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import sys
import traceback
import logging
logger = logging.getLogger(__name__)
from functools import lru_cache
from time import time as ttime
import faiss
import librosa
import numpy as np
import parselmouth
import pyworld
import torch
import torch.nn.functional as F
import torchcrepe
from scipy import signal
from typing import Optional
now_dir = os.getcwd()
sys.path.append(now_dir)
# 导入彩色日志
try:
from lib.logger import log
except ImportError:
log = None
from lib.audio import soft_clip
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
input_audio_path2wav = {}
@lru_cache
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
audio = input_audio_path2wav[input_audio_path]
f0, t = pyworld.harvest(
audio,
fs=fs,
f0_ceil=f0max,
f0_floor=f0min,
frame_period=frame_period,
)
f0 = pyworld.stonemask(audio, f0, t, fs)
return f0
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
rms1 = librosa.feature.rms(
y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
) # 每半秒一个点
rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
rms1 = torch.from_numpy(rms1)
rms1 = F.interpolate(
rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.from_numpy(rms2)
rms2 = F.interpolate(
rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
gain = torch.pow(rms1, torch.tensor(1 - rate)) * torch.pow(rms2, torch.tensor(rate - 1))
# Reduced upper clamp: 4.0x over-amplifies noise in quiet sections,
# producing buzzy/electronic artifacts. 2.0x is sufficient for RMS matching.
gain = torch.clamp(gain, 0.3, 2.0)
data2 *= gain.numpy()
return data2
def repair_f0(
f0: np.ndarray,
max_gap: int = 6,
mask: Optional[np.ndarray] = None,
min_mask_ratio: float = 0.6,
) -> np.ndarray:
"""Fill short unvoiced gaps in F0 to reduce crack/tearing artifacts."""
if f0 is None or len(f0) == 0:
return f0
f0 = np.nan_to_num(f0, nan=0.0).astype(np.float32, copy=False)
voiced = f0 > 0
if voiced.sum() < 2:
return f0
if mask is not None:
mask = mask.astype(bool, copy=False)
if len(mask) < len(f0):
mask = np.pad(mask, (0, len(f0) - len(mask)), mode="edge")
else:
mask = mask[: len(f0)]
x = np.arange(len(f0))
interp = np.interp(x, x[voiced], f0[voiced])
zero_idx = np.where(~voiced)[0]
if zero_idx.size == 0:
return f0
run_start = zero_idx[0]
prev = zero_idx[0]
for idx in zero_idx[1:]:
if idx == prev + 1:
prev = idx
continue
run_end = prev
run_len = run_end - run_start + 1
if run_len <= max_gap and run_start > 0 and run_end < len(f0) - 1:
if mask is None or (mask[run_start : run_end + 1].mean() >= min_mask_ratio):
f0[run_start : run_end + 1] = interp[run_start : run_end + 1]
run_start = idx
prev = idx
run_end = prev
run_len = run_end - run_start + 1
if run_len <= max_gap and run_start > 0 and run_end < len(f0) - 1:
if mask is None or (mask[run_start : run_end + 1].mean() >= min_mask_ratio):
f0[run_start : run_end + 1] = interp[run_start : run_end + 1]
return f0
def _normalize_rmvpe_hybrid_mode(mode: Optional[str]) -> str:
"""Normalize user-facing hybrid mode aliases to internal fallback modes."""
normalized = str(mode or "off").strip().lower()
if normalized in {"", "off", "none", "strict", "official", "rmvpe_strict", "rmvpe-strict", "raw", "rmvpe"}:
return "off"
if normalized in {
"fallback",
"smart",
"rmvpe+fallback",
"rmvpe_fallback",
"rmvpe-fallback",
"hybrid_fallback",
"hybrid-fallback",
"hybrid",
"auto",
"harvest",
"harvest_fallback",
"harvest-fallback",
}:
return "fallback"
return normalized
def _build_protect_mix_curve(pitchf: torch.Tensor, protect: float) -> torch.Tensor:
"""Create a smooth protect curve for voiced/unvoiced transitions."""
protect = float(np.clip(protect, 0.0, 1.0))
if protect >= 1.0:
return torch.ones_like(pitchf, dtype=torch.float32)
voiced = (pitchf > 0).detach().float().cpu().numpy()
if voiced.ndim == 2:
voiced_curve = voiced[0]
else:
voiced_curve = voiced.reshape(-1)
smooth_kernel = np.array([1, 2, 3, 2, 1], dtype=np.float32)
smooth_kernel /= np.sum(smooth_kernel)
voiced_curve = np.convolve(voiced_curve, smooth_kernel, mode="same")
voiced_curve = np.convolve(voiced_curve, smooth_kernel, mode="same")
voiced_curve = np.clip(voiced_curve, 0.0, 1.0)
mix_curve = protect + (1.0 - protect) * voiced_curve
mix_curve = torch.from_numpy(mix_curve.astype(np.float32)).to(pitchf.device)
if pitchf.ndim == 2:
mix_curve = mix_curve.unsqueeze(0)
return mix_curve
def _compute_energy_mask(
audio: np.ndarray,
hop_length: int,
frame_length: int = 1024,
threshold_db: float = -50.0,
) -> np.ndarray:
"""Return frames considered voiced based on RMS energy."""
