feat(infer): add model hash identification

and optimize infer-web ui

infer-web.py

@@ -10,13 +10,12 @@ load_dotenv("sha256.env")
 if sys.platform == "darwin":
     os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 
-from infer.modules.vc import VC
+from infer.modules.vc import VC, show_info
 from infer.modules.uvr5.modules import uvr
 from infer.lib.train.process_ckpt import (
     change_info,
     extract_small_model,
     merge,
-    show_info,
 )
 from i18n.i18n import I18nAuto
 from configs.config import Config
@@ -838,6 +837,7 @@ with gr.Blocks(title="RVC WebUI") as app:
                 clean_button.click(
                     fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
                 )
+            modelinfo = gr.Textbox(label=i18n("模型信息"))
             with gr.TabItem(i18n("单次推理")):
                 with gr.Group():
                     with gr.Row():
@@ -846,24 +846,23 @@ with gr.Blocks(title="RVC WebUI") as app:
                                 label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
                                 value=0,
                             )
-                            input_audio0 = gr.Textbox(
+                            input_audio0 = gr.File(
                                 label=i18n(
-                                    "输入待处理音频文件路径(默认是正确格式示例)"
+                                    "待处理音频文件"
                                 ),
-                                placeholder="C:\\Users\\Desktop\\audio_example.wav",
+                                file_types=["audio"]
-                            )
-                            file_index1 = gr.Textbox(
-                                label=i18n(
-                                    "特征检索库文件路径,为空则使用下拉的选择结果"
-                                ),
-                                placeholder="C:\\Users\\Desktop\\model_example.index",
-                                interactive=True,
                             )
                             file_index2 = gr.Dropdown(
                                 label=i18n("自动检测index路径,下拉式选择(dropdown)"),
                                 choices=sorted(index_paths),
                                 interactive=True,
                             )
+                            file_index1 = 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"
+                                ),
+                            )
+                        with gr.Column():
                             f0method0 = gr.Radio(
                                 label=i18n(
                                     "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
@@ -876,8 +875,6 @@ with gr.Blocks(title="RVC WebUI") as app:
                                 value="rmvpe",
                                 interactive=True,
                             )
-
-                        with gr.Column():
                             resample_sr0 = gr.Slider(
                                 minimum=0,
                                 maximum=48000,
@@ -928,6 +925,10 @@ with gr.Blocks(title="RVC WebUI") as app:
                                 ),
                                 visible=False,
                             )
+                            but0 = gr.Button(i18n("转换"), variant="primary")
+                            vc_output2 = gr.Audio(
+                                label=i18n("输出音频(右下角三个点,点了可以下载)")
+                            )
 
                             refresh_button.click(
                                 fn=change_choices,
@@ -935,19 +936,8 @@ with gr.Blocks(title="RVC WebUI") as app:
                                 outputs=[sid0, file_index2],
                                 api_name="infer_refresh",
                             )
-                            # file_big_npy1 = gr.Textbox(
-                            #     label=i18n("特征文件路径"),
-                            #     value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
-                            #     interactive=True,
-                            # )
                 with gr.Group():
-                    with gr.Column():
+                        vc_output1 = gr.Textbox(label=i18n("输出信息"))
-                        but0 = gr.Button(i18n("转换"), variant="primary")
-                        with gr.Row():
-                            vc_output1 = gr.Textbox(label=i18n("输出信息"))
-                            vc_output2 = gr.Audio(
-                                label=i18n("输出音频(右下角三个点,点了可以下载)")
-                            )
 
                         but0.click(
                             vc.vc_single,
@@ -981,36 +971,28 @@ with gr.Blocks(title="RVC WebUI") as app:
                             label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
                             value=0,
                         )
+                        dir_input = gr.Textbox(
+                            label=i18n(
+                                "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"
+                            ),
+                            placeholder="C:\\Users\\Desktop\\input_vocal_dir",
+                        )
+                        inputs = gr.File(
+                            file_count="multiple",
+                            label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
+                        )
                         opt_input = gr.Textbox(
                             label=i18n("指定输出文件夹"), value="opt"
                         )
-                        file_index3 = gr.Textbox(
-                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
-                            value="",
-                            interactive=True,
-                        )
                         file_index4 = gr.Dropdown(
                             label=i18n("自动检测index路径,下拉式选择(dropdown)"),
                             choices=sorted(index_paths),
                             interactive=True,
                         )
-                        f0method1 = gr.Radio(
+                        file_index3 = gr.File(
                             label=i18n(
-                                "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
+                                "特征检索库文件路径,为空则使用下拉的选择结果"
                             ),
-                            choices=(
-                                ["pm", "harvest", "crepe", "rmvpe"]
-                                if config.dml == False
-                                else ["pm", "harvest", "rmvpe"]
-                            ),
-                            value="rmvpe",
-                            interactive=True,
-                        )
-                        format1 = gr.Radio(
-                            label=i18n("导出文件格式"),
-                            choices=["wav", "flac", "mp3", "m4a"],
-                            value="wav",
-                            interactive=True,
                         )
 
