import io import logging import time from pathlib import Path import librosa import matplotlib.pyplot as plt import numpy as np import soundfile from inference import infer_tool from inference import slicer from inference.infer_tool import Svc logging.getLogger('numba').setLevel(logging.WARNING) chunks_dict = infer_tool.read_temp("inference/chunks_temp.json") def stitch_wav_tensors_with_crossfade(wav_tensor1,wav_tensor2,sample_rate:int): # Load two audio signals as NumPy tensors # Compute the length of the cross-fade window in samples fade_len = int(0.01 * sample_rate) # Create a cross-fade window fade_window = np.hanning(2*fade_len) # Concatenate the signals with cross-fade audio_out = np.concatenate((wav_tensor1[:-fade_len], wav_tensor1[-fade_len:] * fade_window[:fade_len] + wav_tensor2[:fade_len] * fade_window[fade_len:], wav_tensor2[fade_len:])) #sf.write("audio_out.wav", audio_out, sample_rate) return audio_out def main(): import argparse parser = argparse.ArgumentParser(description='sovits4 inference') # 一定要设置的部分 parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径') parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径') 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') parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）') parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称') # 可选项部分 parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False, 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') 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') 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') # 不用动的部分 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') parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu') 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') 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') parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式') args = parser.parse_args() svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path) infer_tool.mkdir(["raw", "results"]) clean_names = args.clean_names trans = args.trans spk_list = args.spk_list slice_db = args.slice_db wav_format = args.wav_format auto_predict_f0 = args.auto_predict_f0 cluster_infer_ratio = args.cluster_infer_ratio noice_scale = args.noice_scale pad_seconds = args.pad_seconds infer_tool.fill_a_to_b(trans, clean_names) for clean_name, tran in zip(clean_names, trans): raw_audio_path = f"raw/{clean_name}" if "." not in raw_audio_path: raw_audio_path += ".wav" infer_tool.format_wav(raw_audio_path) wav_path = Path(raw_audio_path).with_suffix('.wav') chunks = slicer.cut(wav_path, db_thresh=slice_db) audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks) for spk in spk_list: 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) elif length >= 15: # pad pad_len = int(audio_sr * pad_seconds) # if chunk size is too large more than 15 seconds break it into smaller pieces because sliced by large amounts # final piece final_cross_faded_chunk_wav_tensor = None # chunk into smaller pieces cross fade # Set the length of each chunk in seconds chunk_length = 15 # Calculate the number of samples in each chunk chunk_samples = chunk_length * audio_sr # Calculate the total number of chunks total_chunks = int(len(data) / chunk_samples) # Calculate the number of remaining samples remaining_samples = len(data) - (total_chunks * chunk_samples) # Add an additional chunk for any remaining samples if remaining_samples > 0: total_chunks += 1 # Slice the WAV tensor into chunks chunks = [data[i * chunk_samples:min((i + 1) * chunk_samples, len(data))] for i in range(total_chunks)] for chunk in chunks: # prepare for inference data = np.concatenate([np.zeros([pad_len]), chunk, np.zeros([pad_len])]) # Testing writes to the audio to do a match up. soundfile.write(raw_path, data, audio_sr, format="wav") raw_path.seek(0) out_audio, out_sr = svc_model.infer(spk, tran, raw_path, cluster_infer_ratio=cluster_infer_ratio, auto_predict_f0=auto_predict_f0, noice_scale=noice_scale ) #trim padded audio by new sample length and cross fade pad_len = int(svc_model.target_sample * pad_seconds) _audio = _audio[pad_len:-pad_len] _audio = out_audio.cpu().numpy() if final_cross_faded_chunk_wav_tensor == None: final_cross_faded_chunk_wav_tensor = _audio else: final_cross_faded_chunk_wav_tensor = stitch_wav_tensors_with_crossfade(wav_tensor1=final_cross_faded_chunk_wav_tensor,wav_tensor2=_audio,sample_rate=svc_model.target_sample) _audio = final_cross_faded_chunk_wav_tensor else: 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, cluster_infer_ratio=cluster_infer_ratio, auto_predict_f0=auto_predict_f0, noice_scale=noice_scale ) _audio = out_audio.cpu().numpy() #trim padded audio by new sample length pad_len = int(svc_model.target_sample * pad_seconds) # trim padded both sides with new sample ra _audio = _audio[pad_len:-pad_len] audio.extend(list(infer_tool.pad_array(_audio, length))) key = "auto" if auto_predict_f0 else f"{tran}key" cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}" res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}' soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format) if __name__ == '__main__': main()