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import os
import gc
import re
import sys
import torch
import torch.nn.functional as F
import torchcrepe
import faiss
import librosa
import numpy as np
from scipy import signal
from torch import Tensor
now_dir = os.getcwd()
sys.path.append(now_dir)
from rvc.lib.predictors.RMVPE import RMVPE0Predictor
from rvc.lib.predictors.FCPE import FCPEF0Predictor
import logging
logging.getLogger("faiss").setLevel(logging.WARNING)
# Constants for high-pass filter
FILTER_ORDER = 5
CUTOFF_FREQUENCY = 48 # Hz
SAMPLE_RATE = 16000 # Hz
bh, ah = signal.butter(
N=FILTER_ORDER, Wn=CUTOFF_FREQUENCY, btype="high", fs=SAMPLE_RATE
)
input_audio_path2wav = {}
class AudioProcessor:
"""
A class for processing audio signals, specifically for adjusting RMS levels.
"""
def change_rms(
source_audio: np.ndarray,
source_rate: int,
target_audio: np.ndarray,
target_rate: int,
rate: float,
) -> np.ndarray:
"""
Adjust the RMS level of target_audio to match the RMS of source_audio, with a given blending rate.
Args:
source_audio: The source audio signal as a NumPy array.
source_rate: The sampling rate of the source audio.
target_audio: The target audio signal to adjust.
target_rate: The sampling rate of the target audio.
rate: The blending rate between the source and target RMS levels.
"""
# Calculate RMS of both audio data
rms1 = librosa.feature.rms(
y=source_audio,
frame_length=source_rate // 2 * 2,
hop_length=source_rate // 2,
)
rms2 = librosa.feature.rms(
y=target_audio,
frame_length=target_rate // 2 * 2,
hop_length=target_rate // 2,
)
# Interpolate RMS to match target audio length
rms1 = F.interpolate(
torch.from_numpy(rms1).float().unsqueeze(0),
size=target_audio.shape[0],
mode="linear",
).squeeze()
rms2 = F.interpolate(
torch.from_numpy(rms2).float().unsqueeze(0),
size=target_audio.shape[0],
mode="linear",
).squeeze()
rms2 = torch.maximum(rms2, torch.zeros_like(rms2) + 1e-6)
# Adjust target audio RMS based on the source audio RMS
adjusted_audio = (
target_audio
* (torch.pow(rms1, 1 - rate) * torch.pow(rms2, rate - 1)).numpy()
)
return adjusted_audio
class Autotune:
"""
A class for applying autotune to a given fundamental frequency (F0) contour.
"""
def __init__(self, ref_freqs):
"""
Initializes the Autotune class with a set of reference frequencies.
Args:
ref_freqs: A list of reference frequencies representing musical notes.
"""
self.ref_freqs = ref_freqs
self.note_dict = self.ref_freqs # No interpolation needed
def autotune_f0(self, f0, f0_autotune_strength):
"""
Autotunes a given F0 contour by snapping each frequency to the closest reference frequency.
Args:
f0: The input F0 contour as a NumPy array.
"""
autotuned_f0 = np.zeros_like(f0)
for i, freq in enumerate(f0):
closest_note = min(self.note_dict, key=lambda x: abs(x - freq))
autotuned_f0[i] = freq + (closest_note - freq) * f0_autotune_strength
return autotuned_f0
class Pipeline:
"""
The main pipeline class for performing voice conversion, including preprocessing, F0 estimation,
voice conversion using a model, and post-processing.
"""
def __init__(self, tgt_sr, config):
"""
Initializes the Pipeline class with target sampling rate and configuration parameters.
Args:
tgt_sr: The target sampling rate for the output audio.
config: A configuration object containing various parameters for the pipeline.
