466 lines
16 KiB
Python
466 lines
16 KiB
Python
import os
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import glob
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import re
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import sys
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import argparse
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import logging
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import json
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import subprocess
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import warnings
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import random
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import functools
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import librosa
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import numpy as np
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from scipy.io.wavfile import read
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import torch
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from torch.nn import functional as F
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from modules.commons import sequence_mask
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MATPLOTLIB_FLAG = False
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logging.basicConfig(stream=sys.stdout, level=logging.WARN)
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logger = logging
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f0_bin = 256
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f0_max = 1100.0
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f0_min = 50.0
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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def normalize_f0(f0, x_mask, uv, random_scale=True):
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# calculate means based on x_mask
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uv_sum = torch.sum(uv, dim=1, keepdim=True)
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uv_sum[uv_sum == 0] = 9999
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means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
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if random_scale:
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factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
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else:
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factor = torch.ones(f0.shape[0], 1).to(f0.device)
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# normalize f0 based on means and factor
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f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
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if torch.isnan(f0_norm).any():
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exit(0)
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return f0_norm * x_mask
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def plot_data_to_numpy(x, y):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger('matplotlib')
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(10, 2))
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plt.plot(x)
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plt.plot(y)
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def f0_to_coarse(f0):
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is_torch = isinstance(f0, torch.Tensor)
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f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
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f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int)
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assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
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return f0_coarse
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def get_content(cmodel, y):
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with torch.no_grad():
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c = cmodel.extract_features(y.squeeze(1))[0]
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c = c.transpose(1, 2)
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return c
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def get_f0_predictor(f0_predictor,hop_length,sampling_rate,**kargs):
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if f0_predictor == "pm":
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from modules.F0Predictor.PMF0Predictor import PMF0Predictor
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f0_predictor_object = PMF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
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elif f0_predictor == "crepe":
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from modules.F0Predictor.CrepeF0Predictor import CrepeF0Predictor
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f0_predictor_object = CrepeF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate,device=kargs["device"],threshold=kargs["threshold"])
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elif f0_predictor == "harvest":
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from modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
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f0_predictor_object = HarvestF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
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elif f0_predictor == "dio":
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from modules.F0Predictor.DioF0Predictor import DioF0Predictor
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f0_predictor_object = DioF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
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else:
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raise Exception("Unknown f0 predictor")
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return f0_predictor_object
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def get_speech_encoder(speech_encoder,device=None,**kargs):
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if speech_encoder == "vec768l12":
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from vencoder.ContentVec768L12 import ContentVec768L12
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speech_encoder_object = ContentVec768L12(device = device)
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elif speech_encoder == "vec256l9":
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from vencoder.ContentVec256L9 import ContentVec256L9
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speech_encoder_object = ContentVec256L9(device = device)
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elif speech_encoder == "vec256l9-onnx":
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from vencoder.ContentVec256L9_Onnx import ContentVec256L9_Onnx
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speech_encoder_object = ContentVec256L9_Onnx(device = device)
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elif speech_encoder == "vec256l12-onnx":
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from vencoder.ContentVec256L12_Onnx import ContentVec256L12_Onnx
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speech_encoder_object = ContentVec256L12_Onnx(device = device)
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elif speech_encoder == "vec768l9-onnx":
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from vencoder.ContentVec768L9_Onnx import ContentVec768L9_Onnx
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speech_encoder_object = ContentVec768L9_Onnx(device = device)
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elif speech_encoder == "vec768l12-onnx":
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from vencoder.ContentVec768L12_Onnx import ContentVec768L12_Onnx
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speech_encoder_object = ContentVec768L12_Onnx(device = device)
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elif speech_encoder == "hubertsoft-onnx":
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from vencoder.HubertSoft_Onnx import HubertSoft_Onnx
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speech_encoder_object = HubertSoft_Onnx(device = device)
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elif speech_encoder == "hubertsoft":
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from vencoder.HubertSoft import HubertSoft
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speech_encoder_object = HubertSoft(device = device)
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elif speech_encoder == "whisper-ppg":
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from vencoder.WhisperPPG import WhisperPPG
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speech_encoder_object = WhisperPPG(device = device)
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else:
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raise Exception("Unknown speech encoder")
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return speech_encoder_object
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def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
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iteration = checkpoint_dict['iteration']