if audio is None or len(audio) == 0:
return np.zeros(0, dtype=bool)
rms = librosa.feature.rms(
y=audio, frame_length=frame_length, hop_length=hop_length, center=True
)[0]
if rms.size == 0:
return np.zeros(0, dtype=bool)
rms_db = 20 * np.log10(rms + 1e-6)
ref_db = np.percentile(rms_db, 95)
gate_db = ref_db + threshold_db
return rms_db >= gate_db
def _compute_harvest_f0(
audio: np.ndarray,
sr: int,
f0_min: float,
f0_max: float,
frame_period: float = 10.0,
) -> np.ndarray:
"""Compute Harvest F0 for fallback filling."""
audio = audio.astype(np.double, copy=False)
f0, t = pyworld.harvest(
audio,
fs=sr,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=frame_period,
)
f0 = pyworld.stonemask(audio, f0, t, sr)
return f0
def _compute_crepe_f0(
audio: np.ndarray,
sr: int,
hop_length: int,
f0_min: float,
f0_max: float,
device: str,
periodicity_threshold: float = 0.1,
return_periodicity: bool = False,
) -> np.ndarray:
"""Compute CREPE F0 for fallback filling."""
audio_tensor = torch.tensor(np.copy(audio))[None].float()
f0, pd = torchcrepe.predict(
audio_tensor,
sr,
hop_length,
f0_min,
f0_max,
"full",
batch_size=512,
device=device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0 = f0[0].cpu().numpy()
pd = pd[0].cpu().numpy()
if periodicity_threshold is not None:
f0[pd < periodicity_threshold] = 0
if return_periodicity:
return f0, pd
return f0
def _stabilize_f0(
f0: np.ndarray,
max_semitones: float = 6.0,
window: int = 2,
octave_fix: bool = True,
) -> tuple[np.ndarray, int, int]:
"""Stabilize F0 by correcting octave errors and extreme jumps."""
if f0 is None or len(f0) == 0:
return f0, 0, 0
f0 = np.nan_to_num(f0, nan=0.0).astype(np.float32, copy=True)
voiced_idx = np.where(f0 > 0)[0]
if voiced_idx.size < 3:
return f0, 0, 0
win = max(1, int(window))
max_semi = float(max_semitones)
eps = 1e-6
octave_fix_count = 0
outlier_count = 0
for i in voiced_idx:
start = max(0, i - win)
end = min(len(f0), i + win + 1)
neighbors = f0[start:end]
neighbors = neighbors[neighbors > 0]
if neighbors.size < 3:
continue
med = float(np.median(neighbors))
if med <= 0:
continue
if octave_fix:
ratio = f0[i] / (med + eps)
if 1.9 < ratio < 2.1:
f0[i] = f0[i] * 0.5
octave_fix_count += 1
elif 0.48 < ratio < 0.52:
f0[i] = f0[i] * 2.0
octave_fix_count += 1
if max_semi > 0:
semi_diff = 12.0 * abs(np.log2((f0[i] + eps) / (med + eps)))
if semi_diff > max_semi:
f0[i] = med
outlier_count += 1
return f0, octave_fix_count, outlier_count
def _limit_f0_slope(
f0: np.ndarray,
max_semitones: float = 8.0,
) -> tuple[np.ndarray, int]:
"""Limit frame-to-frame pitch jumps to reduce harsh transitions."""
if f0 is None or len(f0) == 0:
return f0, 0
f0 = np.nan_to_num(f0, nan=0.0).astype(np.float32, copy=True)
max_semi = float(max_semitones)
if max_semi <= 0:
return f0, 0
max_ratio = 2 ** (max_semi / 12.0)
min_ratio = 1.0 / max_ratio
changed = 0
prev = None
for i in range(len(f0)):
if f0[i] <= 0:
continue
if prev is None:
prev = f0[i]
continue
ratio = f0[i] / (prev + 1e-6)
if ratio > max_ratio:
f0[i] = prev * max_ratio
changed += 1
elif ratio < min_ratio:
f0[i] = prev * min_ratio
changed += 1
prev = f0[i]
return f0, changed
class Pipeline(object):
def __init__(self, tgt_sr, config):
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
config.x_pad,
config.x_query,
config.x_center,
config.x_max,
config.is_half,
)
self.disable_chunking = bool(getattr(config, "disable_chunking", False))
self.sr = 16000 # hubert输入采样率
self.window = 160 # 每帧点数
self.t_pad = self.sr * self.x_pad # 每条前后pad时间
self.t_pad_tgt = tgt_sr * self.x_pad
self.t_pad2 = self.t_pad * 2
self.t_query = self.sr * self.x_query # 查询切点前后查询时间
self.t_center = self.sr * self.x_center # 查询切点位置
self.t_max = self.sr * self.x_max # 免查询时长阈值
self.device = config.device
self.f0_min = float(getattr(config, "f0_min", 50))
self.f0_max = float(getattr(config, "f0_max", 1100))
if self.f0_max <= self.f0_min:
self.f0_max = max(self.f0_min + 1.0, 1100.0)
self.rmvpe_threshold = float(getattr(config, "rmvpe_threshold", 0.02))
self.f0_energy_threshold_db = float(getattr(config, "f0_energy_threshold_db", -50))
self.f0_hybrid_mode = _normalize_rmvpe_hybrid_mode(
getattr(config, "f0_hybrid_mode", "off")
)
self.rmvpe_strict_modes = {
"",
"off",
"none",
"strict",
"official",
"rmvpe_strict",
