117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
import json
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import os
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import shutil
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from functools import reduce
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from pathlib import Path
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import matplotlib
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import matplotlib.pyplot as plt
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import yaml
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from pylab import xticks, np
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from tqdm import tqdm
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from modules.vocoders.nsf_hifigan import NsfHifiGAN
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from preprocessing.process_pipeline import get_pitch_parselmouth, get_pitch_crepe
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from utils.hparams import set_hparams, hparams
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head_list = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
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def compare_pitch(f0_static_dict, pitch_time_temp, trans_key=0):
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return sum({k: v * f0_static_dict[str(k + trans_key)] for k, v in pitch_time_temp.items() if
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str(k + trans_key) in f0_static_dict}.values())
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def f0_to_pitch(ff):
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f0_pitch = 69 + 12 * np.log2(ff / 440)
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return round(f0_pitch, 0)
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def pitch_to_name(pitch):
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return f"{head_list[int(pitch % 12)]}{int(pitch / 12) - 1}"
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def get_f0(audio_path, crepe=False):
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wav, mel = NsfHifiGAN.wav2spec(audio_path)
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if crepe:
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f0, pitch_coarse = get_pitch_crepe(wav, mel, hparams)
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else:
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f0, pitch_coarse = get_pitch_parselmouth(wav, mel, hparams)
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return f0
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def merge_f0_dict(dict_list):
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def sum_dict(a, b):
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temp = dict()
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for key in a.keys() | b.keys():
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temp[key] = sum([d.get(key, 0) for d in (a, b)])
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return temp
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return reduce(sum_dict, dict_list)
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def collect_f0(f0):
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pitch_num = {}
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pitch_list = [f0_to_pitch(x) for x in f0[f0 > 0]]
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for key in pitch_list:
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pitch_num[key] = pitch_num.get(key, 0) + 1
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return pitch_num
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def static_f0_time(f0):
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if isinstance(f0, dict):
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pitch_num = merge_f0_dict({k: collect_f0(v) for k, v in f0.items()}.values())
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else:
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pitch_num = collect_f0(f0)
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static_pitch_time = {}
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sort_key = sorted(pitch_num.keys())
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for key in sort_key:
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static_pitch_time[key] = round(pitch_num[key] * hparams['hop_size'] / hparams['audio_sample_rate'], 2)
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return static_pitch_time
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def get_end_file(dir_path, end):
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file_lists = []
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for root, dirs, files in os.walk(dir_path):
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files = [f for f in files if f[0] != '.']
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dirs[:] = [d for d in dirs if d[0] != '.']
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for f_file in files:
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if f_file.endswith(end):
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file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
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return file_lists
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if __name__ == "__main__":
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# 给config文件增加f0_static统计音域
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config_path = "../training/config_nsf.yaml"
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hparams = set_hparams(config=config_path, exp_name='', infer=True, reset=True, hparams_str='', print_hparams=False)
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f0_dict = {}
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# 获取batch文件夹下所有wav文件
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wav_paths = get_end_file("../batch", "wav")
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# parselmouth获取f0
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with tqdm(total=len(wav_paths)) as p_bar:
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p_bar.set_description('Processing')
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for wav_path in wav_paths:
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f0_dict[wav_path] = get_f0(wav_path, crepe=False)
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p_bar.update(1)
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pitch_time = static_f0_time(f0_dict)
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total_time = round(sum(pitch_time.values()), 2)
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pitch_time["total_time"] = total_time
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print(f"total time: {total_time}s")
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shutil.copy(config_path, f"{Path(config_path).parent}\\back_{Path(config_path).name}")
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with open(config_path, encoding='utf-8') as f:
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_hparams = yaml.safe_load(f)
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_hparams['f0_static'] = json.dumps(pitch_time)
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with open(config_path, 'w', encoding='utf-8') as f:
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yaml.safe_dump(_hparams, f)
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print("原config文件已在原目录建立备份：back_config.yaml")
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print("音域统计已保存至config文件，此模型可使用自动变调功能")
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matplotlib.use('TkAgg')
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plt.title("数据集音域统计", fontproperties='SimHei')
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plt.xlabel("音高", fontproperties='SimHei')
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plt.ylabel("时长(s)", fontproperties='SimHei')
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xticks_labels = [pitch_to_name(i) for i in range(36, 96)]
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xticks(np.linspace(36, 96, 60, endpoint=True), xticks_labels)
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plt.plot(pitch_time.keys(), pitch_time.values(), color='dodgerblue')
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plt.show()
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