"""
self.x_pad = config.x_pad
self.x_query = config.x_query
self.x_center = config.x_center
self.x_max = config.x_max
self.is_half = config.is_half
self.sample_rate = 16000
self.window = 160
self.t_pad = self.sample_rate * self.x_pad
self.t_pad_tgt = tgt_sr * self.x_pad
self.t_pad2 = self.t_pad * 2
self.t_query = self.sample_rate * self.x_query
self.t_center = self.sample_rate * self.x_center
self.t_max = self.sample_rate * self.x_max
self.time_step = self.window / self.sample_rate * 1000
self.f0_min = 50
self.f0_max = 1100
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
self.device = config.device
self.ref_freqs = [
49.00, # G1
51.91, # G#1 / Ab1
55.00, # A1
58.27, # A#1 / Bb1
61.74, # B1
65.41, # C2
69.30, # C#2 / Db2
73.42, # D2
77.78, # D#2 / Eb2
82.41, # E2
87.31, # F2
92.50, # F#2 / Gb2
98.00, # G2
103.83, # G#2 / Ab2
110.00, # A2
116.54, # A#2 / Bb2
123.47, # B2
130.81, # C3
138.59, # C#3 / Db3
146.83, # D3
155.56, # D#3 / Eb3
164.81, # E3
174.61, # F3
185.00, # F#3 / Gb3
196.00, # G3
207.65, # G#3 / Ab3
220.00, # A3
233.08, # A#3 / Bb3
246.94, # B3
261.63, # C4
277.18, # C#4 / Db4
293.66, # D4
311.13, # D#4 / Eb4
329.63, # E4
349.23, # F4
369.99, # F#4 / Gb4
392.00, # G4
415.30, # G#4 / Ab4
440.00, # A4
466.16, # A#4 / Bb4
493.88, # B4
523.25, # C5
554.37, # C#5 / Db5
587.33, # D5
622.25, # D#5 / Eb5
659.25, # E5
698.46, # F5
739.99, # F#5 / Gb5
783.99, # G5
830.61, # G#5 / Ab5
880.00, # A5
932.33, # A#5 / Bb5
987.77, # B5
1046.50, # C6
]
self.autotune = Autotune(self.ref_freqs)
self.note_dict = self.autotune.note_dict
self.model_rmvpe = RMVPE0Predictor(
os.path.join("rvc", "models", "predictors", "rmvpe.pt"),
is_half=self.is_half,
device=self.device,
)
def get_f0_crepe(
self,
x,
f0_min,
f0_max,
p_len,
hop_length,
model="full",
):
"""
Estimates the fundamental frequency (F0) of a given audio signal using the Crepe model.
Args:
x: The input audio signal as a NumPy array.
f0_min: Minimum F0 value to consider.
f0_max: Maximum F0 value to consider.
p_len: Desired length of the F0 output.
hop_length: Hop length for the Crepe model.
model: Crepe model size to use ("full" or "tiny").
"""
x = x.astype(np.float32)
x /= np.quantile(np.abs(x), 0.999)
audio = torch.from_numpy(x).to(self.device, copy=True)
audio = torch.unsqueeze(audio, dim=0)
if audio.ndim == 2 and audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True).detach()
audio = audio.detach()
pitch: Tensor = torchcrepe.predict(
audio,
self.sample_rate,
hop_length,
f0_min,
f0_max,
model,
batch_size=hop_length * 2,
device=self.device,
pad=True,
)
p_len = p_len or x.shape[0] // hop_length
source = np.array(pitch.squeeze(0).cpu().float().numpy())
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * p_len, len(source)) / p_len,
np.arange(0, len(source)),
source,
)
f0 = np.nan_to_num(target)
return f0
def get_f0_hybrid(
self,
methods_str,
x,
f0_min,
f0_max,
p_len,
hop_length,
):
"""
Estimates the fundamental frequency (F0) using a hybrid approach combining multiple methods.
Args:
methods_str: A string specifying the methods to combine (e.g., "hybrid[crepe+rmvpe]").
x: The input audio signal as a NumPy array.
f0_min: Minimum F0 value to consider.
f0_max: Maximum F0 value to consider.
p_len: Desired length of the F0 output.
hop_length: Hop length for F0 estimation methods.
"""
methods_str = re.search("hybrid\[(.+)\]", methods_str)
if methods_str:
methods = [method.strip() for method in methods_str.group(1).split("+")]
f0_computation_stack = []
print(f"Calculating f0 pitch estimations for methods {str(methods)}")
x = x.astype(np.float32)
x /= np.quantile(np.abs(x), 0.999)
for method in methods:
f0 = None
if method == "crepe":
f0 = self.get_f0_crepe_computation(
x, f0_min, f0_max, p_len, int(hop_length)
)
elif method == "rmvpe":
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
f0 = f0[1:]
elif method == "fcpe":
self.model_fcpe = FCPEF0Predictor(
os.path.join("rvc", "models", "predictors", "fcpe.pt"),
f0_min=int(f0_min),
f0_max=int(f0_max),
dtype=torch.float32,
device=self.device,
sample_rate=self.sample_rate,
threshold=0.03,
)
f0 = self.model_fcpe.compute_f0(x, p_len=p_len)
del self.model_fcpe
gc.collect()
f0_computation_stack.append(f0)
f0_computation_stack = [fc for fc in f0_computation_stack if fc is not None]
f0_median_hybrid = None
if len(f0_computation_stack) == 1:
f0_median_hybrid = f0_computation_stack[0]
else:
f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
return f0_median_hybrid
def get_f0(
self,
input_audio_path,
x,
p_len,
pitch,
f0_method,
filter_radius,
hop_length,
f0_autotune,
f0_autotune_strength,
inp_f0=None,
):
"""
Estimates the fundamental frequency (F0) of a given audio signal using various methods.
