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.generate_interpolated_frequencies() def generate_interpolated_frequencies(self): """ Generates a dictionary of interpolated frequencies between reference frequencies. """ note_dict = [] for i in range(len(self.ref_freqs) - 1): freq_low = self.ref_freqs[i] freq_high = self.ref_freqs[i + 1] interpolated_freqs = np.linspace( freq_low, freq_high, num=10, endpoint=False ) note_dict.extend(interpolated_freqs) note_dict.append(self.ref_freqs[-1]) return note_dict def autotune_f0(self, f0): """ 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] = closest_note 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 = [ 65.41, 82.41, 110.00, 146.83, 196.00, 246.94, 329.63, 440.00, 587.33, 783.99, 1046.50, ] self.autotune = Autotune(self.ref_freqs) self.note_dict = self.autotune.note_dict 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": self.model_rmvpe = RMVPE0Predictor( os.path.join("rvc", "models", "predictors", "rmvpe.pt"), is_half=self.is_half, device=self.device, ) 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, 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": self.model_rmvpe = RMVPE0Predictor( os.path.join("rvc", "models", "predictors", "rmvpe.pt"), is_half=self.is_half, device=self.device, ) 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 == "True": f0 = Autotune.autotune_f0(self, f0) 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. """ feats = torch.from_numpy(audio0) if self.is_half: feats = feats.half() else: feats = feats.float() if feats.dim() == 2: 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, } with torch.no_grad(): logits = model.extract_features(**inputs) feats = model.final_proj(logits[0]) if version == "v1" else logits[0] if protect < 0.5 and pitch != None and pitchf != None: feats0 = feats.clone() if ( isinstance(index, type(None)) == False and isinstance(big_npy, type(None)) == False and index_rate != 0 ): npy = feats[0].cpu().numpy() if self.is_half: npy = npy.astype("float32") 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 ) feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) if protect < 0.5 and pitch != None and pitchf != None: feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute( 0, 2, 1 ) p_len = audio0.shape[0] // self.window if feats.shape[1] < p_len: p_len = feats.shape[1] if pitch != None and pitchf != None: pitch = pitch[:, :p_len] pitchf = pitchf[:, :p_len] if protect < 0.5 and pitch != None and pitchf != None: pitchff = pitchf.clone() pitchff[pitchf > 0] = 1 pitchff[pitchf < 1] = protect pitchff = pitchff.unsqueeze(-1) feats = feats * pitchff + feats0 * (1 - pitchff) feats = feats.to(feats0.dtype) p_len = torch.tensor([p_len], device=self.device).long() with torch.no_grad(): if pitch != None and pitchf != None: audio1 = ( (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]) .data.cpu() .float() .numpy() ) else: audio1 = ( (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy() ) del feats, p_len, padding_mask if torch.cuda.is_available(): torch.cuda.empty_cache() return audio1 def pipeline( self, model, net_g, sid, audio, input_audio_path, pitch, f0_method, file_index, index_rate, pitch_guidance, filter_radius, tgt_sr, resample_sr, volume_envelope, version, protect, hop_length, f0_autotune, 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) == True 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") == True: 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 == True: pitch, pitchf = self.get_f0( input_audio_path, audio_pad, p_len, pitch, f0_method, filter_radius, hop_length, f0_autotune, 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 == True: 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 == True: 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, tgt_sr, 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 * max_int16).astype(np.int16) del pitch, pitchf, sid if torch.cuda.is_available(): torch.cuda.empty_cache() return audio_opt