init

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,382 @@
+## Ignore Visual Studio temporary files, build results, and
+## files generated by popular Visual Studio add-ons.
+##
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+
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+[Ww][Ii][Nn]32/
+[Aa][Rr][Mm]/
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+bld/
+[Bb]in/
+[Oo]bj/
+[Oo]ut/
+[Ll]og/
+[Ll]ogs/
+
+# Visual Studio 2015/2017 cache/options directory
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+# Uncomment if you have tasks that create the project's static files in wwwroot
+#wwwroot/
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+*.log
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+.builds
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+*.svclog
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+# Chutzpah Test files
+_Chutzpah*
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+# Visual C++ cache files
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+# Visual Studio profiler
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+# Visual Studio Trace Files
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+*.[Rr]e[Ss]harper
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+# (https://github.com/github/gitignore/pull/2483#issue-259490424)
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+# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
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+
+# GhostDoc plugin setting file
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+
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+**/*.DesktopClient/GeneratedArtifacts
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+**/*.Server/ModelManifest.xml
+_Pvt_Extensions
+
+# Paket dependency manager
+.paket/paket.exe
+paket-files/
+
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+.fake/
+
+# CodeRush personal settings
+.cr/personal
+
+# Python Tools for Visual Studio (PTVS)
+__pycache__/
+
+
+# Cake - Uncomment if you are using it
+# tools/**
+# !tools/packages.config
+
+# Tabs Studio
+*.tss
+
+# Telerik's JustMock configuration file
+*.jmconfig
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+*.btp.cs
+*.btm.cs
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+ASALocalRun/
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+*.nvuser
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+.mfractor/
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+healthchecksdb
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+# Backup folder for Package Reference Convert tool in Visual Studio 2017
+MigrationBackup/
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+.ionide/
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+# Fody - auto-generated XML schema
+FodyWeavers.xsd
+
+# build
+build
+monotonic_align/core.c
+*.o
+*.so
+*.dll
+
+# data
+/config.json
+/*.pth
+*.wav
+/monotonic_align/monotonic_align
+/resources
+/MoeGoe.spec
+/dist/MoeGoe
+/dist
+
+.idea

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Jingyi Li
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,13 +1,16 @@
 ---
-title: GTA SOVITS
-emoji: 🐨
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
+title: Sovits Models
+emoji: 🎙️
+colorFrom: gray
+colorTo: pink
+sdk: gradio
+sdk_version: 3.18.0
 app_file: app.py
 pinned: false
-license: openrail
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+我是一个菜鸡，推理界面设计参考了大佬。
+
+**但是使用此处的模型务必注明出处（即本人：B站Cyber蝈蝈总），这是孩子唯一的愿望。**
+

app-slice.py

@@ -0,0 +1,135 @@
+import os
+import gradio as gr
+import edge_tts
+from pathlib import Path
+import inference.infer_tool as infer_tool
+import utils
+from inference.infer_tool import Svc
+import logging
+import webbrowser
+import argparse
+import asyncio
+import librosa
+import soundfile
+import gradio.processing_utils as gr_processing_utils
+logging.getLogger('numba').setLevel(logging.WARNING)
+logging.getLogger('markdown_it').setLevel(logging.WARNING)
+logging.getLogger('urllib3').setLevel(logging.WARNING)
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
+
+audio_postprocess_ori = gr.Audio.postprocess
+
+def audio_postprocess(self, y):
+    data = audio_postprocess_ori(self, y)
+    if data is None:
+        return None
+    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
+
+
+gr.Audio.postprocess = audio_postprocess
+def create_vc_fn(model, sid):
+    def vc_fn(input_audio, vc_transform, auto_f0, slice_db, noise_scale, pad_seconds, tts_text, tts_voice, tts_mode):
+        if tts_mode:
+            if len(tts_text) > 100 and limitation:
+                return "Text is too long", None
+            if tts_text is None or tts_voice is None:
+                return "You need to enter text and select a voice", None
+            asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
+            audio, sr = librosa.load("tts.mp3")
+            soundfile.write("tts.wav", audio, 24000, format="wav")
+            wav_path = "tts.wav"
+        else:
+            if input_audio is None:
+                return "You need to select an audio", None
+            raw_audio_path = f"raw/{input_audio}"
+            if "." not in raw_audio_path:
+                raw_audio_path += ".wav"
+            infer_tool.format_wav(raw_audio_path)
+            wav_path = Path(raw_audio_path).with_suffix('.wav')
+        _audio = model.slice_inference(
+            wav_path, sid, vc_transform, slice_db,
+            cluster_infer_ratio=0,
+            auto_predict_f0=auto_f0,
+            noice_scale=noise_scale,
+            pad_seconds=pad_seconds)
+        model.clear_empty()
+        return "Success", (44100, _audio)
+    return vc_fn
+
+def refresh_raw_wav():
+    return gr.Dropdown.update(choices=os.listdir("raw"))
+
+def change_to_tts_mode(tts_mode):
+    if tts_mode:
+        return gr.Audio.update(visible=False), gr.Button.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
+    else:
+        return gr.Audio.update(visible=True), gr.Button.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument('--api', action="store_true", default=False)
+    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
+    parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
+    args = parser.parse_args()
+    hubert_model = utils.get_hubert_model().to(args.device)
+    models = []
+    voices = []
+    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
+    for r in tts_voice_list:
+        voices.append(f"{r['ShortName']}-{r['Gender']}")
+    raw = os.listdir("raw")
+    for f in os.listdir("models"):
+        name = f
+        model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device)
+        cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
+        models.append((name, cover, create_vc_fn(model, name)))
+    with gr.Blocks() as app:
+        gr.Markdown(
+            "# <center> Sovits Models\n"
+            "## <center> The input audio should be clean and pure voice without background music.\n"
+            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n"
+            "[Open In Colab](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)"
+            " without queue and length limitation.\n\n"
+            "[Original Repo](https://github.com/svc-develop-team/so-vits-svc)\n\n"
+            "Other models:\n"
+            "[rudolf](https://huggingface.co/spaces/sayashi/sovits-rudolf)\n"
+            "[teio](https://huggingface.co/spaces/sayashi/sovits-teio)\n"
+            "[goldship](https://huggingface.co/spaces/sayashi/sovits-goldship)\n"
+            "[tannhauser](https://huggingface.co/spaces/sayashi/sovits-tannhauser)\n"
+
+        )
+        with gr.Tabs():
+            for (name, cover, vc_fn) in models:
+                with gr.TabItem(name):
+                    with gr.Row():
+                        gr.Markdown(
+                            '<div align="center">'
+                            f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
+                            '</div>'
+                        )
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Row():
+                                vc_input = gr.Dropdown(label="Input audio", choices=raw)
+                                vc_refresh = gr.Button("🔁", variant="primary")
+                            vc_transform = gr.Number(label="vc_transform", value=0)
+                            slice_db = gr.Number(label="slice_db", value=-40)
+                            noise_scale = gr.Number(label="noise_scale", value=0.4)
+                            pad_seconds = gr.Number(label="pad_seconds", value=0.5)
+                            auto_f0 = gr.Checkbox(label="auto_f0", value=False)
+                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
+                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
+                            tts_voice = gr.Dropdown(choices=voices, visible=False)
+                            vc_submit = gr.Button("Generate", variant="primary")
+                        with gr.Column():
+                            vc_output1 = gr.Textbox(label="Output Message")
+                            vc_output2 = gr.Audio(label="Output Audio")
+                vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, slice_db,  noise_scale, pad_seconds, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2])
+                vc_refresh.click(refresh_raw_wav, [], [vc_input])
+                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, vc_refresh, tts_text, tts_voice])
+        if args.colab:
+            webbrowser.open("http://127.0.0.1:7860")
+        app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)

