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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)
@app.route("/voiceChangeModel", methods=["POST"])
def voice_change_model():
request_form = request.form
wave_file = request.files.get("sample", None)
# pitch changing information
f_pitch_change = float(request_form.get("fPitchChange", 0))
# DAW required sampling rate
daw_sample = int(float(request_form.get("sampleRate", 0)))
speaker_id = int(float(request_form.get("sSpeakId", 0)))
# get wav from http and convert
input_wav_path = io.BytesIO(wave_file.read())
# inference
if raw_infer:
# out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
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, cluster_infer_ratio=0,
auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
# return
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__':
# True means splice directly. There may be explosive sounds at the splice.
# False means use cross fade. There may be slight overlapping sounds at the splice.
# Using 0.3-0.5s in VST plugin can reduce latency.
# You can adjust the maximum slicing time of VST plugin to 1 second and set it to ture here to get a stable sound quality and a relatively large delay。
# Choose an acceptable method on your own.
raw_infer = True
# each model and config are corresponding
model_name = "logs/32k/G_174000-Copy1.pth"
config_name = "configs/config.json"
cluster_model_path = "logs/44k/kmeans_10000.pt"
svc_model = Svc(model_name, config_name, cluster_model_path=cluster_model_path)
svc = RealTimeVC()
# corresponding to the vst plugin here
app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
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