| 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) |
| tran = int(float(request_form.get("tran", 0))) |
| spk = request_form.get("spk", 0) |
| 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: |
| |
| 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) |
|
|