                         refresh_button.click(
@@ -1026,6 +1008,18 @@ with gr.Blocks(title="RVC WebUI") as app:
                         # )
 
                     with gr.Column():
+                        f0method1 = gr.Radio(
+                            label=i18n(
+                                "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
+                            ),
+                            choices=(
+                                ["pm", "harvest", "crepe", "rmvpe"]
+                                if config.dml == False
+                                else ["pm", "harvest", "rmvpe"]
+                            ),
+                            value="rmvpe",
+                            interactive=True,
+                        )
                         resample_sr1 = gr.Slider(
                             minimum=0,
                             maximum=48000,
@@ -1070,48 +1064,42 @@ with gr.Blocks(title="RVC WebUI") as app:
                             value=1,
                             interactive=True,
                         )
-                with gr.Row():
+                        format1 = gr.Radio(
-                    dir_input = gr.Textbox(
+                            label=i18n("导出文件格式"),
-                        label=i18n(
+                            choices=["wav", "flac", "mp3", "m4a"],
-                            "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"
+                            value="wav",
-                        ),
+                            interactive=True,
-                        placeholder="C:\\Users\\Desktop\\input_vocal_dir",
+                        )
-                    )
+                        but1 = gr.Button(i18n("转换"), variant="primary")
-                    inputs = gr.File(
-                        file_count="multiple",
-                        label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
-                    )
 
-                with gr.Row():
+                vc_output3 = gr.Textbox(label=i18n("输出信息"))
-                    but1 = gr.Button(i18n("转换"), variant="primary")
-                    vc_output3 = gr.Textbox(label=i18n("输出信息"))
 
-                    but1.click(
+                but1.click(
-                        vc.vc_multi,
+                    vc.vc_multi,
-                        [
+                    [
-                            spk_item,
+                        spk_item,
-                            dir_input,
+                        dir_input,
-                            opt_input,
+                        opt_input,
-                            inputs,
+                        inputs,
-                            vc_transform1,
+                        vc_transform1,
-                            f0method1,
+                        f0method1,
-                            file_index3,
+                        file_index3,
-                            file_index4,
+                        file_index4,
-                            # file_big_npy2,
+                        # file_big_npy2,
-                            index_rate2,
+                        index_rate2,
-                            filter_radius1,
+                        filter_radius1,
-                            resample_sr1,
+                        resample_sr1,
-                            rms_mix_rate1,
+                        rms_mix_rate1,
-                            protect1,
+                        protect1,
-                            format1,
+                        format1,
-                        ],
+                    ],
-                        [vc_output3],
+                    [vc_output3],
-                        api_name="infer_convert_batch",
+                    api_name="infer_convert_batch",
-                    )
+                )
                 sid0.change(
                     fn=vc.get_vc,
                     inputs=[sid0, protect0, protect1],
-                    outputs=[spk_item, protect0, protect1, file_index2, file_index4],
+                    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

@@ -1,16 +1,17 @@
 import os
-import sys
 import traceback
 from collections import OrderedDict
+from time import time
 
 import torch
 
 from i18n.i18n import I18nAuto
+from infer.modules.vc import model_hash_ckpt, hash_id
 
 i18n = I18nAuto()
 
-
+# add author sign
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
+def save_small_model(ckpt, sr, if_f0, name, epoch, version, hps):
     try:
         opt = OrderedDict()
         opt["weight"] = {}
@@ -39,28 +40,20 @@ def savee(ckpt, sr, if_f0, name, epoch, version, hps):
             hps.data.sampling_rate,
         ]
         opt["info"] = "%sepoch" % epoch
+        opt["name"] = name
+        opt["timestamp"] = int(time())
         opt["sr"] = sr
         opt["f0"] = if_f0
         opt["version"] = version
+        h = model_hash_ckpt(opt)
+        opt["hash"] = h
+        opt["id"] = hash_id(h)
         torch.save(opt, "assets/weights/%s.pth" % name)
         return "Success."
     except:
         return traceback.format_exc()
 