"rmvpe-strict",
}
self.rmvpe_fallback_modes = {
"fallback",
"smart",
"rmvpe+fallback",
"rmvpe_fallback",
"rmvpe-fallback",
"hybrid_fallback",
"hybrid-fallback",
}
self.crepe_pd_threshold = float(getattr(config, "crepe_pd_threshold", 0.1))
self.crepe_force_ratio = float(getattr(config, "crepe_force_ratio", 0.05))
self.crepe_replace_semitones = float(getattr(config, "crepe_replace_semitones", 0.0))
self.f0_fallback_context_radius = int(getattr(config, "f0_fallback_context_radius", 24))
self.f0_fallback_repair_gap = int(getattr(config, "f0_fallback_repair_gap", 12))
self.f0_fallback_post_gap = int(getattr(config, "f0_fallback_post_gap", 10))
self.f0_fallback_use_crepe = bool(getattr(config, "f0_fallback_use_crepe", True))
self.f0_fallback_crepe_max_ratio = float(getattr(config, "f0_fallback_crepe_max_ratio", 0.02))
self.f0_fallback_crepe_max_frames = int(getattr(config, "f0_fallback_crepe_max_frames", 320))
self.f0_stabilize = bool(getattr(config, "f0_stabilize", False))
self.f0_stabilize_window = int(getattr(config, "f0_stabilize_window", 2))
self.f0_stabilize_max_semitones = float(
getattr(config, "f0_stabilize_max_semitones", 6.0)
)
self.f0_stabilize_octave = bool(getattr(config, "f0_stabilize_octave", True))
self.f0_rate_limit = bool(getattr(config, "f0_rate_limit", False))
self.f0_rate_limit_semitones = float(
getattr(config, "f0_rate_limit_semitones", 8.0)
)
if self.crepe_force_ratio < 0:
self.crepe_force_ratio = 0.0
if self.crepe_pd_threshold < 0:
self.crepe_pd_threshold = 0.0
if self.crepe_replace_semitones < 0:
self.crepe_replace_semitones = 0.0
if self.f0_fallback_context_radius < 1:
self.f0_fallback_context_radius = 1
if self.f0_fallback_repair_gap < 0:
self.f0_fallback_repair_gap = 0
if self.f0_fallback_post_gap < 0:
self.f0_fallback_post_gap = 0
if self.f0_fallback_crepe_max_ratio < 0:
self.f0_fallback_crepe_max_ratio = 0.0
if self.f0_fallback_crepe_max_frames < 0:
self.f0_fallback_crepe_max_frames = 0
if self.f0_stabilize_window < 1:
self.f0_stabilize_window = 1
if self.f0_stabilize_max_semitones < 0:
self.f0_stabilize_max_semitones = 0.0
if self.f0_rate_limit_semitones < 0:
self.f0_rate_limit_semitones = 0.0
if log:
log.detail(f"Pipeline初始化: 目标采样率={tgt_sr}Hz")
log.detail(f"设备: {self.device}, 半精度: {self.is_half}")
log.detail(f"x_pad={self.x_pad}, x_query={self.x_query}, x_center={self.x_center}, x_max={self.x_max}")
log.detail(f"禁用分段: {self.disable_chunking}")
log.detail(f"F0范围: {self.f0_min}-{self.f0_max}Hz, RMVPE阈值: {self.rmvpe_threshold}")
log.detail(
f"F0混合: {self.f0_hybrid_mode}, CREPE阈值: {self.crepe_pd_threshold}, "
f"强制比率: {self.crepe_force_ratio}, 替换阈值(半音): {self.crepe_replace_semitones}"
)
log.detail(
f"F0兜底: 上下文半径={self.f0_fallback_context_radius}, "
f"预修补长度={self.f0_fallback_repair_gap}, 后修补长度={self.f0_fallback_post_gap}, "
f"CREPE兜底={self.f0_fallback_use_crepe}, "
f"CREPE最大占比={self.f0_fallback_crepe_max_ratio:.2%}, "
f"CREPE最大帧数={self.f0_fallback_crepe_max_frames}"
)
log.detail(
"RMVPE兜底: "
f"{'on' if self.f0_hybrid_mode in self.rmvpe_fallback_modes else 'off'}"
)
log.detail(
f"F0稳定器: {self.f0_stabilize}, 窗口: {self.f0_stabilize_window}, "
f"最大跳变(半音): {self.f0_stabilize_max_semitones}, "
f"八度修正: {self.f0_stabilize_octave}"
)
log.detail(
f"F0限速: {self.f0_rate_limit}, 最大跳变/帧(半音): {self.f0_rate_limit_semitones}"
)
def get_f0(
self,
input_audio_path,
x,
p_len,
f0_up_key,
f0_method,
filter_radius,
inp_f0=None,
):
global input_audio_path2wav
time_step = self.window / self.sr * 1000
f0_min = self.f0_min
f0_max = self.f0_max
# Mel quantization range MUST match training (50-1100Hz) regardless of
# extraction range, otherwise pitch embedding indices shift and the
# model produces degraded output on all notes.
f0_mel_min = 1127 * np.log(1 + 50.0 / 700)
f0_mel_max = 1127 * np.log(1 + 1100.0 / 700)
if log:
log.progress(f"提取F0: 方法={f0_method}")
log.detail(f"时间步长: {time_step:.2f}ms, F0范围: {f0_min}-{f0_max}Hz")
log.detail(f"音频长度: {len(x)} 样本, p_len: {p_len}")
# 将hybrid映射到rmvpe+crepe模式
if f0_method == "hybrid":
f0_method = "rmvpe"
# 临时设置hybrid模式
original_hybrid_mode = self.f0_hybrid_mode
self.f0_hybrid_mode = "rmvpe+crepe"
restore_hybrid_mode = True
else:
restore_hybrid_mode = False
if f0_method == "pm":
if log:
log.detail("使用Parselmouth提取F0...")
