63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import io
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import logging
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import librosa
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import soundfile
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from flask import Flask, request, send_file
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from flask_cors import CORS
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from infer_tools.infer_tool import Svc
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from utils.hparams import hparams
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app = Flask(__name__)
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CORS(app)
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logging.getLogger('numba').setLevel(logging.WARNING)
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@app.route("/voiceChangeModel", methods=["POST"])
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def voice_change_model():
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request_form = request.form
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wave_file = request.files.get("sample", None)
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# 变调信息
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f_pitch_change = float(request_form.get("fPitchChange", 0))
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# 获取spkid
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speak_id = int(request_form.get("sSpeakId", 0))
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if enable_spk_id_cover:
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speak_id = spk_id
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print("说话人:" + str(int_speak_Id))
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# DAW所需的采样率
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daw_sample = int(float(request_form.get("sampleRate", 0)))
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# http获得wav文件并转换
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input_wav_path = io.BytesIO(wave_file.read())
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# 模型推理
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_f0_tst, _f0_pred, _audio = svc_model.infer(input_wav_path, spk_id=speak_id, key=f_pitch_change, acc=accelerate,
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use_crepe=False)
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tar_audio = librosa.resample(_audio, hparams["audio_sample_rate"], daw_sample)
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# 返回音频
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out_wav_path = io.BytesIO()
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soundfile.write(out_wav_path, tar_audio, daw_sample, format="wav")
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out_wav_path.seek(0)
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return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
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if __name__ == '__main__':
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# 工程文件夹名，训练时用的那个
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project_name = "fox_cn"
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model_path = f'./checkpoints/{project_name}/clean_model_ckpt_steps_120000.ckpt'
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config_path = f'./checkpoints/{project_name}/config.yaml'
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# 默认说话人。以及是否优先使用默认说话人覆盖vst传入的参数。
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spk_id = 0
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enable_spk_id_cover = False
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# 加速倍数
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accelerate = 50
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hubert_gpu = True
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svc_model = Svc(project_name, config_path, hubert_gpu, model_path)
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# 此处与vst插件对应，不建议更改
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app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
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