Args:
input_audio_path: Path to the input audio file.
x: The input audio signal as a NumPy array.
p_len: Desired length of the F0 output.
pitch: Key to adjust the pitch of the F0 contour.
f0_method: Method to use for F0 estimation (e.g., "crepe").
filter_radius: Radius for median filtering the F0 contour.
hop_length: Hop length for F0 estimation methods.
f0_autotune: Whether to apply autotune to the F0 contour.
inp_f0: Optional input F0 contour to use instead of estimating.
"""
global input_audio_path2wav
if f0_method == "crepe":
f0 = self.get_f0_crepe(x, self.f0_min, self.f0_max, p_len, int(hop_length))
elif f0_method == "crepe-tiny":
f0 = self.get_f0_crepe(
x, self.f0_min, self.f0_max, p_len, int(hop_length), "tiny"
)
elif f0_method == "rmvpe":
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
elif f0_method == "fcpe":
self.model_fcpe = FCPEF0Predictor(
os.path.join("rvc", "models", "predictors", "fcpe.pt"),
f0_min=int(self.f0_min),
f0_max=int(self.f0_max),
dtype=torch.float32,
device=self.device,
sample_rate=self.sample_rate,
threshold=0.03,
)
f0 = self.model_fcpe.compute_f0(x, p_len=p_len)
del self.model_fcpe
gc.collect()
elif "hybrid" in f0_method:
input_audio_path2wav[input_audio_path] = x.astype(np.double)
f0 = self.get_f0_hybrid(
f0_method,
x,
self.f0_min,
self.f0_max,
p_len,
hop_length,
)
if f0_autotune is True:
f0 = Autotune.autotune_f0(self, f0, f0_autotune_strength)
f0 *= pow(2, pitch / 12)
tf0 = self.sample_rate // self.window
if inp_f0 is not None:
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
]
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
self.f0_mel_max - self.f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int)
return f0_coarse, f0bak
def voice_conversion(
self,
model,
net_g,
sid,
audio0,
pitch,
pitchf,
index,
big_npy,
index_rate,
version,
protect,
):
"""
Performs voice conversion on a given audio segment.
Args:
model: The feature extractor model.
net_g: The generative model for synthesizing speech.
sid: Speaker ID for the target voice.
audio0: The input audio segment.
pitch: Quantized F0 contour for pitch guidance.
pitchf: Original F0 contour for pitch guidance.
index: FAISS index for speaker embedding retrieval.
big_npy: Speaker embeddings stored in a NumPy array.
index_rate: Blending rate for speaker embedding retrieval.
version: Model version ("v1" or "v2").
protect: Protection level for preserving the original pitch.
"""
with torch.no_grad():
pitch_guidance = pitch != None and pitchf != None
# prepare source audio
feats = (
torch.from_numpy(audio0).half()
if self.is_half
else torch.from_numpy(audio0).float()
)
feats = feats.mean(-1) if feats.dim() == 2 else feats
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1).to(self.device)
# extract features
feats = model(feats)["last_hidden_state"]
feats = (
model.final_proj(feats[0]).unsqueeze(0) if version == "v1" else feats
)
# make a copy for pitch guidance and protection
feats0 = feats.clone() if pitch_guidance else None
if (
index
): # set by parent function, only true if index is available, loaded, and index rate > 0
feats = self._retrieve_speaker_embeddings(
feats, index, big_npy, index_rate
)
# feature upsampling
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(
0, 2, 1
)
# adjust the length if the audio is short
p_len = min(audio0.shape[0] // self.window, feats.shape[1])
if pitch_guidance:
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
0, 2, 1
)
pitch, pitchf = pitch[:, :p_len], pitchf[:, :p_len]
# Pitch protection blending
if protect < 0.5:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect
feats = feats * pitchff.unsqueeze(-1) + feats0 * (
1 - pitchff.unsqueeze(-1)
)
feats = feats.to(feats0.dtype)
else:
pitch, pitchf = None, None
p_len = torch.tensor([p_len], device=self.device).long()
audio1 = (
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
.data.cpu()
.float()
.numpy()
)
# clean up
del feats, feats0, p_len
if torch.cuda.is_available():
torch.cuda.empty_cache()
return audio1
def _retrieve_speaker_embeddings(self, feats, index, big_npy, index_rate):
npy = feats[0].cpu().numpy()
npy = npy.astype("float32") if self.is_half else npy
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)
npy = npy.astype("float16") if self.is_half else npy
feats = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+ (1 - index_rate) * feats
)
return feats
def pipeline(
self,
model,
net_g,
sid,
audio,
pitch,
f0_method,
file_index,
index_rate,
pitch_guidance,
filter_radius,
volume_envelope,
version,
protect,
hop_length,
f0_autotune,
f0_autotune_strength,
f0_file,
):
"""
The main pipeline function for performing voice conversion.