f0 = (
parselmouth.Sound(x, self.sr)
.to_pitch_ac(
time_step=time_step / 1000,
voicing_threshold=0.6,
pitch_floor=f0_min,
pitch_ceiling=f0_max,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
f0 = np.pad(
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
)
if log:
log.detail(f"PM F0提取完成: shape={f0.shape}")
elif f0_method == "harvest":
if log:
log.detail("使用PyWorld Harvest提取F0...")
input_audio_path2wav[input_audio_path] = x.astype(np.double)
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
if filter_radius > 2:
f0 = signal.medfilt(f0, 3)
if log:
log.detail(f"应用中值滤波: radius={filter_radius}")
if log:
log.detail(f"Harvest F0提取完成: shape={f0.shape}")
elif f0_method == "crepe":
if log:
log.detail("使用CREPE提取F0...")
model = "full"
# Pick a batch size that doesn't cause memory errors on your gpu
batch_size = 512
if log:
log.detail(f"CREPE模型: {model}, batch_size: {batch_size}")
# Compute pitch using first gpu
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.sr,
self.window,
f0_min,
f0_max,
model,
batch_size=batch_size,
device=self.device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0[pd < 0.1] = 0
f0 = f0[0].cpu().numpy()
if log:
log.detail(f"CREPE F0提取完成: shape={f0.shape}")
elif f0_method == "rmvpe":
if self.f0_hybrid_mode in ("crepe", "crepe_only", "crepe-only"):
if log:
log.detail("使用CREPE全量F0 (质量优先)...")
f0 = _compute_crepe_f0(
x,
self.sr,
self.window,
f0_min,
f0_max,
self.device,
periodicity_threshold=self.crepe_pd_threshold,
)
if log:
log.detail(f"CREPE F0提取完成: shape={f0.shape}")
else:
if log:
log.detail("使用RMVPE提取F0...")
if not hasattr(self, "model_rmvpe"):
from infer.lib.rmvpe import RMVPE
rmvpe_path = "%s/rmvpe.pt" % os.environ["rmvpe_root"]
logger.info(
"Loading rmvpe model,%s" % rmvpe_path
)
if log:
log.model(f"加载RMVPE模型: {rmvpe_path}")
self.model_rmvpe = RMVPE(
rmvpe_path,
is_half=self.is_half,
device=self.device,
)
if log:
log.success("RMVPE模型加载完成")
# Slightly lower threshold to reduce short unvoiced dropouts
f0 = self.model_rmvpe.infer_from_audio(x, thred=self.rmvpe_threshold)
if log:
log.detail(f"RMVPE F0提取完成: shape={f0.shape}")
if "privateuseone" in str(self.device): # clean ortruntime memory
del self.model_rmvpe.model
del self.model_rmvpe
logger.info("Cleaning ortruntime memory")
if log:
log.detail("清理ONNX Runtime内存")
if self.f0_hybrid_mode in ("rmvpe+crepe", "rmvpe_crepe", "hybrid", "rmvpe-crepe"):
if log:
log.detail("启用RMVPE+CREPE混合F0 (质量优先)...")
crepe_f0, crepe_pd = _compute_crepe_f0(
x,
self.sr,
self.window,
f0_min,
f0_max,
self.device,
periodicity_threshold=self.crepe_pd_threshold,
return_periodicity=True,
)
if len(crepe_f0) < len(f0):
crepe_f0 = np.pad(crepe_f0, (0, len(f0) - len(crepe_f0)), mode="edge")
crepe_pd = np.pad(crepe_pd, (0, len(f0) - len(crepe_pd)), mode="edge")
else:
crepe_f0 = crepe_f0[: len(f0)]
crepe_pd = crepe_pd[: len(f0)]
crepe_mask = crepe_f0 > 0
drop_ratio = float(np.sum(f0 <= 0)) / max(len(f0), 1)
replace_mask = (f0 <= 0) & crepe_mask
if drop_ratio >= self.crepe_force_ratio:
replace_mask = crepe_mask
if self.crepe_replace_semitones > 0:
both_voiced = (f0 > 0) & crepe_mask
if np.any(both_voiced):
diff_semi = np.zeros_like(f0, dtype=np.float32)
diff_semi[both_voiced] = np.abs(
12.0
* np.log2(
(f0[both_voiced] + 1e-6) / (crepe_f0[both_voiced] + 1e-6)
)
)
replace_mask |= both_voiced & (diff_semi >= self.crepe_replace_semitones)
replaced = int(np.sum(replace_mask))
f0[replace_mask] = crepe_f0[replace_mask]
if log:
log.detail(
f"CREPE混合完成: 掉线比率={drop_ratio:.2%}, "
f"替换帧={replaced}/{len(f0)}"
)
f0 *= pow(2, f0_up_key / 12)
if log:
log.detail(f"应用音调偏移: {f0_up_key} 半音, 倍率: {pow(2, f0_up_key / 12):.4f}")
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
tf0 = self.sr // self.window # 每秒f0点数
if inp_f0 is not None:
if log:
log.detail("应用自定义F0曲线...")