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learning_rate = checkpoint_dict['learning_rate']
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if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
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optimizer.load_state_dict(checkpoint_dict['optimizer'])
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saved_state_dict = checkpoint_dict['model']
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if hasattr(model, 'module'):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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new_state_dict = {}
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for k, v in state_dict.items():
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try:
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# assert "dec" in k or "disc" in k
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# print("load", k)
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new_state_dict[k] = saved_state_dict[k]
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assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
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except:
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print("error, %s is not in the checkpoint" % k)
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logger.info("%s is not in the checkpoint" % k)
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new_state_dict[k] = v
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if hasattr(model, 'module'):
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model.module.load_state_dict(new_state_dict)
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else:
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model.load_state_dict(new_state_dict)
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print("load ")
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logger.info("Loaded checkpoint '{}' (iteration {})".format(
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checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info("Saving model and optimizer state at iteration {} to {}".format(
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iteration, checkpoint_path))
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if hasattr(model, 'module'):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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torch.save({'model': state_dict,
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'iteration': iteration,
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'optimizer': optimizer.state_dict(),
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'learning_rate': learning_rate}, checkpoint_path)
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def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
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"""Freeing up space by deleting saved ckpts
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Arguments:
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path_to_models -- Path to the model directory
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n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
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sort_by_time -- True -> chronologically delete ckpts
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False -> lexicographically delete ckpts
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"""
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ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
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name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
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time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
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sort_key = time_key if sort_by_time else name_key
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x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
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to_del = [os.path.join(path_to_models, fn) for fn in
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(x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
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del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
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del_routine = lambda x: [os.remove(x), del_info(x)]
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rs = [del_routine(fn) for fn in to_del]
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def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
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for k, v in scalars.items():
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writer.add_scalar(k, v, global_step)
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for k, v in histograms.items():
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writer.add_histogram(k, v, global_step)
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for k, v in images.items():
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writer.add_image(k, v, global_step, dataformats='HWC')
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for k, v in audios.items():
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writer.add_audio(k, v, global_step, audio_sampling_rate)
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def latest_checkpoint_path(dir_path, regex="G_*.pth"):
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
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x = f_list[-1]
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print(x)
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return x
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def plot_spectrogram_to_numpy(spectrogram):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger('matplotlib')
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(10,2))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower",
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interpolation='none')
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plt.colorbar(im, ax=ax)
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plt.xlabel("Frames")
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plt.ylabel("Channels")
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def plot_alignment_to_numpy(alignment, info=None):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger('matplotlib')
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
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interpolation='none')
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fig.colorbar(im, ax=ax)
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xlabel = 'Decoder timestep'
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if info is not None:
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xlabel += '\n\n' + info
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plt.xlabel(xlabel)
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plt.ylabel('Encoder timestep')
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def load_wav_to_torch(full_path):
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sampling_rate, data = read(full_path)
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
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def load_filepaths_and_text(filename, split="|"):
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with open(filename, encoding='utf-8') as f:
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filepaths_and_text = [line.strip().split(split) for line in f]
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return filepaths_and_text
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def get_hparams(init=True):
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parser = argparse.ArgumentParser()
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parser.add_argument('-c', '--config', type=str, default="./configs/config.json",
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help='JSON file for configuration')
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parser.add_argument('-m', '--model', type=str, required=True,
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help='Model name')
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args = parser.parse_args()
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model_dir = os.path.join("./logs", args.model)
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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config_path = args.config
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config_save_path = os.path.join(model_dir, "config.json")
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if init:
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with open(config_path, "r") as f:
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data = f.read()
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with open(config_save_path, "w") as f:
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f.write(data)
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else:
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with open(config_save_path, "r") as f:
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data = f.read()
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config = json.loads(data)
