import os import sys import json import torch import librosa import numpy as np from scipy.io.wavfile import write from scipy import signal from transformers import WavLMModel # Try to import noisereduce, install if not available try: import noisereduce as nr except ImportError: import subprocess subprocess.check_call([sys.executable, "-m", "pip", "install", "noisereduce"]) import noisereduce as nr # Add FreeVC directory to path with higher priority to avoid conflicts freevc_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "freevc") if freevc_dir not in sys.path: sys.path.insert(0, freevc_dir) # Insert at beginning to prioritize # Import FreeVC modules from models import SynthesizerTrn from mel_processing import mel_spectrogram_torch from speaker_encoder.voice_encoder import SpeakerEncoder def load_config(config_path): """Load configuration from JSON file""" try: with open(config_path, 'r') as f: data = f.read() config = json.loads(data) return HParams(**config) except Exception as e: print(f"Error loading config from {config_path}: {str(e)}") raise def load_checkpoint(checkpoint_path, model, optimizer=None): """Load model checkpoint""" assert os.path.isfile(checkpoint_path), f"Checkpoint file not found: {checkpoint_path}" try: checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') iteration = checkpoint_dict['iteration'] saved_state_dict = checkpoint_dict['model'] if hasattr(model, 'module'): state_dict = model.module.state_dict() else: state_dict = model.state_dict() new_state_dict = {} for k, v in state_dict.items(): try: new_state_dict[k] = saved_state_dict[k] except: print("%s is not in the checkpoint" % k) new_state_dict[k] = v if hasattr(model, 'module'): model.module.load_state_dict(new_state_dict) else: model.load_state_dict(new_state_dict) return model, optimizer, iteration except Exception as e: print(f"Error loading checkpoint from {checkpoint_path}: {str(e)}") raise class HParams(): """Hyperparameters container class""" 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__() def get_model_filenames(model_type): """ Get the config and checkpoint filenames for a given model type Args: model_type (str): The model type (e.g., 'FreeVC', 'FreeVC-s', etc.) Returns: tuple: (config_filename, checkpoint_filename) """ if model_type == "FreeVC (24kHz)": config_filename = "freevc-24.json" checkpoint_filename = "freevc-24.pth" else: base_name = model_type.lower().replace(' ', '-') config_filename = f"{base_name}.json" checkpoint_filename = f"{base_name}.pth" return config_filename, checkpoint_filename def check_file_exists(filepath, description="File"): """Check if a file exists and raise a FileNotFoundError if it doesn't""" if not os.path.exists(filepath): raise FileNotFoundError(f"{description} not found: {filepath}") return True def list_available_models(freevc_dir): """ List available models by checking config and checkpoint files Args: freevc_dir (str): Path to the FreeVC directory Returns: list: List of available model types """ available_models = [] config_dir = os.path.join(freevc_dir, "configs") checkpoint_dir = os.path.join(freevc_dir, "checkpoints") if not os.path.exists(config_dir) or not os.path.exists(checkpoint_dir): print(f"Config or checkpoint directory not found: {config_dir}, {checkpoint_dir}") return available_models # Standard model types model_types = ["FreeVC", "FreeVC-s", "FreeVC (24kHz)"] for model_type in model_types: config_filename, checkpoint_filename = get_model_filenames(model_type) config_path = os.path.join(config_dir, config_filename) checkpoint_path = os.path.join(checkpoint_dir, checkpoint_filename) if os.path.exists(config_path) and os.path.exists(checkpoint_path): available_models.append(model_type) return available_models def