delta_t = np.round(
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
).astype("int16")
replace_f0 = np.interp(
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
)
shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
:shape
]
else:
use_rmvpe_fallback = (
f0_method == "rmvpe"
and self.f0_hybrid_mode not in self.rmvpe_strict_modes
and self.f0_hybrid_mode in self.rmvpe_fallback_modes
)
if use_rmvpe_fallback:
energy_mask = _compute_energy_mask(
x, hop_length=self.window, threshold_db=self.f0_energy_threshold_db
)
if energy_mask.size > 0:
if len(energy_mask) < len(f0):
energy_mask = np.pad(
energy_mask, (0, len(f0) - len(energy_mask)), mode="edge"
)
else:
energy_mask = energy_mask[: len(f0)]
else:
energy_mask = None
# Repair short unvoiced gaps only when fallback mode is explicitly enabled.
f0 = repair_f0(
f0,
max_gap=self.f0_fallback_repair_gap,
mask=energy_mask,
)
# Conservative F0 fallback:
# only fill dropouts that are surrounded by voiced context.
if energy_mask is not None:
voiced_seed = f0 > 0
if np.any(voiced_seed):
idx = np.arange(len(f0))
left_seen = np.where(voiced_seed, idx, -10**9)
left_seen = np.maximum.accumulate(left_seen)
right_seen = np.where(voiced_seed, idx, 10**9)
right_seen = np.minimum.accumulate(right_seen[::-1])[::-1]
context_radius = self.f0_fallback_context_radius
left_near = (idx - left_seen) <= context_radius
right_near = (right_seen - idx) <= context_radius
voiced_context = left_near & right_near
else:
voiced_context = np.zeros_like(f0, dtype=bool)
need_fill = (f0 <= 0) & energy_mask & voiced_context
if np.any(need_fill):
if log:
log.detail(
f"RMVPE掉线帧(主唱上下文): {int(need_fill.sum())}/{len(f0)}，启用保守兜底"
)
f0_min_fb = max(30.0, f0_min - 20.0)
f0_max_fb = min(1800.0, f0_max + 200.0)
f0_fb = _compute_harvest_f0(x, self.sr, f0_min_fb, f0_max_fb, 10.0)
if len(f0_fb) < len(f0):
f0_fb = np.pad(f0_fb, (0, len(f0) - len(f0_fb)), mode="edge")
else:
f0_fb = f0_fb[: len(f0)]
fill_mask = need_fill & (f0_fb > 0)
f0[fill_mask] = f0_fb[fill_mask]
need_fill2 = (f0 <= 0) & energy_mask & voiced_context
need_fill2_count = int(np.sum(need_fill2))
need_fill2_ratio = float(need_fill2_count) / max(len(f0), 1)
if np.any(need_fill2) and self.f0_fallback_use_crepe:
allow_crepe_fallback = (
need_fill2_count <= self.f0_fallback_crepe_max_frames
and need_fill2_ratio <= self.f0_fallback_crepe_max_ratio
)
else:
allow_crepe_fallback = False
if np.any(need_fill2) and allow_crepe_fallback:
if log:
log.detail(
f"Harvest后仍掉线(主唱上下文): {int(need_fill2.sum())}/{len(f0)}，启用CREPE兜底"
)
f0_cr = _compute_crepe_f0(
x,
self.sr,
self.window,
f0_min_fb,
f0_max_fb,
self.device,
periodicity_threshold=self.crepe_pd_threshold,
)
if len(f0_cr) < len(f0):
f0_cr = np.pad(f0_cr, (0, len(f0) - len(f0_cr)), mode="edge")
else:
f0_cr = f0_cr[: len(f0)]
# Require cross-estimator agreement when both estimators are voiced.
both_voiced = (f0_cr > 0) & (f0_fb > 0)
agree_mask = np.zeros_like(f0, dtype=bool)
if np.any(both_voiced):
semitone_diff = np.abs(
12.0 * np.log2((f0_cr + 1e-6) / (f0_fb + 1e-6))
)
agree_mask = both_voiced & (semitone_diff <= 2.0)
fill_mask2 = need_fill2 & (
((f0_cr > 0) & (f0_fb <= 0)) | agree_mask
)
f0[fill_mask2] = f0_cr[fill_mask2]
elif np.any(need_fill2) and log:
log.detail(
f"Harvest后仍掉线(主唱上下文): {need_fill2_count}/{len(f0)}，"
"已跳过CREPE兜底（超出保守阈值）"
)
final_drop = (f0 <= 0) & energy_mask & voiced_context
if np.any(final_drop) and log:
log.detail(
f"保守兜底后保留无声帧: {int(final_drop.sum())}/{len(f0)}"
)