 
-def show_info(path):
-    try:
-        a = torch.load(path, map_location="cpu")
-        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
-            a.get("info", "None"),
-            a.get("sr", "None"),
-            a.get("f0", "None"),
-            a.get("version", "None"),
-        )
-    except:
-        return traceback.format_exc()
-
-
 def extract_small_model(path, name, sr, if_f0, info, version):
     try:
         ckpt = torch.load(path, map_location="cpu")
@@ -182,9 +175,14 @@ def extract_small_model(path, name, sr, if_f0, info, version):
         if info == "":
             info = "Extracted model."
         opt["info"] = info
+        opt["name"] = name
+        opt["timestamp"] = int(time())
         opt["version"] = version
         opt["sr"] = sr
         opt["f0"] = int(if_f0)
+        h = model_hash_ckpt(opt)
+        opt["hash"] = h
+        opt["id"] = hash_id(h)
         torch.save(opt, "assets/weights/%s.pth" % name)
         return "Success."
     except:
@@ -251,10 +249,15 @@ def merge(path1, path2, alpha1, sr, f0, info, name, version):
         elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
         elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
         """
+        opt["name"] = name
+        opt["timestamp"] = int(time())
         opt["sr"] = sr
         opt["f0"] = 1 if f0 == i18n("是") else 0
         opt["version"] = version
         opt["info"] = info
+        h = model_hash_ckpt(opt)
+        opt["hash"] = h
+        opt["id"] = hash_id(h)
         torch.save(opt, "assets/weights/%s.pth" % name)
         return "Success."
     except:

infer/modules/train/train.py

@@ -74,7 +74,7 @@ from infer.lib.train.losses import (
     kl_loss,
 )
 from infer.lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from infer.lib.train.process_ckpt import savee
+from infer.lib.train.process_ckpt import save_small_model
 
 global_step = 0
 
@@ -602,7 +602,7 @@ def train_and_evaluate(
                 % (
                     hps.name,
                     epoch,
-                    savee(
+                    save_small_model(
                         ckpt,
                         hps.sample_rate,
                         hps.if_f0,
@@ -626,7 +626,7 @@ def train_and_evaluate(
         logger.info(
             "saving final ckpt:%s"
             % (
-                savee(
+                save_small_model(
                     ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps
                 )
             )

infer/modules/vc/__init__.py

@@ -1,3 +1,5 @@
 from .pipeline import Pipeline
 from .modules import VC
 from .utils import get_index_path_from_model, load_hubert
+from .info import show_info
+from .hash import model_hash_ckpt, hash_id

infer/modules/vc/hash.py

@@ -0,0 +1,170 @@
+import numpy as np
+import torch
+import hashlib
+import pathlib
+from scipy.fft import fft
+from pybase16384 import encode_to_string, decode_from_string
+
+if __name__ == "__main__":
+    import os, sys
+    now_dir = os.getcwd()
+    sys.path.append(now_dir)
+
+from configs.config import Config, singleton_variable
+
+from .pipeline import Pipeline
+from .utils import load_hubert
+
+from infer.lib.audio import load_audio
+
+class TorchSeedContext:
+    def __init__(self, seed):
+        self.seed = seed
+        self.state = None
+
+    def __enter__(self):
+        self.state = torch.random.get_rng_state()
+        torch.manual_seed(self.seed)
+
+    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)
+    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)
+    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))
+    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)
+    if len(time_field) != 48000:
+        raise Exception("time not hashable")
+    freq_field = fft(time_field)
+    if len(freq_field) != 48000:
+        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')
+    d = 375 * 512 // half_hash_len
+    for i in range(half_hash_len//4):
+        a = i*2
+        b = a+1
+        x = a + half_hash_len//2
+        y = x+1
+        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]))))
+    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)
+    opt_len = len(audio_opt)
+    diff = 48000 - opt_len
+    n = diff//2
+    if n > 0:
+        audio_opt = np.pad(audio_opt, (n, n))
+    elif n < 0:
+        n = -n
+        audio_opt = audio_opt[n:-n]
+    h = wave_hash(audio_opt)
+    del pipeline, audio, audio_opt
+    return h
+
+def model_hash_ckpt(cpt):
+    from infer.lib.infer_pack.models import (
+        SynthesizerTrnMs256NSFsid,
+        SynthesizerTrnMs256NSFsid_nono,
+        SynthesizerTrnMs768NSFsid,
+        SynthesizerTrnMs768NSFsid_nono,
+    )
+    config = Config()
+    with TorchSeedContext(114514):
+        tgt_sr = cpt["config"][-1]
+        if_f0 = cpt.get("f0", 1)
+        version = cpt.get("version", "v1")
+        synthesizer_class = {
+            ("v1", 1): SynthesizerTrnMs256NSFsid,
+            ("v1", 0): SynthesizerTrnMs256NSFsid_nono,
+            ("v2", 1): SynthesizerTrnMs768NSFsid,
+            ("v2", 0): SynthesizerTrnMs768NSFsid_nono,
+        }
+        net_g = synthesizer_class.get(
+            (version, if_f0), SynthesizerTrnMs256NSFsid
+        )(*cpt["config"], is_half=config.is_half)
+
+        del net_g.enc_q
+
+        net_g.load_state_dict(cpt["weight"], strict=False)
+        net_g.eval().to(config.device)
+        if config.is_half:
+            net_g = net_g.half()
+        else:
+            net_g = net_g.float()
+        
+        h = model_hash(config, tgt_sr, net_g, if_f0, version)
+
+    del net_g
+
+    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
+    n -= 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')
+    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
+        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
+    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

