diff --git a/example/readme/.gitkeep b/example/readme/.gitkeep new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/example/readme/.gitkeep @@ -0,0 +1 @@ + diff --git a/example/readme/baseline.wav b/example/readme/baseline.wav new file mode 100644 index 0000000..413007f Binary files /dev/null and b/example/readme/baseline.wav differ diff --git a/example/readme/generated.wav b/example/readme/generated.wav new file mode 100644 index 0000000..6cfa37f Binary files /dev/null and b/example/readme/generated.wav differ diff --git a/example/readme/original-clip.wav b/example/readme/original-clip.wav new file mode 100644 index 0000000..682244b Binary files /dev/null and b/example/readme/original-clip.wav differ diff --git a/main.py b/main.py index 48f6156..4bc4678 100644 --- a/main.py +++ b/main.py @@ -1,17 +1,35 @@ import argparse import os +import traceback from pathlib import Path import numpy as np import soundfile as sf +import torch from utilities.audio_processor import Transcriber, convert_to_wav_mono_24k from utilities.kvoicewalk import KVoiceWalk +from utilities.kvw_informer import KVW_Informer from utilities.pytorch_sanitizer import load_multiple_voices from utilities.speech_generator import SpeechGenerator def main(): + # import config settings + kvw_informer = KVW_Informer() + kvw_settings = kvw_informer.settings + log_view = kvw_settings["preprocessing_logs"] + use_cached = kvw_settings["use_cached"] + cap_memory = kvw_settings["cap_memory"] + cap_memory_frac = kvw_settings["cap_memory_frac"] + # After initial download, recommend use cached copies of models == faster load times + if use_cached: + os.environ['HF_HUB_OFFLINE'] = '1' # Force offline mode + os.environ['TRANSFORMERS_OFFLINE'] = '1' + # True: Limits excess memory overhead reservation, benchmarked at ~0.70GB throughout operation, no spikes + # Cap_memory_frac = 0.2, can be set 0-1, but recommend no lower than 0.15 + if cap_memory: torch.cuda.set_per_process_memory_fraction(cap_memory_frac) + parser = argparse.ArgumentParser(description="A random walk Kokoro voice cloner.") # Common required arguments @@ -78,6 +96,7 @@ def main(): # Handle target_audio input - convert to mono wav 24K automatically if args.target_audio: + if log_view is True: kvw_informer.log_gpu_memory("Preprocessing target audio file", log_view) try: target_audio_path = Path(args.target_audio) if target_audio_path.is_file(): @@ -89,6 +108,7 @@ def main(): # Transcribe (Start Mode) if args.transcribe_start: + if log_view is True: kvw_informer.log_gpu_memory("Transcribing target audio file", log_view) try: target_path = Path(args.target_audio) @@ -165,7 +185,8 @@ def main(): if not args.target_text: parser.error("--target_text is required when using --test_voice") - speech_generator = SpeechGenerator() + speech_generator = SpeechGenerator(kvw_informer=kvw_informer, target_text=args.target_text, + other_text=args.other_text) audio = speech_generator.generate_audio(args.target_text, args.test_voice) sf.write(args.output_name, audio, 24000) else: @@ -175,15 +196,21 @@ def main(): if not args.target_text: parser.error("--target_text is required for random walk mode") + if log_view is True: kvw_informer.log_gpu_memory("Initializing KVoicewalk", log_view) ktb = KVoiceWalk(args.target_audio, - args.target_text, - args.other_text, - args.voice_folder, - args.interpolate_start, - args.population_limit, + args.target_text, + args.other_text, + args.voice_folder, + args.interpolate_start, + args.population_limit, args.starting_voice, - args.output_name) - ktb.random_walk(args.step_limit) - + args.output_name, kvw_informer) + try: + ktb.random_walk(args.step_limit) + except Exception as e: + print("FULL TRACEBACK:") + traceback.print_exc() + print(f"\nERROR: {e}") + print(f"ERROR TYPE: {type(e)}") if __name__ == "__main__": main() diff --git a/pyproject.toml b/pyproject.toml index 62c5335..919e484 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "kvoicewalk" version = "0.1.0" -description = "Add your description here" +description = "A randomwalk through Kokoro latent space" readme = "README.md" requires-python = ">=3.10,<3.13" dependencies = [ @@ -12,4 +12,7 @@ dependencies = [ "soundfile>=0.13.1", "torch>=2.7.0", "tqdm>=4.67.1", + "faster-whisper>=1.1.1", + "speechbrain>=1.0.3", + "torchaudio>=2.7.0", ] diff --git a/utilities/audio_processor.py b/utilities/audio_processor.py index 72d2391..d3f7679 100644 --- a/utilities/audio_processor.py +++ b/utilities/audio_processor.py @@ -10,10 +10,10 @@ def convert_to_wav_mono_24k(audio_path: Path) -> Path: - print(f"Converting {audio_path.name} to Mono Wav 24K...") try: with sf.SoundFile(audio_path, 'r') as f: if f.format != 'WAV' or f.samplerate != 24000 or f.channels != 1: + print(f"Converting {audio_path.name} to Mono Wav 24K...") # Create output filename with proper audio format converted_audio_file = Path(CONVERTED_DIR / str(audio_path.stem + ".wav")) @@ -23,7 +23,7 @@ def convert_to_wav_mono_24k(audio_path: Path) -> Path: # Convert to mono if needed if f.channels > 1: converted_audio_data = np.mean(audio_data, axis=1) - print("Cenverted to Mono...") + # print("Cenverted to Mono...") else: converted_audio_data = audio_data @@ -34,14 +34,14 @@ def convert_to_wav_mono_24k(audio_path: Path) -> Path: orig_sr=f.samplerate, target_sr=24000 ) - print("Resampled to 24K...") + # print("Resampled to 24K...") # Save converted audio