import io import logging import librosa import soundfile from flask import Flask, request, send_file from flask_cors import CORS from infer_tools.infer_tool import Svc from utils.hparams import hparams app = Flask(__name__) CORS(app) logging.getLogger('numba').setLevel(logging.WARNING) @app.route("/voiceChangeModel", methods=["POST"]) 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 _f0_tst, _f0_pred, _audio = model.infer(input_wav_path, key=f_pitch_change, acc=accelerate, use_pe=False, use_crepe=False) tar_audio = librosa.resample(_audio, hparams["audio_sample_rate"], daw_sample) # 返回音频 out_wav_path = io.BytesIO() soundfile.write(out_wav_path, tar_audio, 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__': # 工程文件夹名,训练时用的那个 project_name = "firefox" model_path = f'./checkpoints/{project_name}/model_ckpt_steps_188000.ckpt' config_path = f'./checkpoints/{project_name}/config.yaml' # 加速倍数 accelerate = 50 hubert_gpu = True model = Svc(project_name, config_path, hubert_gpu, model_path) # 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)