@@ -0,0 +1,50 @@
+import traceback
+from i18n.i18n import I18nAuto
+from datetime import datetime
+import torch
+
+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)
+        id = hash_id(h)
+        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")
+        elif h != hread:
+            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
+{i18n("采样率")}: %s
+{i18n("音高引导(f0)")}: %s
+{i18n("版本")}: %s
+{i18n("ID(短)")}: %s
+{i18n("ID(长)")}: %s""" % (
+            cpt.get("name", "None"),
+            datetime.fromtimestamp(float(cpt.get("timestamp", 0))),
+            cpt.get("info", "None"),
+            cpt.get("sr", "None"),
+            i18n("有") if cpt.get("f0", 0) == 1 else i18n("无"),
+            cpt.get("version", "None"),
+            id, h
+        )
+    except:
+        txt = traceback.format_exc()
+
+    return txt
+
+def show_info(path):
+    try:
+        a = torch.load(path, map_location="cpu")
+        txt = show_model_info(a, show_long_id=True)
+        del a
+    except:
+        txt = traceback.format_exc()
+
+    return txt

infer/modules/vc/modules.py

@@ -16,7 +16,7 @@ from infer.lib.infer_pack.models import (
     SynthesizerTrnMs768NSFsid,
     SynthesizerTrnMs768NSFsid_nono,
 )
-
+from .info import show_model_info
 from .pipeline import Pipeline
 from .utils import get_index_path_from_model, load_hubert
 
@@ -136,6 +136,7 @@ class VC:
                 to_return_protect1,
                 index,
                 index,
+                show_model_info(self.cpt)
             )
             if to_return_protect
             else {"visible": True, "maximum": n_spk, "__type__": "update"}
@@ -158,6 +159,8 @@ class VC:
     ):
         if input_audio_path is None:
             return "You need to upload an audio", None
+        elif hasattr(input_audio_path, "name"):
+            input_audio_path = str(input_audio_path.name)
         f0_up_key = int(f0_up_key)
         try:
             audio = load_audio(input_audio_path, 16000)
@@ -170,6 +173,7 @@ 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)
                 file_index = (
                     file_index.strip(" ")
                     .strip('"')
@@ -207,12 +211,12 @@ class VC:
             else:
                 tgt_sr = self.tgt_sr
             index_info = (
-                "Index:\n%s." % file_index
+                "Index: %s." % file_index
                 if os.path.exists(file_index)
                 else "Index not used."
             )
             return (
-                "Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
+                "Success.\n%s\nTime: npy: %.2fs, f0: %.2fs, infer: %.2fs."
                 % (index_info, *times),
                 (tgt_sr, audio_opt),
             )

requirements-amd.txt

@@ -47,3 +47,4 @@ ffmpy==0.3.1
 python-dotenv>=1.0.0
 av
 torchfcpe
+pybase16384

requirements-dml.txt

@@ -45,3 +45,4 @@ ffmpy==0.3.1
 python-dotenv>=1.0.0
 av
 torchfcpe
+pybase16384

requirements-ipex.txt

@@ -53,3 +53,4 @@ av
 FreeSimpleGUI
 sounddevice
 torchfcpe
+pybase16384

requirements-py311.txt

@@ -46,3 +46,4 @@ torchfcpe
 ffmpy==0.3.1
 python-dotenv>=1.0.0
 av
+pybase16384

requirements.txt

@@ -46,3 +46,4 @@ torchfcpe
 ffmpy==0.3.1
 python-dotenv>=1.0.0
 av
+pybase16384