app.py

@@ -0,0 +1,141 @@
+import os
+import io
+import gradio as gr
+import librosa
+import numpy as np
+import utils
+from inference.infer_tool import Svc
+import logging
+import soundfile
+import asyncio
+import argparse
+import edge_tts
+import gradio.processing_utils as gr_processing_utils
+logging.getLogger('numba').setLevel(logging.WARNING)
+logging.getLogger('markdown_it').setLevel(logging.WARNING)
+logging.getLogger('urllib3').setLevel(logging.WARNING)
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
+
+audio_postprocess_ori = gr.Audio.postprocess
+
+def audio_postprocess(self, y):
+    data = audio_postprocess_ori(self, y)
+    if data is None:
+        return None
+    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
+
+
+gr.Audio.postprocess = audio_postprocess
+def create_vc_fn(model, sid):
+    def vc_fn(input_audio, vc_transform, auto_f0, tts_text, tts_voice, tts_mode):
+        if tts_mode:
+            if len(tts_text) > 100 and limitation:
+                return "Text is too long", None
+            if tts_text is None or tts_voice is None:
+                return "You need to enter text and select a voice", None
+            asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
+            audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
+            raw_path = io.BytesIO()
+            soundfile.write(raw_path, audio, 16000, format="wav")
+            raw_path.seek(0)
+            out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
+                                            auto_predict_f0=auto_f0,
+                                            )
+            return "Success", (44100, out_audio.cpu().numpy())
+        if input_audio is None:
+            return "You need to upload an audio", None
+        sampling_rate, audio = input_audio
+        duration = audio.shape[0] / sampling_rate
+        if duration > 20 and limitation:
+            return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
+        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+        if len(audio.shape) > 1:
+            audio = librosa.to_mono(audio.transpose(1, 0))
+        if sampling_rate != 16000:
+            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+        raw_path = io.BytesIO()
+        soundfile.write(raw_path, audio, 16000, format="wav")
+        raw_path.seek(0)
+        out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
+                                       auto_predict_f0=auto_f0,
+                                       )
+        return "Success", (44100, out_audio.cpu().numpy())
+    return vc_fn
+
+def change_to_tts_mode(tts_mode):
+    if tts_mode:
+        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True), gr.Checkbox.update(value=True)
+    else:
+        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Checkbox.update(value=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument('--api', action="store_true", default=False)
+    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
+    args = parser.parse_args()
+    hubert_model = utils.get_hubert_model().to(args.device)
+    models = []
+    # others = {
+    #     "rudolf": "https://huggingface.co/spaces/sayashi/sovits-rudolf",
+    #     "teio": "https://huggingface.co/spaces/sayashi/sovits-teio",
+    #     "goldship": "https://huggingface.co/spaces/sayashi/sovits-goldship",
+    #     "tannhauser": "https://huggingface.co/spaces/sayashi/sovits-tannhauser"
+    # }
+    voices = []
+    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
+    for r in tts_voice_list:
+        voices.append(f"{r['ShortName']}-{r['Gender']}")
+    for f in os.listdir("models"):
+        name = f
+        model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config_{f}.json", device=args.device)
+        cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
+        models.append((name, cover, create_vc_fn(model, name)))
+    with gr.Blocks() as app:
+        gr.Markdown(
+            "# <center> Sovits Models\n"
+            "## <center> The input audio should be clean and pure voice without background music.\n"
+            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n"
+            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n"
+            "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/sayashi/sovits-models?duplicate=true)\n\n"
+            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/svc-develop-team/so-vits-svc)"
+
+        )
+        with gr.Tabs():
+            for (name, cover, vc_fn) in models:
+                with gr.TabItem(name):
+                    with gr.Row():
+                        gr.Markdown(
+                            '<div align="center">'
+                            f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
+                            '</div>'
+                        )
+                    with gr.Row():
+                        with gr.Column():
+                            vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
+                            vc_transform = gr.Number(label="vc_transform", value=0)
+                            auto_f0 = gr.Checkbox(label="auto_f0", value=False)
+                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
+                            tts_text = gr.Textbox(visible=False, label="TTS text (100 words limitation)" if limitation else "TTS text")
+                            tts_voice = gr.Dropdown(choices=voices, visible=False)
+                            vc_submit = gr.Button("Generate", variant="primary")
+                        with gr.Column():
+                            vc_output1 = gr.Textbox(label="Output Message")
+                            vc_output2 = gr.Audio(label="Output Audio")
+                vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2])
+                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice, auto_f0])
+            for category, link in others.items():
+                with gr.TabItem(category):
+                    gr.Markdown(
+                        f'''
+                        <center>
+                          <h2>Click to Go</h2>
+                          <a href="{link}">
+                            <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg"
+                          </a>
+                        </center>
+                        '''
+                    )
+        app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)

cluster/__init__.py

@@ -0,0 +1,29 @@
+import numpy as np
+import torch
+from sklearn.cluster import KMeans
+
+def get_cluster_model(ckpt_path):
+    checkpoint = torch.load(ckpt_path)
+    kmeans_dict = {}
+    for spk, ckpt in checkpoint.items():
+        km = KMeans(ckpt["n_features_in_"])
+        km.__dict__["n_features_in_"] = ckpt["n_features_in_"]
+        km.__dict__["_n_threads"] = ckpt["_n_threads"]
+        km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"]
+        kmeans_dict[spk] = km
+    return kmeans_dict
+
+def get_cluster_result(model, x, speaker):
+    """
+        x: np.array [t, 256]
+        return cluster class result
+    """
+    return model[speaker].predict(x)
+
+def get_cluster_center_result(model, x,speaker):
+    """x: np.array [t, 256]"""
+    predict = model[speaker].predict(x)
+    return model[speaker].cluster_centers_[predict]
+
+def get_center(model, x,speaker):
+    return model[speaker].cluster_centers_[x]

cluster/train_cluster.py

@@ -0,0 +1,89 @@
+import os
+from glob import glob
+from pathlib import Path
+import torch
+import logging
+import argparse
+import torch
+import numpy as np
+from sklearn.cluster import KMeans, MiniBatchKMeans
+import tqdm
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+import time
+import random
+
+def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
+
+    logger.info(f"Loading features from {in_dir}")
+    features = []
+    nums = 0
+    for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
+        features.append(torch.load(path).squeeze(0).numpy().T)
+        # print(features[-1].shape)
+    features = np.concatenate(features, axis=0)
+    print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
+    features = features.astype(np.float32)
+    logger.info(f"Clustering features of shape: {features.shape}")
+    t = time.time()
+    if use_minibatch:
+        kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
+    else:
+        kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
+    print(time.time()-t, "s")
+
+    x = {
+            "n_features_in_": kmeans.n_features_in_,
+            "_n_threads": kmeans._n_threads,
+            "cluster_centers_": kmeans.cluster_centers_,
+    }
+    print("end")
+
+    return x
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset', type=Path, default="./dataset/44k",
+                        help='path of training data directory')
+    parser.add_argument('--output', type=Path, default="logs/44k",
+                        help='path of model output directory')
+
+    args = parser.parse_args()
+
+    checkpoint_dir = args.output
+    dataset = args.dataset
+    n_clusters = 10000
+
+    ckpt = {}
+    for spk in os.listdir(dataset):
+        if os.path.isdir(dataset/spk):
+            print(f"train kmeans for {spk}...")
+            in_dir = dataset/spk
+            x = train_cluster(in_dir, n_clusters, verbose=False)
+            ckpt[spk] = x
+
+    checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
+    checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
+    torch.save(
+        ckpt,
+        checkpoint_path,
+    )
+
+
+    # import cluster
+    # for spk in tqdm.tqdm(os.listdir("dataset")):
+    #     if os.path.isdir(f"dataset/{spk}"):
+    #         print(f"start kmeans inference for {spk}...")
+    #         for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
+    #             mel_path = feature_path.replace(".discrete.npy",".mel.npy")
+    #             mel_spectrogram = np.load(mel_path)
+    #             feature_len = mel_spectrogram.shape[-1]
+    #             c = np.load(feature_path)
+    #             c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
+    #             feature = c.T
+    #             feature_class = cluster.get_cluster_result(feature, spk)
+    #             np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
+
+