# Only smooth short, context-consistent gaps.
f0 = repair_f0(
f0,
max_gap=self.f0_fallback_post_gap,
mask=voiced_context,
)
elif f0_method == "rmvpe" and log:
log.detail("RMVPE严格模式: 不启用Harvest/CREPE兜底，仅使用RMVPE原始结果")
if self.f0_stabilize:
f0, octave_fixed, outlier_fixed = _stabilize_f0(
f0,
max_semitones=self.f0_stabilize_max_semitones,
window=self.f0_stabilize_window,
octave_fix=self.f0_stabilize_octave,
)
if log:
log.detail(
f"F0稳定器完成: 八度修正={octave_fixed}, 跳变修正={outlier_fixed}"
)
if self.f0_rate_limit:
f0, rate_fixed = _limit_f0_slope(
f0,
max_semitones=self.f0_rate_limit_semitones,
)
if log:
log.detail(f"F0限速完成: 修正帧={rate_fixed}")
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int32)
if log:
log.detail(f"F0处理完成: coarse shape={f0_coarse.shape}, bak shape={f0bak.shape}")
# 恢复原始hybrid模式设置
if restore_hybrid_mode:
self.f0_hybrid_mode = original_hybrid_mode
return f0_coarse, f0bak
def vc(
self,
model,
net_g,
sid,
audio0,
pitch,
pitchf,
times,
index,
big_npy,
index_rate,
version,
protect,
energy_ref_db=None,
): # ,file_index,file_big_npy
if log:
log.detail(f"VC推理: 音频长度={len(audio0)}, 版本={version}, 保护={protect}")
feats = torch.from_numpy(audio0)
if self.is_half:
feats = feats.half()
else:
feats = feats.float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
inputs = {
"source": feats.to(self.device),
"padding_mask": padding_mask,
"output_layer": 9 if version == "v1" else 12,
}
if log:
log.detail(f"HuBERT输出层: {inputs['output_layer']}")
t0 = ttime()
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
if log:
log.detail(f"特征提取完成: shape={feats.shape}")
if protect < 0.5 and pitch is not None and pitchf is not None:
feats0 = feats.clone()
if (
not isinstance(index, type(None))
and not isinstance(big_npy, type(None))
and index_rate != 0
):
if log:
log.detail(f"应用索引检索: index_rate={index_rate}")
npy = feats[0].cpu().numpy()
if self.is_half:
npy = npy.astype("float32")
# _, I = index.search(npy, 1)
# npy = big_npy[I.squeeze()]
score, ix = index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if self.is_half:
npy = npy.astype("float16")
feats = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+ (1 - index_rate) * feats
)
if log:
log.detail("索引混合完成")
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
if protect < 0.5 and pitch is not None and pitchf is not None:
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
0, 2, 1
)
t1 = ttime()
p_len = audio0.shape[0] // self.window
if feats.shape[1] < p_len:
p_len = feats.shape[1]
if pitch is not None and pitchf is not None:
pitch = pitch[:, :p_len]
pitchf = pitchf[:, :p_len]
if protect < 0.5 and pitch is not None and pitchf is not None:
if log:
log.detail(f"应用保护: protect={protect}")
pitchff = _build_protect_mix_curve(pitchf, protect).unsqueeze(-1)
feats = feats * pitchff + feats0 * (1 - pitchff)
feats = feats.to(feats0.dtype)
p_len = torch.tensor([p_len], device=self.device).long()
# --- 能量感知软门控（所有特征操作完成后、推理前）---
# 使用连续衰减曲线代替硬二值化，避免静音/有声边界的撕裂伪影。
_p_len_val = p_len.item() if isinstance(p_len, torch.Tensor) else int(p_len)
_audio_np = audio0.astype(np.float32)
_frame_rms = librosa.feature.rms(
y=_audio_np, frame_length=self.window * 2, hop_length=self.window, center=True
)[0]
if _frame_rms.ndim > 1:
_frame_rms = _frame_rms[0]
if len(_frame_rms) > _p_len_val:
_frame_rms = _frame_rms[:_p_len_val]
elif len(_frame_rms) < _p_len_val:
_frame_rms = np.pad(_frame_rms, (0, _p_len_val - len(_frame_rms)), mode='edge')
_energy_db = 20.0 * np.log10(_frame_rms + 1e-8)
_ref = energy_ref_db if energy_ref_db is not None else float(np.percentile(_energy_db, 95))
