File size: 1,768 Bytes
4c9fe71 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
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)
|