sf.write(converted_audio_file, converted_audio_data, samplerate=24000, format='WAV') - print(f"{audio_path.name} successfully converted to Mono WAV 24K format: {converted_audio_file}") + print(f"{audio_path.name} converted to Mono WAV 24K format: {converted_audio_file}") return converted_audio_file else: - print(f"{audio_path.name} matches Mono WAV 24K format") + # print(f"{audio_path.name} matches Mono WAV 24K format") return audio_path except Exception as e: @@ -51,7 +51,7 @@ def convert_to_wav_mono_24k(audio_path: Path) -> Path: class Transcriber: def __init__(self): model_size = "large-v3" - print('Starting Transcriber...') + # print('Starting Transcriber...') # Run on GPU with FP16 # model = WhisperModel(model_size, device="cuda", compute_type="float16") @@ -66,26 +66,24 @@ def transcribe(self, audio_path: Path): start_time = datetime.datetime.now() try: - print(f'Loading {audio_file.name}...') + # print(f'Loading {audio_file.name}...') segments, info = self.model.transcribe(str(audio_file), beam_size=5) print("Detected language '%s' with probability %f" % (info.language, info.language_probability)) print(f'Transcribing {audio_file.name}...') - transcription = '' + transcription = "" for segment in segments: - transcription += segment.text - # print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) # Optional timestamps if parsing longer audio clips + transcription += " " + segment.text.strip() transcription_output = Path(TEXTS_DIR / str(f"{audio_file.stem}.txt")) with open(str(transcription_output), "w") as file: - file.write(f"{transcription}") + file.write(f"{transcription[1:]}") end_time = datetime.datetime.now() - print(f"Transcription completed in {(end_time - start_time).total_seconds()} seconds") print(f"Transcription available at ./texts/{audio_file.name[:-4]}.txt") - print(f"{audio_file.name} Transcription:\n{transcription}") - return transcription + print(f"{audio_file.name} Transcription:\n{transcription[1:]}") + return transcription[1:] except Exception as e: print(f"Transcription failed for {audio_file.name} - Error: {e}") diff --git a/utilities/fitness_scorer.py b/utilities/fitness_scorer.py index f92ab53..43ef39e 100644 --- a/utilities/fitness_scorer.py +++ b/utilities/fitness_scorer.py @@ -1,211 +1,609 @@ -# from scipy.spatial.distance import cosine +import datetime +import warnings +from datetime import timedelta from typing import Any -import librosa import numpy as np -import scipy.stats -import soundfile as sf +import torch +import torchaudio from numpy._typing import NDArray -from resemblyzer import preprocess_wav, VoiceEncoder +from speechbrain.inference.speaker import SpeakerRecognition +from torch import FloatTensor +from torchaudio.prototype.transforms import ChromaSpectrogram +from utilities.kvw_informer import KVW_Informer +from utilities.speech_generator import SpeechGenerator + +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + + +# TODO: Review SpeechBrain Feature Extraction & Analysis +# TODO: Revisit Scoring Calculations class FitnessScorer: - def __init__(self,target_path: str): - self.encoder = VoiceEncoder() - self.target_audio, _ = sf.read(target_path,dtype="float32") - self.target_wav = preprocess_wav(target_path,source_sr=24000) - self.target_embed = self.encoder.embed_utterance(self.target_wav) - self.target_features = self.extract_features(self.target_audio) - - def hybrid_similarity(self, audio: NDArray[np.float32], audio2: NDArray[np.float32],target_similarity: float): - features = self.extract_features(audio) - self_similarity = self.self_similarity(audio,audio2) - target_features_pentalty = self.target_feature_penalty(features) - - #Normalize and make higher = better - feature_similarity = (100.0 - target_features_pentalty) / 100.0 + def __init__(self, target_wav: str, kvw_informer: KVW_Informer, speech_generator: SpeechGenerator, + device: str = 'cuda'): + """ + Initialize FitnessScorer with GPU optimization. + + Args: + target_wav: Path to target audio file + device: Device to use ('cuda' or 'cpu') + """ + self.kvw_informer = kvw_informer + self.speech_generator = speech_generator + self.log_view = self.kvw_informer.settings['fitness_logs'] + self.process_times = self.kvw_informer.settings['tps_reports'] + self.feature_times = self.kvw_informer.settings['feature_times'] + self.device = device if torch.cuda.is_available() else 'cpu' + self.target_wav = target_wav + + # Constants + self.sr = 24000 + self.n_fft = 2048 + self.n_mels = 128 + self.hop_length = 512 + self.fmin = 200 + self.n_bands = 6 + # Initialize Audio Analysis Classes and Objects on GPU + self.verification = SpeakerRecognition.from_hparams( + source="speechbrain/spkrec-ecapa-voxceleb", + run_opts={"device": self.device} + ) + + # Preload Frequency bins + self.freqs = torch.fft.fftfreq(self.n_fft, 1 / self.sr)[:self.n_fft // 2 + 1].to(device) + + # Preload Mel spectrogram + self.mel_transform = torchaudio.transforms.MelSpectrogram( + sample_rate=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels).to(device) + # Preload MFCCs + self.mfcc_transform = torchaudio.transforms.MFCC( + sample_rate=self.sr, n_mfcc=13, + melkwargs={'n_fft': self.n_fft, 'hop_length': self.hop_length, 'n_mels': self.n_mels}).to(device) + + # Preload Chroma transform + self.chroma_transform = ChromaSpectrogram(sample_rate=self.sr, n_fft=self.n_fft, hop_length=self.hop_length).to( + device) + + # Preload Tonnetz transformation matrix (6x12) - standard musicology matrix + self.tonnetz_matrix = torch.tensor([ + [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], # Circle of fifths + [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], # Circle of fifths offset + [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], # Minor thirds + [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], # Minor thirds offset + [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1], # Minor thirds offset2 + [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0] # Major thirds + ], dtype=torch.float32, device=device) + + self.bands = torch.logspace( + torch.log10(torch.tensor(self.fmin)), + torch.log10(torch.tensor(self.sr / 2)), + self.n_bands + 1).to(device) + + # Precompute band masks + self.band_masks = [] + for i in range(self.n_bands): + band_mask = (self.freqs >= self.bands[i]) & (self.freqs < self.bands[i + 1]) + self.band_masks.append(band_mask) + + # Preload contrast values + self.contrast_values = torch.zeros(self.n_bands, device=device) + + # Preload target audio tensor to GPU (do this once!) + self._target_tensor = self.verification.load_audio(target_wav).to(self.device) + self.batch_target = self._target_tensor.unsqueeze(0) + self.emb1 = self.verification.encode_batch(self.batch_target, None, normalize=False) + if self.log_view is True: self.kvw_informer.log_gpu_memory( + f"Target audio loaded to {self._target_tensor.device}", self.log_view) + + # Pre-compute target features for feature penalty calculation + target_audio_numpy = self._target_tensor.cpu().numpy() + self.target_features = self.extract_features(target_audio_numpy) + if self.log_view is True: self.kvw_informer.log_gpu_memory( + f"Extracted {len(self.target_features)} target features", self.log_view) + + def hybrid_similarity(self, best_results: dict[str, Any], audio_array: NDArray[np.float32] | torch.Tensor, + audio_embed1: torch.Tensor, other_text: str, + voice_tensor: NDArray[np.float32] | torch.Tensor, + target_similarity: float, results: dict[str, Any]) -> tuple[dict[ + str, Any], timedelta, timedelta, timedelta] | tuple[dict[str, Any], timedelta, float, float]: + """ + Calculate hybrid similarity score combining target similarity, self similarity, and feature similarity. + GPU-compatible version that accepts both numpy arrays and tensors. + + Args: + :param best_results: best voice scores + :param audio_array: First audio signal (numpy array or torch tensor) + :param audio_embed1: First audio embedding (passed from target_similarity + :param other_text: Comparison text for Audio 2 Self Sim check + :param voice_tensor: voice tensor to be used in Audio 2 Self Sim check + :param target_similarity: Pre-calculated target similarity score + :param results: results dict to return after scoring + + Returns: + Dictionary containing all similarity scores and final score + + """ + audio_tensor1 = audio_array + # Extract features using GPU-optimized method + # Target feature extraction + if self.log_view is True: self.kvw_informer.log_gpu_memory("Evaluating Feature Sim", self.log_view) + feature_start = datetime.datetime.now() + features = self.extract_features(audio_tensor1) + + # Calculate target feature penalty + if self.log_view is True: self.kvw_informer.log_gpu_memory("Evaluating Target Feature Penalty", self.log_view) + # target_penalty_start = datetime.datetime.now() + target_features_penalty = self.target_feature_penalty(features) + # target_penalty_time = datetime.datetime.now() - target_penalty_start + + # Normalize and make higher = better + feature_similarity = (100.0 - target_features_penalty) / 100.0 if feature_similarity < 0.0: feature_similarity = 0.01 + feature_time = datetime.datetime.now() - feature_start + + # Added a check for feature similarity within a certain bounds, to avoid audio2 gen if possible + if feature_similarity >= best_results["feature_similarity"] - 0.1: + audio2_start = datetime.datetime.now() + audio2_array = self.speech_generator.generate_audio(other_text, voice_tensor) + audio2_time = datetime.datetime.now() - audio2_start + + # Calculate self similarity with tensors + if self.log_view is True: self.kvw_informer.log_gpu_memory("Evaluating Self Sim", self.log_view) + self_sim_start = datetime.datetime.now() + self_similarity = self.self_similarity(audio_embed1, audio2_array) + self_sim_time = datetime.datetime.now() - self_sim_start + + # Prepare values for scoring + values = np.array([target_similarity, self_similarity, feature_similarity]) + + # Weights for potential future use (currently using unweighted harmonic mean) + # weights = np.array([0.48, 0.5, 0.02]) + + # Harmonic mean calculation (unweighted as per current implementation) + # Harmonic mean heavily penalizes low scores, encouraging balanced improvement + score = len(values) / np.sum(1.0 / values) + results.update({ + "score": float(score), + "target_similarity": float(target_similarity), + "self_similarity": float(self_similarity), + "feature_similarity": float(feature_similarity), + # "weights": weights.tolist() # Include weights for potential future use + }) + return results, feature_time, audio2_time, self_sim_time + else: + results.update({ + "score": 0.0, + "target_similarity": float(target_similarity), + "self_similarity": 0.0, + "feature_similarity": float(feature_similarity), + # "weights": weights.tolist() # Include weights for potential future use + }) + audio2_time = 