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import io |
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import os |
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os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") |
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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 inference_main |
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import logging |
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logging.getLogger('numba').setLevel(logging.WARNING) |
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logging.getLogger('markdown_it').setLevel(logging.WARNING) |
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logging.getLogger('urllib3').setLevel(logging.WARNING) |
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logging.getLogger('matplotlib').setLevel(logging.WARNING) |
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config_path = "configs/config.json" |
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model_34k = Svc("logs/G_34000.pth", config_path) |
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model_139k = Svc("logs/G_139000.pth", config_path) |
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model_map = { |
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"G_34000.pth": model_34k, |
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"G_139000.pth": model_139k |
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} |
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def vc_fn(sid, input_audio, vc_transform, model): |
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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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out_audio, out_sr = inference_main.infer(sid, out_wav_path, model_map[model], vc_transform) |
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_audio = out_audio.cpu().numpy() |
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return "Success", (44100, _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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这是ai猫雷3.5版本demo，算是一个不太一样的尝试，图一乐就行 |
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模型采用了聚类的方案对content vec进行离散化，主要是针对于解决猫雷模型不够"像"猫雷的问题 |
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牺牲了部分咬字性能（可能会有很多发音错误），但是会更加像目标音色（大概？ |
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暂时不提供训练代码 |
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本地合成可以删除32、33两行代码以解除合成45s长度限制""") |
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sid = gr.Dropdown(label="音色", choices=['nyaru', "taffy", "otto"], value="nyaru") |
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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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model = gr.Dropdown(label="模型", choices=list(model_map.keys()), value="G_34000.pth") |
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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, model], [vc_output1, vc_output2]) |
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app.launch(server_port=7860) |
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