503 lines
16 KiB
Python
503 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 random
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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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from hubert import hubert_model
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MATPLOTLIB_FLAG = False
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logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
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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, random_scale=True):
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# f0_norm = f0.clone() # create a copy of the input Tensor
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# batch_size, _, frame_length = f0_norm.shape
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# for i in range(batch_size):
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# means = torch.mean(f0_norm[i, 0, :])
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# if random_scale:
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# factor = random.uniform(0.8, 1.2)
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# else:
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# factor = 1
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# f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
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# return f0_norm
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# def normalize_f0(f0, random_scale=True):
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# means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
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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, 1).to(f0.device)
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# f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
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# return f0_norm
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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 interpolate_f0(f0):
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'''
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对F0进行插值处理
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'''
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data = np.reshape(f0, (f0.size, 1))
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vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
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vuv_vector[data > 0.0] = 1.0
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vuv_vector[data <= 0.0] = 0.0
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ip_data = data
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frame_number = data.size
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last_value = 0.0
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for i in range(frame_number):
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if data[i] <= 0.0:
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j = i + 1
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for j in range(i + 1, frame_number):
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if data[j] > 0.0:
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break
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if j < frame_number - 1:
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if last_value > 0.0:
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step = (data[j] - data[i - 1]) / float(j - i)
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for k in range(i, j):
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ip_data[k] = data[i - 1] + step * (k - i + 1)
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else:
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for k in range(i, j):
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ip_data[k] = data[j]
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else:
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for k in range(i, frame_number):
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ip_data[k] = last_value
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else:
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ip_data[i] = data[i]
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last_value = data[i]
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return ip_data[:,0], vuv_vector[:,0]
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def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
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import parselmouth
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x = wav_numpy
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if p_len is None:
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p_len = x.shape[0]//hop_length
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else:
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assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
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time_step = hop_length / sampling_rate * 1000
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f0_min = 50
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f0_max = 1100
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f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
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pad_size=(p_len - len(f0) + 1) // 2
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if(pad_size>0 or p_len - len(f0) - pad_size>0):
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f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
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return f0
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def resize_f0(x, target_len):
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source = np.array(x)
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source[source<0.001] = np.nan
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target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
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res = np.nan_to_num(target)
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return res
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def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
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import pyworld
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if p_len is None:
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p_len = wav_numpy.shape[0]//hop_length
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f0, t = pyworld.dio(
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wav_numpy.astype(np.double),
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fs=sampling_rate,
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f0_ceil=800,
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frame_period=1000 * hop_length / sampling_rate,
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)
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f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
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for index, pitch in enumerate(f0):
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f0[index] = round(pitch, 1)
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return resize_f0(f0, p_len)
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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).long() 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_hubert_model():
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vec_path = "hubert/checkpoint_best_legacy_500.pt"
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print("load model(s) from {}".format(vec_path))
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from fairseq import checkpoint_utils
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[vec_path],
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suffix="",
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)
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model = models[0]
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model.eval()
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return model
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def get_hubert_content(hmodel, wav_16k_tensor):
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feats = wav_16k_tensor
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.to(wav_16k_tensor.device),
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"padding_mask": padding_mask.to(wav_16k_tensor.device),
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"output_layer": 9, # layer 9
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}
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with torch.no_grad():
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logits = hmodel.extract_features(**inputs)
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feats = hmodel.final_proj(logits[0])
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return feats.transpose(1, 2)
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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 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/base.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)
|
|
logger.addHandler(h)
|
|
return logger
|
|
|
|
|
|
def repeat_expand_2d(content, target_len):
|
|
# content : [h, t]
|
|
|
|
src_len = content.shape[-1]
|
|
target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
|
|
temp = torch.arange(src_len+1) * target_len / src_len
|
|
current_pos = 0
|
|
for i in range(target_len):
|
|
if i < temp[current_pos+1]:
|
|
target[:, i] = content[:, current_pos]
|
|
else:
|
|
current_pos += 1
|
|
target[:, i] = content[:, current_pos]
|
|
|
|
return target
|
|
|
|
|
|
class HParams():
|
|
def __init__(self, **kwargs):
|
|
for k, v in kwargs.items():
|
|
if type(v) == dict:
|
|
v = HParams(**v)
|
|
self[k] = v
|
|
|
|
def keys(self):
|
|
return self.__dict__.keys()
|
|
|
|
def items(self):
|
|
return self.__dict__.items()
|
|
|
|
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__()
|
|
|