data_utils.py

@@ -0,0 +1,155 @@
+import time
+import os
+import random
+import numpy as np
+import torch
+import torch.utils.data
+
+import modules.commons as commons
+import utils
+from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
+from utils import load_wav_to_torch, load_filepaths_and_text
+
+# import h5py
+
+
+"""Multi speaker version"""
+
+
+class TextAudioSpeakerLoader(torch.utils.data.Dataset):
+    """
+        1) loads audio, speaker_id, text pairs
+        2) normalizes text and converts them to sequences of integers
+        3) computes spectrograms from audio files.
+    """
+
+    def __init__(self, audiopaths, hparams, all_in_mem: bool = False):
+        self.audiopaths = load_filepaths_and_text(audiopaths)
+        self.max_wav_value = hparams.data.max_wav_value
+        self.sampling_rate = hparams.data.sampling_rate
+        self.filter_length = hparams.data.filter_length
+        self.hop_length = hparams.data.hop_length
+        self.win_length = hparams.data.win_length
+        self.sampling_rate = hparams.data.sampling_rate
+        self.use_sr = hparams.train.use_sr
+        self.spec_len = hparams.train.max_speclen
+        self.spk_map = hparams.spk
+
+        random.seed(1234)
+        random.shuffle(self.audiopaths)
+        
+        self.all_in_mem = all_in_mem
+        if self.all_in_mem:
+            self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
+
+    def get_audio(self, filename):
+        filename = filename.replace("\\", "/")
+        audio, sampling_rate = load_wav_to_torch(filename)
+        if sampling_rate != self.sampling_rate:
+            raise ValueError("{} SR doesn't match target {} SR".format(
+                sampling_rate, self.sampling_rate))
+        audio_norm = audio / self.max_wav_value
+        audio_norm = audio_norm.unsqueeze(0)
+        spec_filename = filename.replace(".wav", ".spec.pt")
+
+        # Ideally, all data generated after Mar 25 should have .spec.pt
+        if os.path.exists(spec_filename):
+            spec = torch.load(spec_filename)
+        else:
+            spec = spectrogram_torch(audio_norm, self.filter_length,
+                                     self.sampling_rate, self.hop_length, self.win_length,
+                                     center=False)
+            spec = torch.squeeze(spec, 0)
+            torch.save(spec, spec_filename)
+
+        spk = filename.split("/")[-2]
+        spk = torch.LongTensor([self.spk_map[spk]])
+
+        f0 = np.load(filename + ".f0.npy")
+        f0, uv = utils.interpolate_f0(f0)
+        f0 = torch.FloatTensor(f0)
+        uv = torch.FloatTensor(uv)
+
+        c = torch.load(filename+ ".soft.pt")
+        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
+
+
+        lmin = min(c.size(-1), spec.size(-1))
+        assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
+        assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
+        spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
+        audio_norm = audio_norm[:, :lmin * self.hop_length]
+
+        return c, f0, spec, audio_norm, spk, uv
+
+    def random_slice(self, c, f0, spec, audio_norm, spk, uv):
+        # if spec.shape[1] < 30:
+        #     print("skip too short audio:", filename)
+        #     return None
+        if spec.shape[1] > 800:
+            start = random.randint(0, spec.shape[1]-800)
+            end = start + 790
+            spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
+            audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
+
+        return c, f0, spec, audio_norm, spk, uv
+
+    def __getitem__(self, index):
+        if self.all_in_mem:
+            return self.random_slice(*self.cache[index])
+        else:
+            return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
+
+    def __len__(self):
+        return len(self.audiopaths)
+
+
+class TextAudioCollate:
+
+    def __call__(self, batch):
+        batch = [b for b in batch if b is not None]
+
+        input_lengths, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([x[0].shape[1] for x in batch]),
+            dim=0, descending=True)
+
+        max_c_len = max([x[0].size(1) for x in batch])
+        max_wav_len = max([x[3].size(1) for x in batch])
+
+        lengths = torch.LongTensor(len(batch))
+
+        c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
+        f0_padded = torch.FloatTensor(len(batch), max_c_len)
+        spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
+        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+        spkids = torch.LongTensor(len(batch), 1)
+        uv_padded = torch.FloatTensor(len(batch), max_c_len)
+
+        c_padded.zero_()
+        spec_padded.zero_()
+        f0_padded.zero_()
+        wav_padded.zero_()
+        uv_padded.zero_()
+
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+
+            c = row[0]
+            c_padded[i, :, :c.size(1)] = c
+            lengths[i] = c.size(1)
+
+            f0 = row[1]
+            f0_padded[i, :f0.size(0)] = f0
+
+            spec = row[2]
+            spec_padded[i, :, :spec.size(1)] = spec
+
+            wav = row[3]
+            wav_padded[i, :, :wav.size(1)] = wav
+
+            spkids[i, 0] = row[4]
+
+            uv = row[5]
+            uv_padded[i, :uv.size(0)] = uv
+
+        return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded

hubert/checkpoint_best_legacy_500.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60d936ec5a566776fc392e69ad8b630d14eb588111233fe313436e200a7b187b
+size 1330114945