0.0 + self_sim_time = 0.0 + + return results, feature_time, audio2_time, self_sim_time + + def target_similarity(self, audio_tensor: torch.Tensor | NDArray[np.float32]) -> tuple[float, FloatTensor, Any]: + """ + Calculate similarity between generated audio and target audio.voice = voice.to(device) + GPU-optimized version using direct tensor operations. + + Args: + audio_tensor: Generated audio (tensor or numpy array) + + Returns: + Similarity score (float) + """ + # Ensure input is a tensor on the correct device + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + if isinstance(audio_tensor, np.ndarray): + audio_float_tensor = torch.from_numpy(audio_tensor.astype(np.float32)).to(device) + else: + audio_float_tensor = audio_tensor.to(device).float() + + # Ensure mono audio + if len(audio_float_tensor.shape) > 1: + audio_float_tensor = torch.mean(audio_tensor, dim=-1) + + # Use preloaded target tensor (set in __init__) + if not hasattr(self, 'emb1'): + self._target_tensor = self.verification.load_audio(self.target_wav).to(device) + self.batch_target = self._target_tensor + self.emb1 = self.verification.encode_batch(self.batch_target, None, normalize=False) + + # Create batch for SpeechBrain + batch_audio = audio_float_tensor.unsqueeze(0) + emb2 = self.verification.encode_batch(batch_audio, None, normalize=False) + score = self.verification.similarity(self.emb1, emb2) + + return float(score[0]), audio_float_tensor, emb2 + + def target_feature_penalty(self, features: dict[str, Any]) -> float: + """ + Calculate penalty for differences in audio features compared to target features. + Optimized version with improved stability and error handling. + + Args: + features: Dictionary of extracted audio features + + Returns: + Penalty score (lower is better, 0 = perfect match) + """ + if not hasattr(self, 'target_features') or not self.target_features: + # If no target features are available, return neutral penalty + return 50.0 - values = [target_similarity, self_similarity, feature_similarity] - # Playing around with the weights can greatly affect scoring and random walk behavior - weights = [0.48,0.5,0.02] - score = (np.sum(weights) / np.sum(np.array(weights) / np.array(values))) * 100.0 - - return { - "score": score, - "target_similarity": target_similarity, - "self_similarity": self_similarity, - "feature_similarity": feature_similarity - } - - def target_similarity(self,audio: NDArray[np.float32]) -> float: - audio_wav = preprocess_wav(audio,source_sr=24000) - audio_embed = self.encoder.embed_utterance(audio_wav) - similarity = np.inner(audio_embed, self.target_embed) - return similarity - - def target_feature_penalty(self,features: dict[str, Any]) -> float: - """Penalizes for differences in audio features""" - # Normalized feature difference compared to target features penalty = 0.0 + feature_count = 0 + for key, value in features.items(): - diff = abs((value - self.target_features[key])/self.target_features[key]) - penalty += diff - return penalty + if key not in self.target_features: + # Skip features that don't exist in target (for forward compatibility) + continue + + target_val = self.target_features[key] + + # Handle different cases for robust calculation + try: + # Convert to float if needed (handles numpy types) + current_val = float(value) + target_val = float(target_val) + + # Handle near-zero or zero targets + if abs(target_val) < 1e-8: + # For near-zero targets, use absolute difference + diff = abs(current_val) + # Scale by a reasonable factor to keep penalties in reasonable range + diff = min(diff * 100, 5.0) # Cap at 500% penalty equivalent + else: + # For non-zero targets, use relative difference + diff = abs((current_val - target_val) / target_val) + # Cap maximum penalty per feature to prevent extreme outliers + diff = min(diff, 5.0) # Max 500% difference penalty + + penalty += diff + feature_count += 1 + + except (ValueError, TypeError, ZeroDivisionError): + # Skip problematic features rather than crashing + continue + + if feature_count == 0: + # No valid features to compare + return 50.0 + + # Average penalty across features, convert to percentage scale + average_penalty = penalty / feature_count + return float(average_penalty * 100.0) + + def self_similarity(self, audio_tensor1: torch.Tensor | NDArray[np.float32], + audio_tensor2: torch.Tensor | NDArray[np.float32]) -> float: + """ + Calculate self-similarity between two audio samples from the same voice. + GPU-optimized version using direct tensor operations. + + Args: + audio_tensor1: First audio sample (tensor or numpy array) + audio_tensor2: Second audio sample (tensor or numpy array) + + Returns: + Self-similarity score (float) + """ + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + if isinstance(audio_tensor2, np.ndarray): + audio_tensor2 = torch.from_numpy(audio_tensor2.astype(np.float32)).to(device) + else: + audio_tensor2 = audio_tensor2.to(device).float() - def self_similarity(self,audio1: NDArray[np.float32], audio2: NDArray[np.float32]) -> float: - """Self similarity indicates model stability. Poor self similarity means different input makes different sounding voices""" - audio_wav1 = preprocess_wav(audio1,source_sr=24000) - audio_embed1 = self.encoder.embed_utterance(audio_wav1) + if len(audio_tensor2.shape) > 1: + audio_tensor2 = torch.mean(audio_tensor2, dim=-1) - audio_wav2 = preprocess_wav(audio2,source_sr=24000) - audio_embed2 = self.encoder.embed_utterance(audio_wav2) - return np.inner(audio_embed1, audio_embed2) + # Create batches + batch2 = audio_tensor2.unsqueeze(0) - def extract_features(self, audio: NDArray[np.float32] | NDArray[np.float64], sr: int = 24000) -> dict[str, Any]: + # Verify on GPU + emb1 = audio_tensor1 + emb2 = self.verification.encode_batch(batch2, None, normalize=False) + score = self.verification.similarity(emb1, emb2) + + return float(score[0]) + + def extract_features(self, audio_array: NDArray[np.float32] | NDArray[np.float64] | torch.Tensor) -> dict[str, Any]: """ Extract a comprehensive set of audio features for fingerprinting speech segments. + GPU-optimized version using torchaudio where possible. Args: - audio: Audio signal as numpy array (np.float32) - sr: Sample rate (fixed at 24000 Hz) + audio_array: Audio signal as numpy array or torch tensor Returns: Dictionary containing extracted features """ - # Ensure audio is the right shape (flatten stereo to mono if needed) - if len(audio.shape) > 1 and audio.shape[1] > 1: - audio = np.mean(audio, axis=1) + start = datetime.datetime.now() + # Convert input to tensor and ensure it's on GPU + if isinstance(audio_array, np.ndarray): + audio_tensor = torch.from_numpy(audio_array.astype(np.float32)).to(self.device) + else: + audio_tensor = audio_array.to(self.device).float() + + # Ensure mono + if len(audio_tensor.shape) > 1: + if audio_tensor.shape[-1] > 1: + audio_tensor = torch.mean(audio_tensor, dim=-1) + else: + audio_tensor = audio_tensor.squeeze() # Initialize features dictionary features = {} - - # Basic features - # features["duration"] = len(audio) / sr - features["rms_energy"] = float(np.sqrt(np.mean(audio**2))) - features["zero_crossing_rate"] = float(np.mean(librosa.feature.zero_crossing_rate(audio))) - - # Spectral features - # Compute STFT - n_fft = 2048 # Window size - hop_length = 512 # Hop length - - # Spectral centroid and bandwidth (where the "center" of the sound is) - spectral_centroids = librosa.feature.spectral_centroid(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length)[0] - features["spectral_centroid_mean"] = float(np.mean(spectral_centroids)) - features["spectral_centroid_std"] = float(np.std(spectral_centroids)) - - spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length)[0] - features["spectral_bandwidth_mean"] = float(np.mean(spectral_bandwidth)) - features["spectral_bandwidth_std"] = float(np.std(spectral_bandwidth)) - - # Spectral rolloff - rolloff = librosa.feature.spectral_rolloff(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length)[0] - features["spectral_rolloff_mean"] = float(np.mean(rolloff)) - features["spectral_rolloff_std"] = float(np.std(rolloff)) - - # Spectral contrast - contrast = librosa.feature.spectral_contrast(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length) - features["spectral_contrast_mean"] = float(np.mean(contrast)) - features["spectral_contrast_std"] = float(np.std(contrast)) - - # MFCCs (Mel-frequency cepstral coefficients) - important for speech - mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13, n_fft=n_fft, hop_length=hop_length) - - # Store each MFCC coefficient mean and std - for i in range(len(mfccs)): - features[f"mfcc{i+1}_mean"] = float(np.mean(mfccs[i])) - features[f"mfcc{i+1}_std"] = float(np.std(mfccs[i])) - - # MFCC delta features (first derivative) - mfcc_delta = librosa.feature.delta(mfccs) - for i in range(len(mfcc_delta)): - features[f"mfcc{i+1}_delta_mean"] = float(np.mean(mfcc_delta[i])) - features[f"mfcc{i+1}_delta_std"] = float(np.std(mfcc_delta[i])) - - # Chroma features - useful for characterizing harmonic content - chroma = librosa.feature.chroma_stft(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length) - features["chroma_mean"] = float(np.mean(chroma)) - features["chroma_std"] = float(np.std(chroma)) - - # Store individual chroma features - for i in range(len(chroma)): - features[f"chroma_{i+1}_mean"] = float(np.mean(chroma[i])) - features[f"chroma_{i+1}_std"] = float(np.std(chroma[i])) - - # Mel spectrogram (average across frequency bands) - mel_spec = librosa.feature.melspectrogram(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length) - features["mel_spec_mean"] = float(np.mean(mel_spec)) - features["mel_spec_std"] = float(np.std(mel_spec)) - - # Spectral flatness - measure of the noisiness of the signal - flatness = librosa.feature.spectral_flatness(y=audio, n_fft=n_fft, hop_length=hop_length)[0] - features["spectral_flatness_mean"] = float(np.mean(flatness)) - features["spectral_flatness_std"] = float(np.std(flatness)) - - # Tonnetz (tonal centroid features) - tonnetz = librosa.feature.tonnetz(y=audio, sr=sr) - features["tonnetz_mean"] = float(np.mean(tonnetz)) - features["tonnetz_std"] = float(np.std(tonnetz)) - - # Rhythm features - tempo and beat strength - tempo, beat_frames = librosa.beat.beat_track(y=audio, sr=sr) - features["tempo"] = float(tempo) - - if len(beat_frames) > 0: - # Calculate beat_stats only if beats are detected - beat_times = librosa.frames_to_time(beat_frames, sr=sr) - if len(beat_times) > 1: - beat_diffs = np.diff(beat_times) - features["beat_mean"] = float(np.mean(beat_diffs)) - features["beat_std"] = float(np.std(beat_diffs)) - else: - features["beat_mean"] = 0.0 - features["beat_std"] = 0.0 - else: - features["beat_mean"] = 0.0 - features["beat_std"] = 0.0 - - # Pitch and harmonics - pitches, magnitudes = librosa.core.piptrack(y=audio, sr=sr, n_fft=n_fft, hop_length=hop_length) - - # For each frame, find the highest magnitude pitch - pitch_values = [] - for i in range(magnitudes.shape[1]): - index = magnitudes[:, i].argmax() - pitch = pitches[index, i] - if pitch > 0: # Exclude zero pitch - pitch_values.append(pitch) - - if pitch_values: - features["pitch_mean"] = float(np.mean(pitch_values)) - features["pitch_std"] = float(np.std(pitch_values)) - else: - features["pitch_mean"] = 0.0 - features["pitch_std"] = 0.0 - - # Speech-specific features - - # Voice Activity Detection (simplified) - # Higher energies typically indicate voice activity - energy = np.array([sum(abs(audio[i:i+hop_length])) for i in range(0, len(audio), hop_length)]) - features["energy_mean"] = float(np.mean(energy)) - features["energy_std"] = float(np.std(energy)) - - # Harmonics-to-noise ratio (simplified approximation) - # Using the squared magnitude of the spectrogram - S = np.abs(librosa.stft(audio, n_fft=n_fft, hop_length=hop_length)) - S_squared = S**2 - S_mean = np.mean(S_squared, axis=1) - S_std = np.std(S_squared, axis=1) - S_ratio = np.divide(S_mean, S_std, out=np.zeros_like(S_mean), where=S_std!=0) - features["harmonic_ratio"] = float(np.mean(S_ratio)) - - # Statistical features from the raw waveform - features["audio_mean"] = float(np.mean(audio)) - features["audio_std"] = float(np.std(audio)) - features["audio_skew"] = float(scipy.stats.skew(audio)) - features["audio_kurtosis"] = float(scipy.stats.kurtosis(audio)) + if self.feature_times is True: print( + f"Feature Prep Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + # ===== GPU-ACCELERATED FEATURES ===== + with torch.no_grad(): + start = datetime.datetime.now() + # Basic features - GPU + features["rms_energy"] = float(torch.sqrt(torch.mean(audio_tensor ** 2)).item()) + + # Zero crossing rate - GPU implementation + zero_crossings = torch.diff(torch.sign(audio_tensor), dim=0) + features["zero_crossing_rate"] = float(torch.mean(torch.abs(zero_crossings)).item() / 2.0) + + # STFT for spectral features - GPU + stft = torch.stft(audio_tensor, n_fft=self.n_fft, hop_length=self.hop_length, + win_length=self.n_fft, return_complex=True, center=True) + magnitude = torch.abs(stft) + power = magnitude ** 2 + + # Spectral centroid - GPU + weighted_freqs = self.freqs.unsqueeze(1) * magnitude + spectral_centroids = torch.sum(weighted_freqs, dim=0) / (torch.sum(magnitude, dim=0) + 1e-8) + features["spectral_centroid_mean"] = float(torch.mean(spectral_centroids).item()) + features["spectral_centroid_std"] = float(torch.std(spectral_centroids).item()) + + # Spectral bandwidth - GPU + freq_diff = (self.freqs.unsqueeze(1) - spectral_centroids.unsqueeze(0)) ** 2 + spectral_bandwidth = torch.sqrt( + torch.sum(freq_diff * magnitude, dim=0) / (torch.sum(magnitude, dim=0) + 1e-8)) + features["spectral_bandwidth_mean"] = float(torch.mean(spectral_bandwidth).item()) + features["spectral_bandwidth_std"] = float(torch.std(spectral_bandwidth).item()) + + # Spectral rolloff - GPU (85% of spectral energy) + cumsum_power = torch.cumsum(power, dim=0) + total_power = torch.sum(power, dim=0) + rolloff_thresh = 0.85 * total_power + rolloff_indices = torch.argmax((cumsum_power >= rolloff_thresh.unsqueeze(0)).float(), dim=0) + rolloff_freqs = self.freqs[rolloff_indices] + features["spectral_rolloff_mean"] = float(torch.mean(rolloff_freqs).item()) + features["spectral_rolloff_std"] = float(torch.std(rolloff_freqs).item()) + + mel_spec = self.mel_transform(audio_tensor.unsqueeze(0)).squeeze(0) + features["mel_spec_mean"] = float(torch.mean(mel_spec).item()) + features["mel_spec_std"] = float(torch.std(mel_spec).item()) + + if self.feature_times is True: print( + f"Spectral Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + mfccs = self.mfcc_transform(audio_tensor.unsqueeze(0)).squeeze(0) + + # Store each MFCC coefficient mean and std + mfcc_means = torch.mean(mfccs, dim=1) + mfcc_stds = torch.std(mfccs, dim=1) + + # MFCC delta features (first derivative) - GPU + mfcc_delta = torch.diff(mfccs, dim=1, prepend=mfccs[:, :1]) + mfcc_delta_means = torch.mean(mfcc_delta, dim=1) + mfcc_delta_stds = torch.std(mfcc_delta, dim=1) + + # Then just index into the results + for i in range(13): + features[f"mfcc{i + 1}_mean"] = float(mfcc_means[i].item()) + features[f"mfcc{i + 1}_std"] = float(mfcc_stds[i].item()) + + for i in range(13): + features[f"mfcc{i + 1}delta_mean"] = float(mfcc_delta_means[i].item()) + features[f"mfcc{i + 1}delta_std"] = float(mfcc_delta_stds[i].item()) + + if self.feature_times is True: print( + f"MFCC Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + # Spectral flatness - GPU + geometric_mean = torch.exp(torch.mean(torch.log(magnitude + 1e-8), dim=0)) + arithmetic_mean = torch.mean(magnitude, dim=0) + flatness = geometric_mean / (arithmetic_mean + 1e-8) + features["spectral_flatness_mean"] = float(torch.mean(flatness).item()) + features["spectral_flatness_std"] = float(torch.std(flatness).item()) + + # Spectral contrast - GPU implementation + for i, band_mask in enumerate(self.band_masks): # Add the missing loop + if torch.sum(band_mask) == 0: + self.contrast_values[i] = 0.0 + continue + + # Get magnitude in this band + band_mag = magnitude[band_mask, :] # Use the current band_mask from loop + + # Calculate contrast (peak vs valley) + if band_mag.numel() > 0: + # Flatten for easier