apply_post_processing(audio, sampling_rate, clarity_enhancement=0.3, voice_match_strength=0.5, presence_boost=0.4, normalize_output=True, normalization_level=0.95): """ Apply enhanced post-processing to improve the converted voice Args: audio (numpy.ndarray): Audio data to process sampling_rate (int): Sampling rate of the audio clarity_enhancement (float): Amount of clarity enhancement to apply (0.0-1.0) voice_match_strength (float): How strongly to enhance voice characteristics (0.0-1.0) presence_boost (float): Amount of vocal presence enhancement (0.0-1.0) normalize_output (bool): Whether to normalize the output audio normalization_level (float): Target level for normalization (0.0-1.0) Returns: numpy.ndarray: Processed audio data """ try: # Gentle noise gate to remove background artifacts threshold = 0.005 audio[np.abs(audio) < threshold] = 0 # Apply voice matching EQ to enhance the voice characteristics if voice_match_strength > 0: # Enhance formants (vocal tract resonances) - key for voice mimicry # Different formant regions for better voice adaptation b1, a1 = signal.butter(2, [500/(sampling_rate/2), 1200/(sampling_rate/2)], 'bandpass', analog=False) formant1_enhanced = signal.filtfilt(b1, a1, audio) b2, a2 = signal.butter(2, [1200/(sampling_rate/2), 2500/(sampling_rate/2)], 'bandpass', analog=False) formant2_enhanced = signal.filtfilt(b2, a2, audio) # Higher formants for voice character b3, a3 = signal.butter(2, [2500/(sampling_rate/2), 3500/(sampling_rate/2)], 'bandpass', analog=False) formant3_enhanced = signal.filtfilt(b3, a3, audio) # Mix the enhancements with original with precise formant balancing audio = audio * (1-voice_match_strength) + \ formant1_enhanced * (voice_match_strength*0.5) + \ formant2_enhanced * (voice_match_strength*0.3) + \ formant3_enhanced * (voice_match_strength*0.2) # Enhance vocal presence - makes voice sound more "in the room" if presence_boost > 0: # Presence is typically in this region b_presence, a_presence = signal.butter(2, [2000/(sampling_rate/2), 5000/(sampling_rate/2)], 'bandpass', analog=False) presence = signal.filtfilt(b_presence, a_presence, audio) # Apply slight compression to presence band for more consistent character threshold = np.max(np.abs(presence)) * 0.3 ratio = 0.7 presence_compressed = np.where( np.abs(presence) > threshold, np.sign(presence) * (threshold + (np.abs(presence) - threshold) * ratio), presence ) # Mix back with slight delay (1ms) for more depth delay_samples = int(sampling_rate * 0.001) if delay_samples > 0: delayed_presence = np.zeros_like(audio) delayed_presence[delay_samples:] = presence_compressed[:-delay_samples] audio = audio * (1-presence_boost) + delayed_presence * presence_boost else: audio = audio * (1-presence_boost) + presence_compressed * presence_boost # Apply clarity enhancement if requested - improves intelligibility if clarity_enhancement > 0: # Clarity lies in mid-high frequencies b, a = signal.butter(2, 3000/(sampling_rate/2), 'high', analog=False) clarity = signal.filtfilt(b, a, audio) # Add subtle harmonics enhancement (improves perceived clarity) harmonics = audio * audio * 0.1 # Simple harmonic generation harmonics = signal.filtfilt(b, a, harmonics) # Filter to keep only high harmonics # Mix clarity enhancement with original audio = audio * (1-clarity_enhancement) + \ clarity * (clarity_enhancement * 0.8) + \ harmonics * (clarity_enhancement * 0.2) # Apply gentle multiband compression for professional sound try: # Low-end compression (below 300Hz) b_low, a_low = signal.butter(2, 300/(sampling_rate/2), 'low', analog=False) low_band = signal.filtfilt(b_low, a_low, audio) # Mid-band compression (300Hz-3kHz - where the voice mainly is) b_mid, a_mid = signal.butter(2, [300/(sampling_rate/2), 3000/(sampling_rate/2)], 'bandpass', analog=False) mid_band = signal.filtfilt(b_mid, a_mid, audio) # High-band compression (above 3kHz) b_high, a_high = signal.butter(2, 3000/(sampling_rate/2), 'high', analog=False) high_band = signal.filtfilt(b_high, a_high, audio) # Apply different compression settings to each band # Low band - stronger compression low_threshold = np.max(np.abs(low_band)) * 0.4 low_ratio = 0.5 low_compressed = np.where( np.abs(low_band) > low_threshold, np.sign(low_band) * (low_threshold + (np.abs(low_band) - low_threshold) * low_ratio), low_band ) # Mid band - moderate compression mid_threshold = np.max(np.abs(mid_band)) * 0.5 mid_ratio = 0.7 mid_compressed = np.where( np.abs(mid_band) > mid_threshold, np.sign(mid_band) * (mid_threshold + (np.abs(mid_band) - mid_threshold) * mid_ratio), mid_band ) # High band - gentle compression high_threshold = np.max(np.abs(high_band)) * 0.6 high_ratio = 0.8 high_compressed = np.where( np.abs(high_band) > high_threshold, np.sign(high_band) * (high_threshold + (np.abs(high_band) - high_threshold) * high_ratio), high_band ) # Recombine the bands audio = low_compressed + mid_compressed + high_compressed except Exception as e: print(f"Multiband compression failed: {e}, skipping...") # Final gentle noise gate threshold_final = 0.01 audio[np.abs(audio) < threshold_final] = 0 # Apply output normalization if requested if normalize_output: print(f"Normalizing output audio to level: {normalization_level}") # First normalize to [-1, 1] if np.max(np.abs(audio)) > 0: audio = librosa.util.normalize(audio) # Then scale to the requested level audio = audio * normalization_level # Add very subtle warmth (low-mid emphasis) b_warm, a_warm = signal.butter(2, [100/(sampling_rate/2), 800/(sampling_rate/2)], 'bandpass', analog=False) warmth = signal.filtfilt(b_warm, a_warm, audio) audio = audio * 0.95 + warmth * 0.05 except Exception as e: print(f"Post-processing failed: {e}, using unprocessed audio...") return audio def process_audio(audio_data, target_sr, label=""): """ Process audio data: convert to mono if stereo and resample if needed. Args: audio_data (dict): Audio data dictionary with 'waveform' and 'sample_rate' target_sr (int): Target sampling rate label (str): Label for logging purposes Returns: numpy.ndarray: Processed audio data """ if not isinstance(audio_data, dict) or 'waveform' not in audio_data: raise ValueError(f"Invalid {label} audio format: {type(audio_data)}") # Extract waveform and handle stereo wav = audio_data['waveform'].squeeze().numpy() sr = audio_data['sample_rate'] print(f"{label} audio loaded: {wav.shape}, sr={sr}") # Convert stereo to mono if needed if len(wav.shape) > 1 and wav.shape[0] == 2: print(f"Converting {label} audio from stereo to mono") wav = np.mean(wav, axis=0) # Resample if needed if sr != target_sr: print(f"Resampling {label} audio from {sr} to {target_sr}") wav = librosa.resample(wav, orig_sr=sr, target_sr=target_sr) return wav def preprocess_audio(wav, sr, target_sr, noise_reduction_strength=0.5, neutralize_source=0.0, enhance_clarity=0.0, remove_sibilance=0.0, vad_sensitivity=20): """ Enhanced preprocessing for audio with multiple options Args: wav (numpy.ndarray): Audio data sr (int): Sample rate of audio target_sr (int): Target sample rate noise_reduction_strength (float): Amount of noise reduction to apply neutralize_source (float): How much to neutralize voice characteristics enhance_clarity (float): Amount of clarity enhancement remove_sibilance (float): Amount of sibilance reduction vad_sensitivity (int): Voice activity detection sensitivity Returns: numpy.ndarray: Processed audio data """ # Apply noise reduction if noise_reduction_strength > 0: try: wav = nr.reduce_noise(y=wav, sr=sr, prop_decrease=noise_reduction_strength) except Exception as e: print(f"Noise reduction failed: {e}, skipping...") # Voice activity detection to focus on speech segments try: intervals = librosa.effects.split(wav, top_db=vad_sensitivity) if