import io import numpy as np import soundfile from flask import Flask, request, send_file from inference import infer_tool from inference import slicer app = Flask(__name__) @app.route("/wav2wav", methods=["POST"]) def wav2wav(): request_form = request.form audio_path = request_form.get("audio_path", None) # wav path tran = int(float(request_form.get("tran", 0))) # tone spk = request_form.get("spk", 0) # speaker(id or name) wav_format = request_form.get("wav_format", 'wav') infer_tool.format_wav(audio_path) chunks = slicer.cut(audio_path, db_thresh=-40) audio_data, audio_sr = slicer.chunks2audio(audio_path, chunks) audio = [] for (slice_tag, data) in audio_data: print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======') length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample)) if slice_tag: print('jump empty segment') _audio = np.zeros(length) else: # padd pad_len = int(audio_sr * 0.5) data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])]) raw_path = io.BytesIO() soundfile.write(raw_path, data, audio_sr, format="wav") raw_path.seek(0) out_audio, out_sr = svc_model.infer(spk, tran, raw_path) svc_model.clear_empty() _audio = out_audio.cpu().numpy() pad_len = int(svc_model.target_sample * 0.5) _audio = _audio[pad_len:-pad_len] audio.extend(list(infer_tool.pad_array(_audio, length))) out_wav_path = io.BytesIO() soundfile.write(out_wav_path, audio, svc_model.target_sample, format=wav_format) out_wav_path.seek(0) return send_file(out_wav_path, download_name=f"temp.{wav_format}", as_attachment=True) if __name__ == '__main__': model_name = "logs/44k/G_60000.pth" config_name = "configs/config.json" svc_model = infer_tool.Svc(model_name, config_name) app.run(port=1145, host="0.0.0.0", debug=False, threaded=False)