# Soft gate: sigmoid curve centered at ref-45dB with 6dB transition width.
# Frames well above threshold → gain≈1; frames well below → gain≈0.05
# (keep a small floor to avoid zero-feature shock to the network).
_silence_center = _ref - 45.0
_transition_width = 6.0 # dB for the sigmoid ramp
_energy_gate = 1.0 / (1.0 + np.exp(-(_energy_db - _silence_center) / (_transition_width / 4.0)))
# Apply floor: never fully zero features (network handles near-zero better than hard zero)
_energy_gate = np.clip(_energy_gate, 0.05, 1.0)
# Smooth temporally
_sm = np.array([1, 2, 3, 2, 1], dtype=np.float32)
_sm /= _sm.sum()
_energy_gate = np.convolve(_energy_gate, _sm, mode='same')[:_p_len_val]
_energy_gate = np.clip(_energy_gate, 0.05, 1.0)
# Apply soft gate to features
_feat_len = feats.shape[1]
if len(_energy_gate) > _feat_len:
_feat_gate = _energy_gate[:_feat_len]
elif len(_energy_gate) < _feat_len:
_feat_gate = np.pad(_energy_gate, (0, _feat_len - len(_energy_gate)), mode='constant', constant_values=1.0)
else:
_feat_gate = _energy_gate
_gate_t = torch.from_numpy(_feat_gate.astype(np.float32)).to(feats.device).unsqueeze(0).unsqueeze(-1)
feats = feats * _gate_t
# F0 soft gating: consistently soft-attenuate both pitch confidence and pitch value
if pitch is not None and pitchf is not None:
_pitch_len = pitch.shape[1]
if len(_energy_gate) > _pitch_len:
_f0_gate = _energy_gate[:_pitch_len]
elif len(_energy_gate) < _pitch_len:
_f0_gate = np.pad(_energy_gate, (0, _pitch_len - len(_energy_gate)), mode='constant', constant_values=1.0)
else:
_f0_gate = _energy_gate
_f0_gate_t = torch.from_numpy(_f0_gate.astype(np.float32)).to(pitch.device).unsqueeze(0)
pitchf = pitchf * _f0_gate_t
# Soft-blend pitch toward silence bin (1) instead of hard switch
_silence_pitch = torch.ones_like(pitch)
_blend = _f0_gate_t.unsqueeze(-1) if _f0_gate_t.dim() < pitch.dim() else _f0_gate_t
pitch = (pitch.float() * _blend + _silence_pitch.float() * (1.0 - _blend)).long()
if log:
log.detail("执行神经网络推理...")
with torch.no_grad():
hasp = pitch is not None and pitchf is not None
arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
del hasp, arg
del feats, p_len, padding_mask
if torch.cuda.is_available():
torch.cuda.empty_cache()
t2 = ttime()
times[0] += t1 - t0
times[2] += t2 - t1
if log:
log.detail(f"VC推理完成: 输出长度={len(audio1)}, 耗时={t2-t0:.3f}s")
return audio1
def pipeline(
self,
model,
net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
f0_file=None,
):
if log:
log.progress("开始推理管道...")
log.detail(f"输入音频: {input_audio_path}")
log.detail(f"音频长度: {len(audio)} 样本 ({len(audio)/16000:.2f}秒)")
log.config(f"F0方法: {f0_method}, 音调偏移: {f0_up_key}")
log.config(f"索引率: {index_rate}, 滤波半径: {filter_radius}")
log.config(f"目标采样率: {tgt_sr}Hz, 重采样: {resample_sr}Hz")
log.config(f"RMS混合率: {rms_mix_rate}, 保护: {protect}")
log.config(f"版本: {version}, F0启用: {if_f0}")
if (
file_index != ""
# and file_big_npy != ""
# and os.path.exists(file_big_npy) == True
and os.path.exists(file_index)
and index_rate != 0
):
try:
if log:
log.model(f"加载索引文件: {file_index}")
index = faiss.read_index(file_index)
# big_npy = np.load(file_big_npy)
big_npy = index.reconstruct_n(0, index.ntotal)
if log:
log.detail(f"索引加载完成: {index.ntotal} 个向量")
except:
traceback.print_exc()
if log:
log.warning("索引加载失败，将不使用索引")
index = big_npy = None
else:
index = big_npy = None
if log:
log.detail("未使用索引文件")
if log:
log.detail("应用高通滤波...")
audio = signal.filtfilt(bh, ah, audio)
# 全局能量参考（用于分段 vc() 的能量遮蔽阈值一致性）
_global_rms = librosa.feature.rms(
y=audio, frame_length=self.window * 2, hop_length=self.window, center=True
)[0]
if _global_rms.ndim > 1:
_global_rms = _global_rms[0]
if _global_rms.size > 0:
_global_energy_db = 20.0 * np.log10(_global_rms + 1e-8)
_global_ref_db = float(np.percentile(_global_energy_db, 95))
else:
_global_ref_db = -20.0
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
opt_ts = []
if not self.disable_chunking and audio_pad.shape[0] > self.t_max:
if log:
log.detail(f"音频较长，进行分段处理: {audio_pad.shape[0]} > {self.t_max}")
audio_sum = np.zeros_like(audio)
for i in range(self.window):
audio_sum += np.abs(audio_pad[i : i - self.window])
for t in range(self.t_center, audio.shape[0], self.t_center):
opt_ts.append(
t
- self.t_query
+ np.where(
audio_sum[t - self.t_query : t + self.t_query]
== audio_sum[t - self.t_query : t + self.t_query].min()
)[0][0]
)
if log:
log.detail(f"分段数量: {len(opt_ts) + 1}")
else:
if log:
if self.disable_chunking:
log.detail("已禁用分段，单次处理")
else:
log.detail("音频较短，单次处理")
s = 0
audio_opt = []
t = None
t1 = ttime()
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
p_len = audio_pad.shape[0] // self.window
if log:
log.detail(f"填充后音频长度: {audio_pad.shape[0]}, p_len: {p_len}")
inp_f0 = None
if hasattr(f0_file, "name"):
try:
if log:
log.detail(f"加载自定义F0文件: {f0_file.name}")
with open(f0_file.name, "r") as f:
lines = f.read().strip("\n").split("\n")
inp_f0 = []
for line in lines:
inp_f0.append([float(i) for i in line.split(",")])
inp_f0 = np.array(inp_f0, dtype="float32")
if log:
log.detail(f"自定义F0加载完成: {inp_f0.shape}")
except:
traceback.print_exc()
if log:
log.warning("自定义F0加载失败")
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
pitch, pitchf = None, None
if if_f0 == 1:
if log:
log.progress("提取基频(F0)...")