Args:
model: The feature extractor model.
net_g: The generative model for synthesizing speech.
sid: Speaker ID for the target voice.
audio: The input audio signal.
input_audio_path: Path to the input audio file.
pitch: Key to adjust the pitch of the F0 contour.
f0_method: Method to use for F0 estimation.
file_index: Path to the FAISS index file for speaker embedding retrieval.
index_rate: Blending rate for speaker embedding retrieval.
pitch_guidance: Whether to use pitch guidance during voice conversion.
filter_radius: Radius for median filtering the F0 contour.
tgt_sr: Target sampling rate for the output audio.
resample_sr: Resampling rate for the output audio.
volume_envelope: Blending rate for adjusting the RMS level of the output audio.
version: Model version.
protect: Protection level for preserving the original pitch.
hop_length: Hop length for F0 estimation methods.
f0_autotune: Whether to apply autotune to the F0 contour.
f0_file: Path to a file containing an F0 contour to use.
"""
if file_index != "" and os.path.exists(file_index) and index_rate > 0:
try:
index = faiss.read_index(file_index)
big_npy = index.reconstruct_n(0, index.ntotal)
except Exception as error:
print(f"An error occurred reading the FAISS index: {error}")
index = big_npy = None
else:
index = big_npy = None
audio = signal.filtfilt(bh, ah, audio)
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
opt_ts = []
if audio_pad.shape[0] > self.t_max:
audio_sum = np.zeros_like(audio)
for i in range(self.window):
audio_sum += 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(
np.abs(audio_sum[t - self.t_query : t + self.t_query])
== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
)[0][0]
)
s = 0
audio_opt = []
t = None
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
p_len = audio_pad.shape[0] // self.window
inp_f0 = None
if hasattr(f0_file, "name"):
try:
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")
except Exception as error:
print(f"An error occurred reading the F0 file: {error}")
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
if pitch_guidance:
pitch, pitchf = self.get_f0(
"input_audio_path", # questionable purpose of making a key for an array
audio_pad,
p_len,
pitch,
f0_method,
filter_radius,
hop_length,
f0_autotune,
f0_autotune_strength,
inp_f0,
)
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
if self.device == "mps":
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()
for t in opt_ts:
t = t // self.window * self.window
if pitch_guidance:
audio_opt.append(
self.voice_conversion(
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],
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
else:
audio_opt.append(
self.voice_conversion(
model,
net_g,
sid,
audio_pad[s : t + self.t_pad2 + self.window],
None,
None,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
s = t
if pitch_guidance:
audio_opt.append(
self.voice_conversion(
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,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
else:
audio_opt.append(
self.voice_conversion(
model,
net_g,
sid,
audio_pad[t:],
None,
None,
index,
big_npy,
index_rate,
version,
protect,
)[self.t_pad_tgt : -self.t_pad_tgt]
)
audio_opt = np.concatenate(audio_opt)
if volume_envelope != 1:
audio_opt = AudioProcessor.change_rms(
audio, self.sample_rate, audio_opt, self.sample_rate, volume_envelope
)
# if resample_sr >= self.sample_rate and tgt_sr != resample_sr:
# audio_opt = librosa.resample(
# audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
# )
# audio_max = np.abs(audio_opt).max() / 0.99
# max_int16 = 32768
# if audio_max > 1:
# max_int16 /= audio_max
# audio_opt = (audio_opt * 32768).astype(np.int16)
audio_max = np.abs(audio_opt).max() / 0.99
if audio_max > 1:
audio_opt /= audio_max
if pitch_guidance:
del pitch, pitchf
del sid
if torch.cuda.is_available():
torch.cuda.empty_cache()
return audio_opt
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