hubert/hubert_model.py

@@ -0,0 +1,222 @@
+import copy
+import random
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as t_func
+from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
+
+
+class Hubert(nn.Module):
+    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
+        super().__init__()
+        self._mask = mask
+        self.feature_extractor = FeatureExtractor()
+        self.feature_projection = FeatureProjection()
+        self.positional_embedding = PositionalConvEmbedding()
+        self.norm = nn.LayerNorm(768)
+        self.dropout = nn.Dropout(0.1)
+        self.encoder = TransformerEncoder(
+            nn.TransformerEncoderLayer(
+                768, 12, 3072, activation="gelu", batch_first=True
+            ),
+            12,
+        )
+        self.proj = nn.Linear(768, 256)
+
+        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
+        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
+
+    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        mask = None
+        if self.training and self._mask:
+            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
+            x[mask] = self.masked_spec_embed.to(x.dtype)
+        return x, mask
+
+    def encode(
+            self, x: torch.Tensor, layer: Optional[int] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = self.feature_extractor(x)
+        x = self.feature_projection(x.transpose(1, 2))
+        x, mask = self.mask(x)
+        x = x + self.positional_embedding(x)
+        x = self.dropout(self.norm(x))
+        x = self.encoder(x, output_layer=layer)
+        return x, mask
+
+    def logits(self, x: torch.Tensor) -> torch.Tensor:
+        logits = torch.cosine_similarity(
+            x.unsqueeze(2),
+            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
+            dim=-1,
+        )
+        return logits / 0.1
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        x, mask = self.encode(x)
+        x = self.proj(x)
+        logits = self.logits(x)
+        return logits, mask
+
+
+class HubertSoft(Hubert):
+    def __init__(self):
+        super().__init__()
+
+    @torch.inference_mode()
+    def units(self, wav: torch.Tensor) -> torch.Tensor:
+        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        x, _ = self.encode(wav)
+        return self.proj(x)
+
+
+class FeatureExtractor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
+        self.norm0 = nn.GroupNorm(512, 512)
+        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
+        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = t_func.gelu(self.norm0(self.conv0(x)))
+        x = t_func.gelu(self.conv1(x))
+        x = t_func.gelu(self.conv2(x))
+        x = t_func.gelu(self.conv3(x))
+        x = t_func.gelu(self.conv4(x))
+        x = t_func.gelu(self.conv5(x))
+        x = t_func.gelu(self.conv6(x))
+        return x
+
+
+class FeatureProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = nn.LayerNorm(512)
+        self.projection = nn.Linear(512, 768)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        x = self.projection(x)
+        x = self.dropout(x)
+        return x
+
+
+class PositionalConvEmbedding(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            768,
+            768,
+            kernel_size=128,
+            padding=128 // 2,
+            groups=16,
+        )
+        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x.transpose(1, 2))
+        x = t_func.gelu(x[:, :, :-1])
+        return x.transpose(1, 2)
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
+    ) -> None:
+        super(TransformerEncoder, self).__init__()
+        self.layers = nn.ModuleList(
+            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+
+    def forward(
+            self,
+            src: torch.Tensor,
+            mask: torch.Tensor = None,
+            src_key_padding_mask: torch.Tensor = None,
+            output_layer: Optional[int] = None,
+    ) -> torch.Tensor:
+        output = src
+        for layer in self.layers[:output_layer]:
+            output = layer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+        return output
+
+
+def _compute_mask(
+        shape: Tuple[int, int],
+        mask_prob: float,
+        mask_length: int,
+        device: torch.device,
+        min_masks: int = 0,
+) -> torch.Tensor:
+    batch_size, sequence_length = shape
+
+    if mask_length < 1:
+        raise ValueError("`mask_length` has to be bigger than 0.")
+
+    if mask_length > sequence_length:
+        raise ValueError(
+            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
+        )
+
+    # compute number of masked spans in batch
+    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # make sure num masked indices <= sequence_length
+    if num_masked_spans * mask_length > sequence_length:
+        num_masked_spans = sequence_length // mask_length
+
+    # SpecAugment mask to fill
+    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
+
+    # uniform distribution to sample from, make sure that offset samples are < sequence_length
+    uniform_dist = torch.ones(
+        (batch_size, sequence_length - (mask_length - 1)), device=device
+    )
+
+    # get random indices to mask
+    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
+
+    # expand masked indices to masked spans
+    mask_indices = (
+        mask_indices.unsqueeze(dim=-1)
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    offsets = (
+        torch.arange(mask_length, device=device)[None, None, :]
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    mask_idxs = mask_indices + offsets
+
+    # scatter indices to mask
+    mask = mask.scatter(1, mask_idxs, True)
+
+    return mask
+
+
+def hubert_soft(
+        path: str,
+) -> HubertSoft:
+    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
+    Args:
+        path (str): path of a pretrained model
+    """
+    hubert = HubertSoft()
+    checkpoint = torch.load(path)
+    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
+    hubert.load_state_dict(checkpoint)
+    hubert.eval()
+    return hubert

hubert/hubert_model_onnx.py

@@ -0,0 +1,217 @@
+import copy
+import random
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as t_func
+from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
+
+
+class Hubert(nn.Module):
+    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
+        super().__init__()
+        self._mask = mask
+        self.feature_extractor = FeatureExtractor()
+        self.feature_projection = FeatureProjection()
+        self.positional_embedding = PositionalConvEmbedding()
+        self.norm = nn.LayerNorm(768)
+        self.dropout = nn.Dropout(0.1)
+        self.encoder = TransformerEncoder(
+            nn.TransformerEncoderLayer(
+                768, 12, 3072, activation="gelu", batch_first=True
+            ),
+            12,
+        )
+        self.proj = nn.Linear(768, 256)
+
+        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
+        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
+
+    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        mask = None
+        if self.training and self._mask:
+            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
+            x[mask] = self.masked_spec_embed.to(x.dtype)
+        return x, mask
+
+    def encode(
+            self, x: torch.Tensor, layer: Optional[int] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = self.feature_extractor(x)
+        x = self.feature_projection(x.transpose(1, 2))
+        x, mask = self.mask(x)
+        x = x + self.positional_embedding(x)
+        x = self.dropout(self.norm(x))
+        x = self.encoder(x, output_layer=layer)
+        return x, mask
+
+    def logits(self, x: torch.Tensor) -> torch.Tensor:
+        logits = torch.cosine_similarity(
+            x.unsqueeze(2),
+            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
+            dim=-1,
+        )
+        return logits / 0.1
+
+
+class HubertSoft(Hubert):
+    def __init__(self):
+        super().__init__()
+
+    def units(self, wav: torch.Tensor) -> torch.Tensor:
+        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        x, _ = self.encode(wav)
+        return self.proj(x)
+
+    def forward(self, x):
+        return self.units(x)
+
+class FeatureExtractor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
+        self.norm0 = nn.GroupNorm(512, 512)
+        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
+        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = t_func.gelu(self.norm0(self.conv0(x)))
+        x = t_func.gelu(self.conv1(x))
+        x = t_func.gelu(self.conv2(x))
+        x = t_func.gelu(self.conv3(x))
+        x = t_func.gelu(self.conv4(x))
+        x = t_func.gelu(self.conv5(x))
+        x = t_func.gelu(self.conv6(x))
+        return x
+
+
+class FeatureProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = nn.LayerNorm(512)
+        self.projection = nn.Linear(512, 768)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        x = self.projection(x)
+        x = self.dropout(x)
+        return x
+
+
+class PositionalConvEmbedding(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            768,
+            768,
+            kernel_size=128,
+            padding=128 // 2,
+            groups=16,
+        )
+        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x.transpose(1, 2))
+        x = t_func.gelu(x[:, :, :-1])
+        return x.transpose(1, 2)
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
+    ) -> None:
+        super(TransformerEncoder, self).__init__()
+        self.layers = nn.ModuleList(
+            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+
+    def forward(
+            self,
+            src: torch.Tensor,
+            mask: torch.Tensor = None,
+            src_key_padding_mask: torch.Tensor = None,
+            output_layer: Optional[int] = None,
+    ) -> torch.Tensor:
+        output = src
+        for layer in self.layers[:output_layer]:
+            output = layer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+        return output
+
+
+def _compute_mask(
+        shape: Tuple[int, int],
+        mask_prob: float,
+        mask_length: int,
+        device: torch.device,
+        min_masks: int = 0,
+) -> torch.Tensor:
+    batch_size, sequence_length = shape
+
+    if mask_length < 1:
+        raise ValueError("`mask_length` has to be bigger than 0.")
+
+    if mask_length > sequence_length:
+        raise ValueError(
+            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
+        )
+
+    # compute number of masked spans in batch
+    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # make sure num masked indices <= sequence_length
+    if num_masked_spans * mask_length > sequence_length:
+        num_masked_spans = sequence_length // mask_length
+
+    # SpecAugment mask to fill
+    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
+
+    # uniform distribution to sample from, make sure that offset samples are < sequence_length
+    uniform_dist = torch.ones(
+        (batch_size, sequence_length - (mask_length - 1)), device=device
+    )
+
+    # get random indices to mask
+    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
+
+    # expand masked indices to masked spans
+    mask_indices = (
+        mask_indices.unsqueeze(dim=-1)
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    offsets = (
+        torch.arange(mask_length, device=device)[None, None, :]
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    mask_idxs = mask_indices + offsets
+
+    # scatter indices to mask
+    mask = mask.scatter(1, mask_idxs, True)
+
+    return mask
+
+
+def hubert_soft(
+        path: str,
+) -> HubertSoft:
+    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
+    Args:
+        path (str): path of a pretrained model
+    """
+    hubert = HubertSoft()
+    checkpoint = torch.load(path)
+    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
+    hubert.load_state_dict(checkpoint)
+    hubert.eval()
+    return hubert