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hparams = HParams(**config)
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hparams.model_dir = model_dir
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return hparams
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def get_hparams_from_dir(model_dir):
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config_save_path = os.path.join(model_dir, "config.json")
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with open(config_save_path, "r") as f:
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data = f.read()
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config = json.loads(data)
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hparams =HParams(**config)
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hparams.model_dir = model_dir
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return hparams
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def get_hparams_from_file(config_path):
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with open(config_path, "r") as f:
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data = f.read()
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config = json.loads(data)
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hparams =HParams(**config)
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return hparams
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def check_git_hash(model_dir):
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source_dir = os.path.dirname(os.path.realpath(__file__))
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if not os.path.exists(os.path.join(source_dir, ".git")):
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logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
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source_dir
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))
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return
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cur_hash = subprocess.getoutput("git rev-parse HEAD")
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path = os.path.join(model_dir, "githash")
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if os.path.exists(path):
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saved_hash = open(path).read()
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if saved_hash != cur_hash:
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logger.warn("git hash values are different. {}(saved) != {}(current)".format(
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saved_hash[:8], cur_hash[:8]))
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else:
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open(path, "w").write(cur_hash)
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def get_logger(model_dir, filename="train.log"):
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global logger
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logger = logging.getLogger(os.path.basename(model_dir))
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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h = logging.FileHandler(os.path.join(model_dir, filename))
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h.setLevel(logging.DEBUG)
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h.setFormatter(formatter)
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logger.addHandler(h)
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return logger
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def repeat_expand_2d(content, target_len):
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# content : [h, t]
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src_len = content.shape[-1]
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target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
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temp = torch.arange(src_len+1) * target_len / src_len
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current_pos = 0
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for i in range(target_len):
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if i < temp[current_pos+1]:
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target[:, i] = content[:, current_pos]
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else:
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current_pos += 1
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target[:, i] = content[:, current_pos]
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return target
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def mix_model(model_paths,mix_rate,mode):
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mix_rate = torch.FloatTensor(mix_rate)/100
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model_tem = torch.load(model_paths[0])
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models = [torch.load(path)["model"] for path in model_paths]
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if mode == 0:
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mix_rate = F.softmax(mix_rate,dim=0)
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for k in model_tem["model"].keys():
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model_tem["model"][k] = torch.zeros_like(model_tem["model"][k])
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for i,model in enumerate(models):
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model_tem["model"][k] += model[k]*mix_rate[i]
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torch.save(model_tem,os.path.join(os.path.curdir,"output.pth"))
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return os.path.join(os.path.curdir,"output.pth")
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def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比 from RVC
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# print(data1.max(),data2.max())
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rms1 = librosa.feature.rms(
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y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
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) # 每半秒一个点
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rms2 = librosa.feature.rms(y=data2.detach().cpu().numpy(), frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
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rms1 = torch.from_numpy(rms1).to(data2.device)
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rms1 = F.interpolate(
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rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
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).squeeze()
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rms2 = torch.from_numpy(rms2).to(data2.device)
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rms2 = F.interpolate(
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rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
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).squeeze()
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rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
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data2 *= (
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torch.pow(rms1, torch.tensor(1 - rate))
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* torch.pow(rms2, torch.tensor(rate - 1))
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)
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return data2
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class HParams():
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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if type(v) == dict:
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v = HParams(**v)
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self[k] = v
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def keys(self):
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return self.__dict__.keys()
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def items(self):
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return self.__dict__.items()
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def values(self):
|
||
return self.__dict__.values()
|
||
|
||
def __len__(self):
|
||
return len(self.__dict__)
|
||
|
||
def __getitem__(self, key):
|
||
return getattr(self, key)
|
||
|
||
def __setitem__(self, key, value):
|
||
return setattr(self, key, value)
|
||
|
||
def __contains__(self, key):
|
||
return key in self.__dict__
|
||
|
||
def __repr__(self):
|
||
return self.__dict__.__repr__()
|
||
|
||
def get(self,index):
|
||
return self.__dict__.get(index)
|
||
|
||
class Volume_Extractor:
|
||
def __init__(self, hop_size = 512):
|
||
self.hop_size = hop_size
|
||
|
||
def extract(self, audio): # audio: 2d tensor array
|
||
if not isinstance(audio,torch.Tensor):
|
||
audio = torch.Tensor(audio)
|
||
n_frames = int(audio.size(-1) // self.hop_size)
|
||
audio2 = audio ** 2
|
||
audio2 = torch.nn.functional.pad(audio2, (int(self.hop_size // 2), int((self.hop_size + 1) // 2)), mode = 'reflect')
|
||
volume = torch.FloatTensor([torch.mean(audio2[:,int(n * self.hop_size) : int((n + 1) * self.hop_size)]) for n in range(n_frames)])
|
||
volume = torch.sqrt(volume)
|
||
return volume |