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import io |
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import gradio as gr |
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import librosa |
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import numpy as np |
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import soundfile |
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import torch |
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from inference.infer_tool import Svc |
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import logging |
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logging.getLogger('numba').setLevel(logging.WARNING) |
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model_name = "logs/32k/tiehu.pth" |
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config_name = "configs/tiehu.json" |
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svc_model = Svc(model_name, config_name) |
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sid_map = { |
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"南云铁虎": "tiehu" |
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} |
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def vc_fn(sid, input_audio, vc_transform): |
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if input_audio is None: |
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return "You need to upload an audio", None |
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sampling_rate, audio = input_audio |
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duration = audio.shape[0] / sampling_rate |
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if duration > 45: |
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return "请上传小于45s的音频，需要转换长音频请本地进行转换", None |
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
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if len(audio.shape) > 1: |
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audio = librosa.to_mono(audio.transpose(1, 0)) |
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if sampling_rate != 16000: |
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) |
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print(audio.shape) |
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out_wav_path = io.BytesIO() |
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soundfile.write(out_wav_path, audio, 16000, format="wav") |
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out_wav_path.seek(0) |
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sid = sid_map[sid] |
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out_audio, out_sr = svc_model.infer(sid, vc_transform, out_wav_path) |
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_audio = out_audio.cpu().numpy() |
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return "Success", (32000, _audio) |
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app = gr.Blocks() |
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with app: |
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with gr.Tabs(): |
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with gr.TabItem("Basic"): |
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gr.Markdown(value=""" |
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南云铁虎Sovits3.0模型\n |
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使用前请仔细阅读terms.md了解相关条款，使用模型将默认您同意条款\n |
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如需本地使用，下载files里configs文件夹中.json格式文件,\n |
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logs/32k文件夹中.pth格式文件\n |
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https://huggingface.co/datasets/chilge/3.0tuili/tree/main下载3.0.zip文件并解压，查看说明.txt文件\n |
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项目改写基于 https://huggingface.co/spaces/innnky/nyaru-svc-3.0\n |
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""") |
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sid = gr.Dropdown(label="音色", choices=["南云铁虎"], value="tiehu") |
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vc_input3 = gr.Audio(label="上传音频（长度小于45秒）") |
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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) |
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vc_submit = gr.Button("转换", variant="primary") |
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vc_output1 = gr.Textbox(label="Output Message") |
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vc_output2 = gr.Audio(label="Output Audio") |
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vc_submit.click(vc_fn, [sid, vc_input3, vc_transform], [vc_output1, vc_output2]) |
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app.launch() |
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