inference/chunks_temp.json

@@ -0,0 +1 @@
+{"info": "temp_dict"}

inference/infer_tool.py

@@ -0,0 +1,324 @@
+import hashlib
+import io
+import json
+import logging
+import os
+import time
+from pathlib import Path
+from inference import slicer
+
+import librosa
+import numpy as np
+# import onnxruntime
+import parselmouth
+import soundfile
+import torch
+import torchaudio
+
+import cluster
+from hubert import hubert_model
+import utils
+from models import SynthesizerTrn
+
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+
+def read_temp(file_name):
+    if not os.path.exists(file_name):
+        with open(file_name, "w") as f:
+            f.write(json.dumps({"info": "temp_dict"}))
+        return {}
+    else:
+        try:
+            with open(file_name, "r") as f:
+                data = f.read()
+            data_dict = json.loads(data)
+            if os.path.getsize(file_name) > 50 * 1024 * 1024:
+                f_name = file_name.replace("\\", "/").split("/")[-1]
+                print(f"clean {f_name}")
+                for wav_hash in list(data_dict.keys()):
+                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
+                        del data_dict[wav_hash]
+        except Exception as e:
+            print(e)
+            print(f"{file_name} error,auto rebuild file")
+            data_dict = {"info": "temp_dict"}
+        return data_dict
+
+
+def write_temp(file_name, data):
+    with open(file_name, "w") as f:
+        f.write(json.dumps(data))
+
+
+def timeit(func):
+    def run(*args, **kwargs):
+        t = time.time()
+        res = func(*args, **kwargs)
+        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
+        return res
+
+    return run
+
+
+def format_wav(audio_path):
+    if Path(audio_path).suffix == '.wav':
+        return
+    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
+    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
+
+
+def get_end_file(dir_path, end):
+    file_lists = []
+    for root, dirs, files in os.walk(dir_path):
+        files = [f for f in files if f[0] != '.']
+        dirs[:] = [d for d in dirs if d[0] != '.']
+        for f_file in files:
+            if f_file.endswith(end):
+                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
+    return file_lists
+
+
+def get_md5(content):
+    return hashlib.new("md5", content).hexdigest()
+
+def fill_a_to_b(a, b):
+    if len(a) < len(b):
+        for _ in range(0, len(b) - len(a)):
+            a.append(a[0])
+
+def mkdir(paths: list):
+    for path in paths:
+        if not os.path.exists(path):
+            os.mkdir(path)
+
+def pad_array(arr, target_length):
+    current_length = arr.shape[0]
+    if current_length >= target_length:
+        return arr
+    else:
+        pad_width = target_length - current_length
+        pad_left = pad_width // 2
+        pad_right = pad_width - pad_left
+        padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
+        return padded_arr
+    
+def split_list_by_n(list_collection, n, pre=0):
+    for i in range(0, len(list_collection), n):
+        yield list_collection[i-pre if i-pre>=0 else i: i + n]
+
+
+class F0FilterException(Exception):
+    pass
+
+class Svc(object):
+    def __init__(self, net_g_path, config_path,
+                 device=None,
+                 cluster_model_path="logs/44k/kmeans_10000.pt"):
+        self.net_g_path = net_g_path
+        if device is None:
+            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        else:
+            self.dev = torch.device(device)
+        self.net_g_ms = None
+        self.hps_ms = utils.get_hparams_from_file(config_path)
+        self.target_sample = self.hps_ms.data.sampling_rate
+        self.hop_size = self.hps_ms.data.hop_length
+        self.spk2id = self.hps_ms.spk
+        # 加载hubert
+        self.hubert_model = utils.get_hubert_model().to(self.dev)
+        self.load_model()
+        if os.path.exists(cluster_model_path):
+            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
+
+    def load_model(self):
+        # 获取模型配置
+        self.net_g_ms = SynthesizerTrn(
+            self.hps_ms.data.filter_length // 2 + 1,
+            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
+            **self.hps_ms.model)
+        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
+        if "half" in self.net_g_path and torch.cuda.is_available():
+            _ = self.net_g_ms.half().eval().to(self.dev)
+        else:
+            _ = self.net_g_ms.eval().to(self.dev)
+
+
+
+    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling):
+
+        wav, sr = librosa.load(in_path, sr=self.target_sample)
+
+        if F0_mean_pooling == True:
+            f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev)
+            if f0_filter and sum(f0) == 0:
+                raise F0FilterException("未检���到人声")
+            f0 = torch.FloatTensor(list(f0))
+            uv = torch.FloatTensor(list(uv))
+        if F0_mean_pooling == False:
+            f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
+            if f0_filter and sum(f0) == 0:
+                raise F0FilterException("未检测到人声")
+            f0, uv = utils.interpolate_f0(f0)
+            f0 = torch.FloatTensor(f0)
+            uv = torch.FloatTensor(uv)
+
+        f0 = f0 * 2 ** (tran / 12)
+        f0 = f0.unsqueeze(0).to(self.dev)
+        uv = uv.unsqueeze(0).to(self.dev)
+
+        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
+        wav16k = torch.from_numpy(wav16k).to(self.dev)
+        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
+        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
+
+        if cluster_infer_ratio !=0:
+            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
+            cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
+            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
+
+        c = c.unsqueeze(0)
+        return c, f0, uv
+
+    def infer(self, speaker, tran, raw_path,
+              cluster_infer_ratio=0,
+              auto_predict_f0=False,
+              noice_scale=0.4,
+              f0_filter=False,
+              F0_mean_pooling=False
+              ):
+
+        speaker_id = self.spk2id.__dict__.get(speaker)
+        if not speaker_id and type(speaker) is int:
+            if len(self.spk2id.__dict__) >= speaker:
+                speaker_id = speaker
+        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
+        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling)
+        if "half" in self.net_g_path and torch.cuda.is_available():
+            c = c.half()
+        with torch.no_grad():
+            start = time.time()
+            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
+            use_time = time.time() - start
+            print("vits use time:{}".format(use_time))
+        return audio, audio.shape[-1]
+
+    def clear_empty(self):
+        # 清理显存
+        torch.cuda.empty_cache()
+
+    def slice_inference(self,
+                        raw_audio_path,
+                        spk,
+                        tran,
+                        slice_db,
+                        cluster_infer_ratio,
+                        auto_predict_f0,
+                        noice_scale,
+                        pad_seconds=0.5,
+                        clip_seconds=0,
+                        lg_num=0,
+                        lgr_num =0.75,
+                        F0_mean_pooling = False
+                        ):
+        wav_path = raw_audio_path
+        chunks = slicer.cut(wav_path, db_thresh=slice_db)
+        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
+        per_size = int(clip_seconds*audio_sr)
+        lg_size = int(lg_num*audio_sr)
+        lg_size_r = int(lg_size*lgr_num)
+        lg_size_c_l = (lg_size-lg_size_r)//2
+        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
+        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
+        
+        audio = []
+        for (slice_tag, data) in audio_data:
+            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
+            # padd
+            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
+            if slice_tag:
+                print('jump empty segment')
+                _audio = np.zeros(length)
+                audio.extend(list(pad_array(_audio, length)))
+                continue
+            if per_size != 0:
+                datas = split_list_by_n(data, per_size,lg_size)
+            else:
+                datas = [data]
+            for k,dat in enumerate(datas):
+                per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
+                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
+                # padd
+                pad_len = int(audio_sr * pad_seconds)
+                dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
+                raw_path = io.BytesIO()
+                soundfile.write(raw_path, dat, audio_sr, format="wav")
+                raw_path.seek(0)
+                out_audio, out_sr = self.infer(spk, tran, raw_path,
+                                                    cluster_infer_ratio=cluster_infer_ratio,
+                                                    auto_predict_f0=auto_predict_f0,
+                                                    noice_scale=noice_scale,
+                                                    F0_mean_pooling = F0_mean_pooling
+                                                    )
+                _audio = out_audio.cpu().numpy()
+                pad_len = int(self.target_sample * pad_seconds)
+                _audio = _audio[pad_len:-pad_len]
+                _audio = pad_array(_audio, per_length)
+                if lg_size!=0 and k!=0:
+                    lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
+                    lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr_num != 1 else _audio[0:lg_size]
+                    lg_pre = lg1*(1-lg)+lg2*lg
+                    audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
+                    audio.extend(lg_pre)
+                    _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
+                audio.extend(list(_audio))
+        return np.array(audio)
+
+class RealTimeVC:
+    def __init__(self):
+        self.last_chunk = None
+        self.last_o = None
+        self.chunk_len = 16000  # 区块长度
+        self.pre_len = 3840  # 交叉淡化长度，640的倍数
+
+    """输入输出都是1维numpy 音频波形数组"""
+
+    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
+                cluster_infer_ratio=0,
+                auto_predict_f0=False,
+                noice_scale=0.4,
+                f0_filter=False):
+
+        import maad
+        audio, sr = torchaudio.load(input_wav_path)
+        audio = audio.cpu().numpy()[0]
+        temp_wav = io.BytesIO()
+        if self.last_chunk is None:
+            input_wav_path.seek(0)
+
+            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
+                                        cluster_infer_ratio=cluster_infer_ratio,
+                                        auto_predict_f0=auto_predict_f0,
+                                        noice_scale=noice_scale,
+                                        f0_filter=f0_filter)
+
+            audio = audio.cpu().numpy()
+            self.last_chunk = audio[-self.pre_len:]
+            self.last_o = audio
+            return audio[-self.chunk_len:]
+        else:
+            audio = np.concatenate([self.last_chunk, audio])
+            soundfile.write(temp_wav, audio, sr, format="wav")
+            temp_wav.seek(0)
+
+            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
+                                        cluster_infer_ratio=cluster_infer_ratio,
+                                        auto_predict_f0=auto_predict_f0,
+                                        noice_scale=noice_scale,
+                                        f0_filter=f0_filter)
+
+            audio = audio.cpu().numpy()
+            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
+            self.last_chunk = audio[-self.pre_len:]
+            self.last_o = audio
+            return ret[self.chunk_len:2 * self.chunk_len]