len(intervals) > 0: # Only apply if we found speech segments wav_output = np.zeros_like(wav) for interval in intervals: wav_output[interval[0]:interval[1]] = wav[interval[0]:interval[1]] wav = wav_output except Exception as e: print(f"VAD failed: {e}, using original audio...") # Neutralize source voice characteristics if neutralize_source > 0: try: # Flatten formants a bit to neutralize voice b1, a1 = signal.butter(2, [300/(sr/2), 3000/(sr/2)], 'bandpass', analog=False) neutral_voice = signal.filtfilt(b1, a1, wav) # Mix with original wav = wav * (1-neutralize_source) + neutral_voice * neutralize_source except Exception as e: print(f"Source neutralization failed: {e}, skipping...") # Enhance clarity if enhance_clarity > 0: try: b2, a2 = signal.butter(2, 2000/(sr/2), 'high', analog=False) clarity = signal.filtfilt(b2, a2, wav) wav = wav * (1-enhance_clarity) + clarity * enhance_clarity except Exception as e: print(f"Clarity enhancement failed: {e}, skipping...") # Reduce sibilance (de-essing) if remove_sibilance > 0: try: # Sibilance mainly in 5-8kHz range b3, a3 = signal.butter(2, [5000/(sr/2), 8000/(sr/2)], 'bandpass', analog=False) sibilance = signal.filtfilt(b3, a3, wav) # Compress sibilance thresh = np.max(np.abs(sibilance)) * 0.2 ratio = 0.3 sibilance_reduced = np.where( np.abs(sibilance) > thresh, np.sign(sibilance) * (thresh + (np.abs(sibilance) - thresh) * ratio), sibilance ) # Recombine with reduced sibilance wav = wav - (sibilance * remove_sibilance) + (sibilance_reduced * remove_sibilance) except Exception as e: print(f"Sibilance reduction failed: {e}, skipping...") # Normalize audio wav = librosa.util.normalize(wav) * 0.95 return wav def process_multiple_references(reference_audios, target_sr, device, speaker_encoder, noise_reduction_strength=0.5): """ Process multiple reference samples to get more robust speaker embedding Args: reference_audios (list): List of reference audio dictionaries target_sr (int): Target sampling rate device (torch.device): Device to use for processing speaker_encoder: Speaker encoder model noise_reduction_strength (float): Amount of noise reduction to apply Returns: torch.Tensor: Speaker embedding tensor """ if not isinstance(reference_audios, list): reference_audios = [reference_audios] embeddings = [] for ref_audio in reference_audios: wav_ref = process_audio(ref_audio, target_sr, "reference") wav_ref = preprocess_audio(wav_ref, target_sr, target_sr, noise_reduction_strength) # Get embedding for this reference try: print("Getting speaker embedding") ref_embed = speaker_encoder.embed_utterance(wav_ref) embeddings.append(ref_embed) except Exception as e: print(f"Error getting speaker embedding: {str(e)}") # Average the embeddings if embeddings: avg_embed = np.mean(embeddings, axis=0) # Re-normalize the averaged embedding avg_embed = avg_embed / np.linalg.norm(avg_embed, 2) return torch.from_numpy(avg_embed).unsqueeze(0).to(device) # If we get here with no embeddings, something went wrong print("WARNING: No embeddings generated from reference audio. This will cause errors.") return None def save_audio(audio, file_path, sample_rate=16000): """ Save audio data to a file Args: audio (numpy.ndarray): Audio data file_path (str): Path to save the audio file sample_rate (int): Sampling rate of the audio """ try: # Normalize audio to avoid clipping audio = np.clip(audio, -1.0, 1.0) # Convert to int16 audio_int16 = (audio * 32767).astype(np.int16) # Save as WAV write(file_path, sample_rate, audio_int16) print(f"Audio saved to {file_path}") except Exception as e: print(f"Error saving audio: {e}") # Simplified FreeVCNode class - moved to nodes.py NODE_CLASS_MAPPINGS = { "FreeVC Voice Conversion": None # Defined in nodes.py } NODE_DISPLAY_NAME_MAPPINGS = { "FreeVC Voice Conversion": "FreeVC Voice Converter 🎤" }