commit
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@ -102,6 +102,10 @@ def pad_array(arr, target_length):
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pad_right = pad_width - pad_left
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padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
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return padded_arr
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def split_list_by_n(list_collection, n, pre=0):
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for i in range(0, len(list_collection), n):
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yield list_collection[i-pre if i-pre>=0 else i: i + n]
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class Svc(object):
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@ -25,6 +25,7 @@ def main():
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# 一定要设置的部分
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parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
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parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
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parser.add_argument('-cl', '--clip', type=float, default=0, help='音频自动切片,0为不切片,单位为秒/s')
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parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表,放在raw文件夹下')
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parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整,支持正负(半音)')
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parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
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@ -34,6 +35,7 @@ def main():
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help='语音转换自动预测音高,转换歌声时不要打开这个会严重跑调')
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parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径,如果没有训练聚类则随便填')
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parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比,范围0-1,若没有训练聚类模型则填0即可')
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parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两段音频切片的交叉淡入长度,如果自动切片后出现人声不连贯可调整该数值,如果连贯建议采用默认值0,单位为秒/s')
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# 不用动的部分
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parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40,嘈杂的音频可以-30,干声保留呼吸可以-50')
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@ -41,6 +43,7 @@ def main():
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parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别,会影响咬字和音质,较为玄学')
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parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='推理音频pad秒数,由于未知原因开头结尾会有异响,pad一小段静音段后就不会出现')
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parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式')
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parser.add_argument('-lgr', '--linear_gradient_retain', type=float, default=0.75, help='自动音频切片后,需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例,范围0-1,左开右闭')
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args = parser.parse_args()
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@ -55,6 +58,9 @@ def main():
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cluster_infer_ratio = args.cluster_infer_ratio
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noice_scale = args.noice_scale
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pad_seconds = args.pad_seconds
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clip = args.clip
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lg = args.linear_gradient
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lgr = args.linear_gradient_retain
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infer_tool.fill_a_to_b(trans, clean_names)
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for clean_name, tran in zip(clean_names, trans):
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@ -65,22 +71,36 @@ def main():
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wav_path = Path(raw_audio_path).with_suffix('.wav')
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chunks = slicer.cut(wav_path, db_thresh=slice_db)
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audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
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per_size = int(clip*audio_sr)
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lg_size = int(lg*audio_sr)
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lg_size_r = int(lg_size*lgr)
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lg_size_c_l = (lg_size-lg_size_r)//2
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lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
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lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
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for spk in spk_list:
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audio = []
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for (slice_tag, data) in audio_data:
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print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
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length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
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if slice_tag:
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print('jump empty segment')
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_audio = np.zeros(length)
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audio.extend(list(infer_tool.pad_array(_audio, length)))
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continue
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if per_size != 0:
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datas = infer_tool.split_list_by_n(data, per_size,lg_size)
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else:
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datas = [data]
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for k,dat in enumerate(datas):
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per_length = int(np.ceil(len(dat) / audio_sr * svc_model.target_sample)) if clip!=0 else length
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if clip!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
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# padd
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pad_len = int(audio_sr * pad_seconds)
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data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
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dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
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raw_path = io.BytesIO()
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soundfile.write(raw_path, data, audio_sr, format="wav")
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soundfile.write(raw_path, dat, audio_sr, format="wav")
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raw_path.seek(0)
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out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
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cluster_infer_ratio=cluster_infer_ratio,
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_audio = out_audio.cpu().numpy()
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pad_len = int(svc_model.target_sample * pad_seconds)
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_audio = _audio[pad_len:-pad_len]
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audio.extend(list(infer_tool.pad_array(_audio, length)))
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_audio = infer_tool.pad_array(_audio, per_length)
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if lg_size!=0 and k!=0:
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lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr != 1 else audio[-lg_size:]
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lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr != 1 else _audio[0:lg_size]
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lg_pre = lg1*(1-lg)+lg2*lg
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audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr != 1 else audio[0:-lg_size]
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audio.extend(lg_pre)
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_audio = _audio[lg_size_c_l+lg_size_r:] if lgr != 1 else _audio[lg_size:]
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audio.extend(list(_audio))
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key = "auto" if auto_predict_f0 else f"{tran}key"
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cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
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res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'
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Reference in New Issue