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| import io | |
| import logging | |
| import soundfile | |
| import torch | |
| import torchaudio | |
| from flask import Flask, request, send_file | |
| from flask_cors import CORS | |
| from inference.infer_tool import Svc, RealTimeVC | |
| app = Flask(__name__) | |
| CORS(app) | |
| logging.getLogger('numba').setLevel(logging.WARNING) | |
| def voice_change_model(): | |
| request_form = request.form | |
| wave_file = request.files.get("sample", None) | |
| # 变调信息 | |
| f_pitch_change = float(request_form.get("fPitchChange", 0)) | |
| # DAW所需的采样率 | |
| daw_sample = int(float(request_form.get("sampleRate", 0))) | |
| speaker_id = int(float(request_form.get("sSpeakId", 0))) | |
| # http获得wav文件并转换 | |
| input_wav_path = io.BytesIO(wave_file.read()) | |
| # 模型推理 | |
| if raw_infer: | |
| out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path) | |
| tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample) | |
| else: | |
| out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path) | |
| tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample) | |
| # 返回音频 | |
| out_wav_path = io.BytesIO() | |
| soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav") | |
| out_wav_path.seek(0) | |
| return send_file(out_wav_path, download_name="temp.wav", as_attachment=True) | |
| if __name__ == '__main__': | |
| # 启用则为直接切片合成，False为交叉淡化方式 | |
| # 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 | |
| # 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 | |
| raw_infer = True | |
| # 每个模型和config是唯一对应的 | |
| model_name = "logs/32k/G_174000-Copy1.pth" | |
| config_name = "configs/config.json" | |
| svc_model = Svc(model_name, config_name) | |
| svc = RealTimeVC() | |
| # 此处与vst插件对应，不建议更改 | |
| app.run(port=6842, host="0.0.0.0", debug=False, threaded=False) | |