pitch, pitchf = self.get_f0(
input_audio_path,
audio_pad,
p_len,
f0_up_key,
f0_method,
filter_radius,
inp_f0,
)
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
if "mps" not in str(self.device) or "xpu" not in str(self.device):
pitchf = pitchf.astype(np.float32)
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
if log:
log.success("F0提取完成")
t2 = ttime()
times[1] += t2 - t1
if log:
log.detail(f"F0提取耗时: {t2-t1:.3f}s")
# 分段推理（带交叉淡入淡出消除边界撕裂）
segment_count = len(opt_ts) + 1
current_segment = 0
# Crossfade length at target rate (~12ms). Each boundary segment
# keeps this many extra samples from the normally-trimmed padding
# region. The overlap between adjacent segments is 2 * _xfade_tgt.
_xfade_tgt = min(int(0.012 * tgt_sr), self.t_pad_tgt // 4) if len(opt_ts) > 0 else 0
def _trim_segment(raw, is_first, is_last):
"""Trim padding from vc() output, keeping crossfade overlap."""
left = self.t_pad_tgt if is_first else (self.t_pad_tgt - _xfade_tgt)
right = self.t_pad_tgt if is_last else (self.t_pad_tgt - _xfade_tgt)
return raw[left : -right] if right > 0 else raw[left:]
for idx, t in enumerate(opt_ts):
current_segment += 1
if log:
log.progress(f"处理分段 {current_segment}/{segment_count}...")
t = t // self.window * self.window
if if_f0 == 1:
raw = self.vc(
model,
net_g,
sid,
audio_pad[s : t + self.t_pad2 + self.window],
pitch[:, s // self.window : (t + self.t_pad2) // self.window],
pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
times,
index,
big_npy,
index_rate,
version,
protect,
energy_ref_db=_global_ref_db,
)
else:
raw = self.vc(
model,
net_g,
sid,
audio_pad[s : t + self.t_pad2 + self.window],
None,
None,
times,
index,
big_npy,
index_rate,
version,
protect,
energy_ref_db=_global_ref_db,
)
audio_opt.append(_trim_segment(raw, is_first=(idx == 0), is_last=False))
s = t
# 最后一段
if log:
log.progress(f"处理分段 {segment_count}/{segment_count}...")
if if_f0 == 1:
raw = self.vc(
model,
net_g,
sid,
audio_pad[t:],
pitch[:, t // self.window :] if t is not None else pitch,
pitchf[:, t // self.window :] if t is not None else pitchf,
times,
index,
big_npy,
index_rate,
version,
protect,
energy_ref_db=_global_ref_db,
)
else:
raw = self.vc(
model,
net_g,
sid,
audio_pad[t:],
None,
None,
times,
index,
big_npy,
index_rate,
version,
protect,
energy_ref_db=_global_ref_db,
)
audio_opt.append(_trim_segment(raw, is_first=(len(opt_ts) == 0), is_last=True))
if log:
log.detail("合并音频分段...")
# Overlap-add crossfade: adjacent segments share 2*_xfade_tgt
# samples of overlapping content (same original audio region
# processed as part of different chunks). Linear crossfade
# ensures amplitude-preserving smooth transition.
if len(audio_opt) > 1 and _xfade_tgt > 0:
overlap = 2 * _xfade_tgt
result = audio_opt[0]
for seg in audio_opt[1:]:
xf = min(overlap, len(result), len(seg))
if xf > 1:
fade_out = np.linspace(1.0, 0.0, xf, dtype=np.float32)
fade_in = 1.0 - fade_out
blended = result[-xf:] * fade_out + seg[:xf] * fade_in
result = np.concatenate([result[:-xf], blended, seg[xf:]])
else:
result = np.concatenate([result, seg])
audio_opt = result
else:
audio_opt = np.concatenate(audio_opt) if audio_opt else np.array([], dtype=np.float32)
if rms_mix_rate != 1:
if log:
log.detail(f"应用RMS混合: rate={rms_mix_rate}")
audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
if tgt_sr != resample_sr >= 16000:
if log:
log.detail(f"重采样: {tgt_sr}Hz -> {resample_sr}Hz")
audio_opt = librosa.resample(
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
)
peak_before_clip = float(np.max(np.abs(audio_opt)))
audio_opt = soft_clip(audio_opt, threshold=0.9, ceiling=0.99)
if log and peak_before_clip > 0.9:
peak_after_clip = float(np.max(np.abs(audio_opt)))
log.detail(
f"音频软削波: 峰值 {peak_before_clip:.4f} -> {peak_after_clip:.4f}"
)
audio_opt = np.clip(audio_opt, -0.99, 0.99)
audio_opt = (audio_opt * 32767.0).astype(np.int16)
del pitch, pitchf, sid
if torch.cuda.is_available():
torch.cuda.empty_cache()
if log:
log.detail("已清理CUDA缓存")
if log:
log.success(f"推理管道完成: 输出长度={len(audio_opt)} 样本")
return audio_opt
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