inference/infer_tool_grad.py

@@ -0,0 +1,160 @@
+import hashlib
+import json
+import logging
+import os
+import time
+from pathlib import Path
+import io
+import librosa
+import maad
+import numpy as np
+from inference import slicer
+import parselmouth
+import soundfile
+import torch
+import torchaudio
+
+from hubert import hubert_model
+import utils
+from models import SynthesizerTrn
+logging.getLogger('numba').setLevel(logging.WARNING)
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+def resize2d_f0(x, target_len):
+    source = np.array(x)
+    source[source < 0.001] = np.nan
+    target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
+                       source)
+    res = np.nan_to_num(target)
+    return res
+
+def get_f0(x, p_len,f0_up_key=0):
+
+    time_step = 160 / 16000 * 1000
+    f0_min = 50
+    f0_max = 1100
+    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+
+    f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
+        time_step=time_step / 1000, voicing_threshold=0.6,
+        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
+
+    pad_size=(p_len - len(f0) + 1) // 2
+    if(pad_size>0 or p_len - len(f0) - pad_size>0):
+        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
+
+    f0 *= pow(2, f0_up_key / 12)
+    f0_mel = 1127 * np.log(1 + f0 / 700)
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
+    f0_mel[f0_mel <= 1] = 1
+    f0_mel[f0_mel > 255] = 255
+    f0_coarse = np.rint(f0_mel).astype(np.int)
+    return f0_coarse, f0
+
+def clean_pitch(input_pitch):
+    num_nan = np.sum(input_pitch == 1)
+    if num_nan / len(input_pitch) > 0.9:
+        input_pitch[input_pitch != 1] = 1
+    return input_pitch
+
+
+def plt_pitch(input_pitch):
+    input_pitch = input_pitch.astype(float)
+    input_pitch[input_pitch == 1] = np.nan
+    return input_pitch
+
+
+def f0_to_pitch(ff):
+    f0_pitch = 69 + 12 * np.log2(ff / 440)
+    return f0_pitch
+
+
+def fill_a_to_b(a, b):
+    if len(a) < len(b):
+        for _ in range(0, len(b) - len(a)):
+            a.append(a[0])
+
+
+def mkdir(paths: list):
+    for path in paths:
+        if not os.path.exists(path):
+            os.mkdir(path)
+
+
+class VitsSvc(object):
+    def __init__(self):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.SVCVITS = None
+        self.hps = None
+        self.speakers = None
+        self.hubert_soft = utils.get_hubert_model()
+
+    def set_device(self, device):
+        self.device = torch.device(device)
+        self.hubert_soft.to(self.device)
+        if self.SVCVITS != None:
+            self.SVCVITS.to(self.device)
+
+    def loadCheckpoint(self, path):
+        self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
+        self.SVCVITS = SynthesizerTrn(
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            **self.hps.model)
+        _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None)
+        _ = self.SVCVITS.eval().to(self.device)
+        self.speakers = self.hps.spk
+
+    def get_units(self, source, sr):
+        source = source.unsqueeze(0).to(self.device)
+        with torch.inference_mode():
+            units = self.hubert_soft.units(source)
+            return units
+
+
+    def get_unit_pitch(self, in_path, tran):
+        source, sr = torchaudio.load(in_path)
+        source = torchaudio.functional.resample(source, sr, 16000)
+        if len(source.shape) == 2 and source.shape[1] >= 2:
+            source = torch.mean(source, dim=0).unsqueeze(0)
+        soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
+        f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
+        return soft, f0
+
+    def infer(self, speaker_id, tran, raw_path):
+        speaker_id = self.speakers[speaker_id]
+        sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
+        soft, pitch = self.get_unit_pitch(raw_path, tran)
+        f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
+        stn_tst = torch.FloatTensor(soft)
+        with torch.no_grad():
+            x_tst = stn_tst.unsqueeze(0).to(self.device)
+            x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
+            audio = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
+        return audio, audio.shape[-1]
+
+    def inference(self,srcaudio,chara,tran,slice_db):
+        sampling_rate, audio = srcaudio
+        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+        if len(audio.shape) > 1:
+            audio = librosa.to_mono(audio.transpose(1, 0))
+        if sampling_rate != 16000:
+            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+        soundfile.write("tmpwav.wav", audio, 16000, format="wav")
+        chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
+        audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
+        audio = []
+        for (slice_tag, data) in audio_data:
+            length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate))
+            raw_path = io.BytesIO()
+            soundfile.write(raw_path, data, audio_sr, format="wav")
+            raw_path.seek(0)
+            if slice_tag:
+                _audio = np.zeros(length)
+            else:
+                out_audio, out_sr = self.infer(chara, tran, raw_path)
+                _audio = out_audio.cpu().numpy()
+            audio.extend(list(_audio))
+        audio = (np.array(audio) * 32768.0).astype('int16')
+        return (self.hps.data.sampling_rate,audio)

