Upload 2 files

logs/44k/G_160000.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:327077b358e87610e1257869510ebb5cc8466e0ce0cc872ca882b7c9069ff98b
+size 542792859

logs/44k/app.py

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+import io
+import os
+
+# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
+import gradio as gr
+import librosa
+import numpy as np
+import soundfile
+from inference.infer_tool import Svc
+import logging
+
+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)
+
+config_path = "configs/config.json"
+
+model = Svc("logs/44k/G_160000.pth", "configs/config.json", cluster_model_path="logs/44k/kmeans_10000.pt")
+
+
+
+def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
+    if input_audio is None:
+        return "You need to upload an audio", None
+    sampling_rate, audio = input_audio
+    # print(audio.shape,sampling_rate)
+    duration = audio.shape[0] / sampling_rate
+    if duration < 0:
+        return "请上传小于90s的音频，需要转换长音频请本地进行转换", 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)
+    print(audio.shape)
+    out_wav_path = "temp.wav"
+    soundfile.write(out_wav_path, audio, 16000, format="wav")
+    print( cluster_ratio, auto_f0, noise_scale)
+    _audio = model.slice_inference(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
+    return "Success", (44100, _audio)
+
+
+app = gr.Blocks()
+with app:
+    with gr.Tabs():
+        with gr.TabItem("Basic"):
+            gr.Markdown(value="""
+                sovits4.0 在线demo
+                
+                此demo为预训练底模在线demo，使用数据：云灏 即霜 辉宇·星AI 派蒙 绫地宁宁
+                """)
+            spks = list(model.spk2id.keys())
+            sid = gr.Dropdown(label="音色", choices=spks, value=spks[0])
+            vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
+            vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
+            cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
+            auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False)
+            slice_db = gr.Number(label="切片阈值", value=-40)
+            noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
+            vc_submit = gr.Button("转换", variant="primary")
+            vc_output1 = gr.Textbox(label="Output Message")
+            vc_output2 = gr.Audio(label="Output Audio")
+        vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [vc_output1, vc_output2])
+
+    app.launch(share=True)
+
+
+