57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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import io
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import logging
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import soundfile
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import torch
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import torchaudio
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from flask import Flask, request, send_file
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from flask_cors import CORS
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from inference.infer_tool import Svc, RealTimeVC
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app = Flask(__name__)
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CORS(app)
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logging.getLogger('numba').setLevel(logging.WARNING)
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@app.route("/voiceChangeModel", methods=["POST"])
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def voice_change_model():
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request_form = request.form
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wave_file = request.files.get("sample", None)
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# 变调信息
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f_pitch_change = float(request_form.get("fPitchChange", 0))
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# DAW所需的采样率
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daw_sample = int(float(request_form.get("sampleRate", 0)))
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speaker_id = int(float(request_form.get("sSpeakId", 0)))
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# http获得wav文件并转换
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input_wav_path = io.BytesIO(wave_file.read())
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# 模型推理
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if raw_infer:
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out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
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tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample)
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else:
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out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path)
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tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
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# 返回音频
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out_wav_path = io.BytesIO()
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soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav")
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out_wav_path.seek(0)
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return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
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if __name__ == '__main__':
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# 启用则为直接切片合成，False为交叉淡化方式
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# vst插件调整0.3-0.5s切片时间可以降低延迟，直接切片方法会有连接处爆音、交叉淡化会有轻微重叠声音
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# 自行选择能接受的方法，或将vst最大切片时间调整为1s，此处设为Ture，延迟大音质稳定一些
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raw_infer = True
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# 每个模型和config是唯一对应的
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model_name = "logs/32k/G_174000-Copy1.pth"
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config_name = "configs/config.json"
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svc_model = Svc(model_name, config_name)
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svc = RealTimeVC()
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# 此处与vst插件对应，不建议更改
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app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
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