inference/slicer.py

@@ -0,0 +1,142 @@
+import librosa
+import torch
+import torchaudio
+
+
+class Slicer:
+    def __init__(self,
+                 sr: int,
+                 threshold: float = -40.,
+                 min_length: int = 5000,
+                 min_interval: int = 300,
+                 hop_size: int = 20,
+                 max_sil_kept: int = 5000):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
+        if not max_sil_kept >= hop_size:
+            raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        if len(waveform.shape) > 1:
+            return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
+        else:
+            return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
+
+    # @timeit
+    def slice(self, waveform):
+        if len(waveform.shape) > 1:
+            samples = librosa.to_mono(waveform)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
+        rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start: i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if silence_start is not None and total_frames - silence_start >= self.min_interval:
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        if len(sil_tags) == 0:
+            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
+        else:
+            chunks = []
+            # 第一段静音并非从头开始，补上有声片段
+            if sil_tags[0][0]:
+                chunks.append(
+                    {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
+            for i in range(0, len(sil_tags)):
+                # 标识有声片段（跳过第一段）
+                if i:
+                    chunks.append({"slice": False,
+                                   "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
+                # 标识所有静音片段
+                chunks.append({"slice": True,
+                               "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
+            # 最后一段静音并非结尾，补上结尾片段
+            if sil_tags[-1][1] * self.hop_size < len(waveform):
+                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
+            chunk_dict = {}
+            for i in range(len(chunks)):
+                chunk_dict[str(i)] = chunks[i]
+            return chunk_dict
+
+
+def cut(audio_path, db_thresh=-30, min_len=5000):
+    audio, sr = librosa.load(audio_path, sr=None)
+    slicer = Slicer(
+        sr=sr,
+        threshold=db_thresh,
+        min_length=min_len
+    )
+    chunks = slicer.slice(audio)
+    return chunks
+
+
+def chunks2audio(audio_path, chunks):
+    chunks = dict(chunks)
+    audio, sr = torchaudio.load(audio_path)
+    if len(audio.shape) == 2 and audio.shape[1] >= 2:
+        audio = torch.mean(audio, dim=0).unsqueeze(0)
+    audio = audio.cpu().numpy()[0]
+    result = []
+    for k, v in chunks.items():
+        tag = v["split_time"].split(",")
+        if tag[0] != tag[1]:
+            result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
+    return result, sr

inference_main.py

@@ -0,0 +1,130 @@
+import io
+import logging
+import time
+from pathlib import Path
+
+import librosa
+import matplotlib.pyplot as plt
+import numpy as np
+import soundfile
+
+from inference import infer_tool
+from inference import slicer
+from inference.infer_tool import Svc
+
+logging.getLogger('numba').setLevel(logging.WARNING)
+chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
+
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description='sovits4 inference')
+
+    # 一定要设置的部分
+    parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
+    parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
+    parser.add_argument('-cl', '--clip', type=float, default=0, help='音频强制切片，默认0为自动切片，单位为秒/s')
+    parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下')
+    parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）')
+    parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
+
+    # 可选项部分
+    parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False,help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调')
+    parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填')
+    parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则默认0即可')
+    parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两段音频切片的交叉淡入长度，如果强制切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒')
+    parser.add_argument('-fmp', '--f0_mean_pooling', type=bool, default=False, help='是否对F0使用均值滤波器(池化)，对部分哑音有改善。注意，启动该选项会导致推理速度下降，默认关闭')
+
+    # 不用动的部分
+    parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50')
+    parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu')
+    parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别，会影响咬字和音质，较为玄学')
+    parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现')
+    parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式')
+    parser.add_argument('-lgr', '--linear_gradient_retain', type=float, default=0.75, help='自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭')
+
+    args = parser.parse_args()
+
+    svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path)
+    infer_tool.mkdir(["raw", "results"])
+    clean_names = args.clean_names
+    trans = args.trans
+    spk_list = args.spk_list
+    slice_db = args.slice_db
+    wav_format = args.wav_format
+    auto_predict_f0 = args.auto_predict_f0
+    cluster_infer_ratio = args.cluster_infer_ratio
+    noice_scale = args.noice_scale
+    pad_seconds = args.pad_seconds
+    clip = args.clip
+    lg = args.linear_gradient
+    lgr = args.linear_gradient_retain
+    F0_mean_pooling = args.f0_mean_pooling
+
+    infer_tool.fill_a_to_b(trans, clean_names)
+    for clean_name, tran in zip(clean_names, trans):
+        raw_audio_path = f"raw/{clean_name}"
+        if "." not in raw_audio_path:
+            raw_audio_path += ".wav"
+        infer_tool.format_wav(raw_audio_path)
+        wav_path = Path(raw_audio_path).with_suffix('.wav')
+        chunks = slicer.cut(wav_path, db_thresh=slice_db)
+        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
+        per_size = int(clip*audio_sr)
+        lg_size = int(lg*audio_sr)
+        lg_size_r = int(lg_size*lgr)
+        lg_size_c_l = (lg_size-lg_size_r)//2
+        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
+        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
+
+        for spk in spk_list:
+            audio = []
+            for (slice_tag, data) in audio_data:
+                print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
+                
+                length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
+                if slice_tag:
+                    print('jump empty segment')
+                    _audio = np.zeros(length)
+                    audio.extend(list(infer_tool.pad_array(_audio, length)))
+                    continue
+                if per_size != 0:
+                    datas = infer_tool.split_list_by_n(data, per_size,lg_size)
+                else:
+                    datas = [data]
+                for k,dat in enumerate(datas):
+                    per_length = int(np.ceil(len(dat) / audio_sr * svc_model.target_sample)) if clip!=0 else length
+                    if clip!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
+                    # padd
+                    pad_len = int(audio_sr * pad_seconds)
+                    dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
+                    raw_path = io.BytesIO()
+                    soundfile.write(raw_path, dat, audio_sr, format="wav")
+                    raw_path.seek(0)
+                    out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
+                                                        cluster_infer_ratio=cluster_infer_ratio,
+                                                        auto_predict_f0=auto_predict_f0,
+                                                        noice_scale=noice_scale,
+                                                        F0_mean_pooling = F0_mean_pooling
+                                                        )
+                    _audio = out_audio.cpu().numpy()
+                    pad_len = int(svc_model.target_sample * pad_seconds)
+                    _audio = _audio[pad_len:-pad_len]
+                    _audio = infer_tool.pad_array(_audio, per_length)
+                    if lg_size!=0 and k!=0:
+                        lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr != 1 else audio[-lg_size:]
+                        lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr != 1 else _audio[0:lg_size]
+                        lg_pre = lg1*(1-lg)+lg2*lg
+                        audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr != 1 else audio[0:-lg_size]
+                        audio.extend(lg_pre)
+                        _audio = _audio[lg_size_c_l+lg_size_r:] if lgr != 1 else _audio[lg_size:]
+                    audio.extend(list(_audio))
+            key = "auto" if auto_predict_f0 else f"{tran}key"
+            cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
+            res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'
+            soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
+
+if __name__ == '__main__':
+    main()

