,
+        0.25,
+        version,
+        0.33,
+    )
     opt_len = len(audio_opt)
     diff = 48000 - opt_len
     n = diff // 2
@@ -98,6 +132,7 @@ def model_hash(config, tgt_sr, net_g, if_f0, version):
     del pipeline, audio, audio_opt
     return h
 
+
 def model_hash_ckpt(cpt):
     from infer.lib.infer_pack.models import (
         SynthesizerTrnMs256NSFsid,
@@ -105,6 +140,7 @@ def model_hash_ckpt(cpt):
         SynthesizerTrnMs768NSFsid,
         SynthesizerTrnMs768NSFsid_nono,
     )
+
     config = Config()
     with TorchSeedContext(114514):
         tgt_sr = cpt["config"][-1]
@@ -116,9 +152,9 @@ def model_hash_ckpt(cpt):
             ("v2", 1): SynthesizerTrnMs768NSFsid,
             ("v2", 0): SynthesizerTrnMs768NSFsid_nono,
         }
-        net_g = synthesizer_class.get(
-            (version, if_f0), SynthesizerTrnMs256NSFsid
-        )(*cpt["config"], is_half=config.is_half)
+        net_g = synthesizer_class.get((version, if_f0), SynthesizerTrnMs256NSFsid)(
+            *cpt["config"], is_half=config.is_half
+        )
 
         del net_g.enc_q
 
@@ -135,36 +171,47 @@ def model_hash_ckpt(cpt):
 
     return h
 
+
 def model_hash_from(path):
     cpt = torch.load(path, map_location="cpu")
     h = model_hash_ckpt(cpt)
     del cpt
     return h
 
+
 def _extend_difference(n, a, b):
-    if n < a: n = a
-    elif n > b: n = b
+    if n < a:
+        n = a
+    elif n > b:
+        n = b
     n -= a
-    n /= (b-a)
+    n /= b - a
     return n
 
+
 def hash_similarity(h1: str, h2: str) -> int:
     h1b, h2b = decode_from_string(h1), decode_from_string(h2)
     if len(h1b) != half_hash_len * 2 or len(h2b) != half_hash_len * 2:
         raise Exception("invalid hash length")
-    h1n, h2n = np.frombuffer(h1b, dtype='>i2'), np.frombuffer(h2b, dtype='>i2')
+    h1n, h2n = np.frombuffer(h1b, dtype=">i2"), np.frombuffer(h2b, dtype=">i2")
     d = 0
     for i in range(half_hash_len // 4):
         a = i * 2
         b = a + 1
         ax = complex(h1n[a], h1n[b])
         bx = complex(h2n[a], h2n[b])
-        if abs(ax) == 0 or abs(bx) == 0: continue
+        if abs(ax) == 0 or abs(bx) == 0:
+            continue
         d += np.abs(ax - bx)
-    frac = (np.linalg.norm(h1n) * np.linalg.norm(h2n))
-    cosine = np.dot(h1n.astype(np.float32), h2n.astype(np.float32)) / frac if frac != 0 else 1.0
+    frac = np.linalg.norm(h1n) * np.linalg.norm(h2n)
+    cosine = (
+        np.dot(h1n.astype(np.float32), h2n.astype(np.float32)) / frac
+        if frac != 0
+        else 1.0
+    )
     distance = _extend_difference(np.exp(-d / expand_factor), 0.5, 1.0)
     return round((abs(cosine) + distance) / 2, 6)
 
+
 def hash_id(h: str) -> str:
     return encode_to_string(hashlib.md5(decode_from_string(h)).digest())[:-1]

infer/modules/vc/info.py

@@ -7,6 +7,7 @@ from .hash import model_hash_ckpt, hash_id
 
 i18n = I18nAuto()
 
+
 def show_model_info(cpt, show_long_id=False):
     try:
         h = model_hash_ckpt(cpt)
@@ -14,10 +15,27 @@ def show_model_info(cpt, show_long_id=False):
         idread = cpt.get("id", "None")
         hread = cpt.get("hash", "None")
         if id != idread:
-            id += "("+i18n("实际计算")+"), "+idread+"("+i18n("从模型中读取")+")"
-        if not show_long_id: h = i18n("不显示")
+            id += (
+                "("
+                + i18n("实际计算")
+                + "), "
+                + idread
+                + "("
+                + i18n("从模型中读取")
+                + ")"
+            )
+        if not show_long_id:
+            h = i18n("不显示")
         elif h != hread:
-            h += "("+i18n("实际计算")+"), "+hread+"("+i18n("从模型中读取")+")"
+            h += (
+                "("
+                + i18n("实际计算")
+                + "), "
+                + hread
+                + "("
+                + i18n("从模型中读取")
+                + ")"
+            )
         txt = f"""{i18n("模型名")}: %s
 {i18n("封装时间")}: %s
 {i18n("信息")}: %s
@@ -32,13 +50,15 @@ def show_model_info(cpt, show_long_id=False):
             cpt.get("sr", "None"),
             i18n("有") if cpt.get("f0", 0) == 1 else i18n("无"),
             cpt.get("version", "None"),
-            id, h
+            id,
+            h,
         )
     except:
         txt = traceback.format_exc()
 
     return txt
 
+
 def show_info(path):
     try:
         a = torch.load(path, map_location="cpu")

infer/modules/vc/modules.py

@@ -136,7 +136,7 @@ class VC:
                 to_return_protect1,
                 index,
                 index,
-                show_model_info(self.cpt)
+                show_model_info(self.cpt),
             )
             if to_return_protect
             else {"visible": True, "maximum": n_spk, "__type__": "update"}
@@ -173,7 +173,8 @@ class VC:
                 self.hubert_model = load_hubert(self.config.device, self.config.is_half)
 
             if file_index:
-                if hasattr(file_index, "name"): file_index = str(file_index.name)
+                if hasattr(file_index, "name"):
+                    file_index = str(file_index.name)
                 file_index = (
                     file_index.strip(" ")
                     .strip('"')

infer/modules/vc/pipeline.py

@@ -114,6 +114,7 @@ class Pipeline(object):
                 )
         elif f0_method == "harvest":
             from hashlib import md5
+
             f0_cache_key = md5(x.tobytes()).digest()
             input_audio_path2wav[f0_cache_key] = x.astype(np.double)
             f0 = cache_harvest_f0(f0_cache_key, self.sr, f0_max, f0_min, 10)