models.py

@@ -0,0 +1,420 @@
+import copy
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import modules.attentions as attentions
+import modules.commons as commons
+import modules.modules as modules
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+import utils
+from modules.commons import init_weights, get_padding
+from vdecoder.hifigan.models import Generator
+from utils import f0_to_coarse
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class Encoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    # print(x.shape,x_lengths.shape)
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      n_layers,
+      gin_channels=0,
+      filter_channels=None,
+      n_heads=None,
+      p_dropout=None):
+    super().__init__()
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+    self.f0_emb = nn.Embedding(256, hidden_channels)
+
+    self.enc_ =  attentions.Encoder(
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+
+  def forward(self, x, x_mask, f0=None, noice_scale=1):
+    x = x + self.f0_emb(f0).transpose(1,2)
+    x = self.enc_(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
+
+    return z, m, logs, x_mask
+
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class SpeakerEncoder(torch.nn.Module):
+    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
+        super(SpeakerEncoder, self).__init__()
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, mels):
+        self.lstm.flatten_parameters()
+        _, (hidden, _) = self.lstm(mels)
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+
+    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
+        mel_slices = []
+        for i in range(0, total_frames-partial_frames, partial_hop):
+            mel_range = torch.arange(i, i+partial_frames)
+            mel_slices.append(mel_range)
+
+        return mel_slices
+
+    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
+        mel_len = mel.size(1)
+        last_mel = mel[:,-partial_frames:]
+
+        if mel_len > partial_frames:
+            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
+            mels = list(mel[:,s] for s in mel_slices)
+            mels.append(last_mel)
+            mels = torch.stack(tuple(mels), 0).squeeze(1)
+
+            with torch.no_grad():
+                partial_embeds = self(mels)
+            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
+            #embed = embed / torch.linalg.norm(embed, 2)
+        else:
+            with torch.no_grad():
+                embed = self(last_mel)
+
+        return embed
+
+class F0Decoder(nn.Module):
+    def __init__(self,
+                 out_channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout,
+                 spk_channels=0):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.spk_channels = spk_channels
+
+        self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
+        self.decoder = attentions.FFT(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout)
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.f0_prenet = nn.Conv1d(1, hidden_channels , 3, padding=1)
+        self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
+
+    def forward(self, x, norm_f0, x_mask, spk_emb=None):
+        x = torch.detach(x)
+        if (spk_emb is not None):
+            x = x + self.cond(spk_emb)
+        x += self.f0_prenet(norm_f0)
+        x = self.prenet(x) * x_mask
+        x = self.decoder(x * x_mask, x_mask)
+        x = self.proj(x) * x_mask
+        return x
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock,
+    resblock_kernel_sizes,
+    resblock_dilation_sizes,
+    upsample_rates,
+    upsample_initial_channel,
+    upsample_kernel_sizes,
+    gin_channels,
+    ssl_dim,
+    n_speakers,
+    sampling_rate=44100,
+    **kwargs):
+
+    super().__init__()
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.gin_channels = gin_channels
+    self.ssl_dim = ssl_dim
+    self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+    self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
+
+    self.enc_p = TextEncoder(
+        inter_channels,
+        hidden_channels,
+        filter_channels=filter_channels,
+        n_heads=n_heads,
+        n_layers=n_layers,
+        kernel_size=kernel_size,
+        p_dropout=p_dropout
+    )
+    hps = {
+        "sampling_rate": sampling_rate,
+        "inter_channels": inter_channels,
+        "resblock": resblock,
+        "resblock_kernel_sizes": resblock_kernel_sizes,
+        "resblock_dilation_sizes": resblock_dilation_sizes,
+        "upsample_rates": upsample_rates,
+        "upsample_initial_channel": upsample_initial_channel,
+        "upsample_kernel_sizes": upsample_kernel_sizes,
+        "gin_channels": gin_channels,
+    }
+    self.dec = Generator(h=hps)
+    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+    self.f0_decoder = F0Decoder(
+        1,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        spk_channels=gin_channels
+    )
+    self.emb_uv = nn.Embedding(2, hidden_channels)
+
+  def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
+    g = self.emb_g(g).transpose(1,2)
+    # ssl prenet
+    x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
+    x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
+
+    # f0 predict
+    lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
+    norm_lf0 = utils.normalize_f0(lf0, x_mask, uv)
+    pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
+
+    # encoder
+    z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
+    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
+
+    # flow
+    z_p = self.flow(z, spec_mask, g=g)
+    z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
+
+    # nsf decoder
+    o = self.dec(z_slice, g=g, f0=pitch_slice)
+
+    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0
+
+  def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
+    c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
+    g = self.emb_g(g).transpose(1,2)
+    x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
+    x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
+
+    if predict_f0:
+        lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
+        norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
+        pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
+        f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
+
+    z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale)
+    z = self.flow(z_p, c_mask, g=g, reverse=True)
+    o = self.dec(z * c_mask, g=g, f0=f0)
+    return o

models/arthur/arthur.pth

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+size 180653938

models/arthur/config_arthur.json

@@ -0,0 +1,93 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 800,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 6,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 10240,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 512,
+    "port": "8001",
+    "keep_ckpts": 0
+  },
+  "data": {
+    "training_files": "filelists/train.txt",
+    "validation_files": "filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": 22050
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [
+      3,
+      7,
+      11
+    ],
+    "resblock_dilation_sizes": [
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ]
+    ],
+    "upsample_rates": [
+      8,
+      8,
+      2,
+      2,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      4,
+      4,
+      4
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 256,
+    "n_speakers": 1
+  },
+  "spk": {
+    "arthur": 0
+  }
+}

models/carl/carl.pth

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+size 180665609

models/carl/config_carl.json

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+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 800,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 4,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 10240,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 512,
+    "port": "8001",
+    "keep_ckpts": 20
+  },
+  "data": {
+    "training_files": "filelists/train.txt",
+    "validation_files": "filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": 22050
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [
+      3,
+      7,
+      11
+    ],
+    "resblock_dilation_sizes": [
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ]
+    ],
+    "upsample_rates": [
+      8,
+      8,
+      2,
+      2,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      4,
+      4,
+      4
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256,
+    "ssl_dim": 256,
+    "n_speakers": 1
+  },
+  "spk": {
+    "carl": 0
+  }
+}

models/cesar/cesar.pth

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+size 209238367

models/cesar/config_cesar.json

@@ -0,0 +1,99 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 2000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 6,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 10240,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 512,
+    "port": "8001",
+    "keep_ckpts": 10,
+    "all_in_mem": false,
+    "vol_aug": true
+  },
+  "data": {
+    "training_files": "filelists/train.txt",
+    "validation_files": "filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": 22050
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [
+      3,
+      7,
+      11
+    ],
+    "resblock_dilation_sizes": [
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ],
+      [
+        1,
+        3,
+        5
+      ]
+    ],
+    "upsample_rates": [
+      8,
+      8,
+      2,
+      2,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      4,
+      4,
+      4
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 768,
+    "ssl_dim": 768,
+    "n_speakers": 1,
+    "vocoder_name": "nsf-hifigan",
+    "speech_encoder": "vec768l12",
+    "speaker_embedding": false,
+    "vol_embedding": true
+  },
+  "spk": {
+    "cesar": 0
+  }
+}

models/katalina/config_katalina.json

@@ -0,0 +1,99 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 800,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 12,
+    "fp16_run": false,
+    "lr_decay": 0.999875,
+    "segment_size": 10240,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "use_sr": true,
+    "max_speclen": 512,
+    "port": "8001",
+    "keep_ckpts": 3,
+    "all_in_mem": false,
+    "vol_aug": true
+  },
+  "data": {
+    "training_files": "filelists/train.txt",
+    "validation_files": "filelists/val.txt",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": 22050
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [
+      3,
+      7,
+      11
+    ],
+    "resblock_dilation_sizes": [
+      [
+        1,
+        3,
+        5
+      ],
+      [
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