processing + band_flat = band_mag.flatten() + + # Peak: mean of top 20% values + top_k = max(1, int(0.2 * band_flat.shape[0])) + peaks, _ = torch.topk(band_flat, top_k, dim=0) # Fix: band_flat not band*flat + peak_val = torch.mean(peaks) + + # Valley: mean of bottom 20% values + bottom_k = max(1, int(0.2 * band_flat.shape[0])) + valleys, _ = torch.topk(band_flat, bottom_k, dim=0, largest=False) # Fix: band_flat + valley_val = torch.mean(valleys) + + # Contrast ratio + self.contrast_values[i] = peak_val / (valley_val + 1e-8) + else: + self.contrast_values[i] = 0.0 + + # After the loop, compute final features + features["spectral_contrast_mean"] = float(torch.mean(self.contrast_values).item()) + features["spectral_contrast_std"] = float(torch.std(self.contrast_values).item()) + + if self.feature_times is True: print( + f"Spectral Flatness and Contrast Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + # Energy features - GPU + frame_length = self.hop_length + frames = audio_tensor.unfold(0, frame_length, self.hop_length) + energy = torch.sum(torch.abs(frames), dim=1) + features["energy_mean"] = float(torch.mean(energy).item()) + features["energy_std"] = float(torch.std(energy).item()) + + # Harmonics-to-noise ratio - GPU + S_squared = magnitude ** 2 + S_mean = torch.mean(S_squared, dim=1) + S_std = torch.std(S_squared, dim=1) + S_ratio = S_mean / (S_std + 1e-8) + features["harmonic_ratio"] = float(torch.mean(S_ratio).item()) + + # Statistical features from raw waveform - GPU + features["audio_mean"] = float(torch.mean(audio_tensor).item()) + features["audio_std"] = float(torch.std(audio_tensor).item()) + + if self.feature_times is True: print( + f"Energy Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + # Chroma features - GPU using torchaudio.prototype + chroma = self.chroma_transform(audio_tensor.unsqueeze(0)).squeeze(0) + + # Overall chroma statistics + chroma_mean = torch.mean(chroma) + chroma_std = torch.std(chroma) + features["chroma_mean"] = float(chroma_mean.item()) + features["chroma_std"] = float(chroma_std.item()) + + # Per-chroma-bin statistics (same pattern as MFCC) + chroma_means = torch.mean(chroma, dim=1) + chroma_stds = torch.std(chroma, dim=1) + + # Store individual chroma features + for i in range(chroma.shape[0]): + features[f"chroma_{i + 1}_mean"] = float(chroma_means[i].item()) + features[f"chroma_{i + 1}_std"] = float(chroma_stds[i].item()) + + # Normalize chroma (avoid division by zero) + chroma_norm = chroma / (torch.sum(chroma, dim=0, keepdim=True) + 1e-8) + + if self.feature_times is True: print( + f"Chroma Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + # Apply Tonnetz transformation + tonnetz = torch.matmul(self.tonnetz_matrix, chroma_norm) # [6, time] + + # Extract features + features["tonnetz_mean"] = float(torch.mean(tonnetz).item()) + features["tonnetz_std"] = float(torch.std(tonnetz).item()) + + if self.feature_times is True: print( + f"Tonnetz Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + # Statistical features - GPU implementation + audio_mean = torch.mean(audio_tensor) + audio_std = torch.std(audio_tensor) + normalized = (audio_tensor - audio_mean) / (audio_std + 1e-8) + + # Skewness (third moment) + skewness = torch.mean(normalized ** 3) + features["audio_skew"] = float(skewness.item()) + + # Kurtosis (fourth moment minus 3) + kurtosis = torch.mean(normalized ** 4) - 3.0 + features["audio_kurtosis"] = float(kurtosis.item()) + + if self.feature_times is True: print( + f"Statistics Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + start = datetime.datetime.now() + + # Extract Kaldi pitch for better quality filtering + try: + # compute_kaldi_pitch expects [channels, samples] format + if audio_tensor.dim() == 1: + audio_for_pitch = audio_tensor.unsqueeze(0) + else: + audio_for_pitch = audio_tensor + + # Kaldi pitch computation + pitch = torchaudio.functional.detect_pitch_frequency( + audio_for_pitch, + sample_rate=self.sr + ) + + # pitch shape: [channels, frames] - pitch values in Hz + pitch_hz = pitch[0, :] # Get pitch values from first channel + + # Filter valid pitches (detect_pitch_frequency returns 0 for unvoiced) + valid_mask = pitch_hz > 0 + + if torch.sum(valid_mask) > 0: + valid_pitches = pitch_hz[valid_mask] + features["pitch_mean"] = float(torch.mean(valid_pitches).item()) + features["pitch_std"] = float(torch.std(valid_pitches).item()) + # No confidence score available with detect_pitch_frequency + features["pitch_confidence_mean"] = 1.0 # Placeholder since all detected pitches are "confident" + else: + features["pitch_mean"] = 0.0 + features["pitch_std"] = 0.0 + features["pitch_confidence_mean"] = 0.0 + + except Exception as e: + print(f"Pitch detection failed: {e}") + features["pitch_mean"] = 0.0 + features["pitch_std"] = 0.0 + features["pitch_confidence_mean"] = 0.0 + + if self.feature_times is True: print( + f"Pitch Analysis Time: {(datetime.datetime.now() - start).total_seconds():.3f}") + + # TODO: add tempo, rhythm support... BeatNet won't work for this! + # # BeatNet expects numpy format + # audio_numpy = audio_tensor.cpu().numpy() + # + # # Process audio - BeatNet returns beat times directly + # output = self.beat_tracker.process(audio_numpy, self.sr) + # + # if len(output) > 0: + # # BeatNet output format: [beat_times] or [(beat_times, tempo)] + # if isinstance(output[0], tuple): + # beat_times, tempo = output[0] + # else: + # beat_times = output + # # Estimate tempo from beat intervals + # if len(beat_times) > 1: + # beat_intervals = np.diff(beat_times) + # tempo = 60.0 / np.mean(beat_intervals) + # else: + # tempo = 120.0 # Default + # + # features["tempo"] = float(tempo) + # + # if len(beat_times) > 1: + # beat_diffs = np.diff(beat_times) + # features["beat_mean"] = float(np.mean(beat_diffs)) + # features["beat_std"] = float(np.std(beat_diffs)) + # else: + # features["beat_mean"] = 0.0 + # features["beat_std"] = 0.0 + # else: + # features["tempo"] = 120.0 # Default tempo + # features["beat_mean"] = 0.0 + # features["beat_std"] = 0.0 return features diff --git a/utilities/initial_selector.py b/utilities/initial_selector.py index 4c03289..368b215 100644 --- a/utilities/initial_selector.py +++ b/utilities/initial_selector.py @@ -1,18 +1,22 @@ import os +from typing import Any import numpy as np import torch -from utilities.fitness_scorer import FitnessScorer +from utilities.kvw_informer import KVW_Informer from utilities.path_router import INTERPOLATED_DIR from utilities.pytorch_sanitizer import load_voice_safely -from utilities.speech_generator import SpeechGenerator class InitialSelector: - def __init__(self,target_path: str, target_text: str, other_text: str, voice_folder: str = "./voices",) -> None: - self.fitness_scorer = FitnessScorer(target_path) - self.speech_generator = SpeechGenerator() + def __init__(self, fitness_scorer: Any, speech_generator: Any, target_path: str, target_text: str, other_text: str, + kvw_informer: KVW_Informer, voice_folder: str = "./voices") -> None: + self.kvw_informer = kvw_informer + self.log_view = self.kvw_informer.settings['voice_loading_logs'] + self.process_times = self.kvw_informer.settings['tps_reports'] + self.fitness_scorer = fitness_scorer + self.speech_generator = speech_generator voices = [] for filename in os.listdir(voice_folder): if filename.endswith('.pt'): @@ -29,12 +33,23 @@ def __init__(self,target_path: str, target_text: str, other_text: str, voice_fol def top_performer_start(self,population_limit: int) -> list[torch.Tensor]: """Simple top performer search to find best voices to use in random walk""" + + results = {} + if self.log_view is True: self.kvw_informer.log_gpu_memory("Before top_performer_start call", self.log_view) for voice in self.voices: + best_results = { + "score": 0.0, + "target_similarity": 0.0, + "self_similarity": 0.0, + "feature_similarity": 0.0, + } audio = self.speech_generator.generate_audio(self.target_text, voice["voice"]) - audio2 = self.speech_generator.generate_audio(self.other_text, voice["voice"]) - target_similarity = self.fitness_scorer.target_similarity(audio) - results = self.fitness_scorer.hybrid_similarity(audio,audio2,target_similarity) - print(f'{voice["name"]:<30} Target Sim:{results["target_similarity"]:.3f} Self Sim:{results["self_similarity"]:.3f} Feature Sim:{results["feature_similarity"]:.2f} Score:{results["score"]:.2f}') + target_similarity, audio_float_tensor, audio_embed1 = self.fitness_scorer.target_similarity(audio) + results, _, _, _ = self.fitness_scorer.hybrid_similarity(best_results, audio_float_tensor, audio_embed1, + self.other_text, voice["voice"], target_similarity, + results) + if self.log_view is True: print( + f'{voice["name"]:<30} Target Sim:{results["target_similarity"]:.3f} Self Sim:{results["self_similarity"]:.3f} Feature Sim:{results["feature_similarity"]:.2f} Score:{results["score"]:.2f}') voice["results"] = results voices = sorted(self.voices, key=lambda x: x["results"]["score"],reverse=True) @@ -44,16 +59,26 @@ def top_performer_start(self,population_limit: int) -> list[torch.Tensor]: print(f'{voice["name"]:<30} Target Sim:{voice["results"]["target_similarity"]:.3f} Self Sim:{voice["results"]["self_similarity"]:.3f} Feature Sim:{voice["results"]["feature_similarity"]:.2f} Score:{voice["results"]["score"]:.2f}') tensors = [voice["voice"]for voice in voices] + if self.log_view is True: self.kvw_informer.log_gpu_memory("After top_performer_start call", self.log_view) return tensors def interpolate_search(self,population_limit: int) -> list[torch.Tensor]: """Finds an initial population of voices more optimal because of interpolated features""" + results = {} for voice in self.voices: + best_results = { + "score": 0.0, + "target_similarity": 0.0, + "self_similarity": 0.0, + "feature_similarity": 0.0, + } audio = self.speech_generator.generate_audio(self.target_text, voice["voice"]) - audio2 = self.speech_generator.generate_audio(self.other_text, voice["voice"]) - target_similarity = self.fitness_scorer.target_similarity(audio) - results = self.fitness_scorer.hybrid_similarity(audio,audio2,target_similarity) - print(f'{voice["name"]:<20} Target Sim:{results["target_similarity"]:.3f}, Self Sim:{results["self_similarity"]:.3f}, Feature Sim:{results["feature_similarity"]:.2f}, Score:{results["score"]:.2f}') + target_similarity, audio_float_tensor, audio_embed1 = self.fitness_scorer.target_similarity(audio) + results, _, _, _ = self.fitness_scorer.hybrid_similarity(best_results, audio_float_tensor, audio_embed1, + self.other_text, voice["voice"], target_similarity, + results) + if self.log_view is True: print( + f'{voice["name"]:<20} Target Sim:{results["target_similarity"]:.3f}, Self Sim:{results["self_similarity"]:.3f}, Feature Sim:{results["feature_similarity"]:.2f}, Score:{results["score"]:.2f}') voice["results"] = results voices = sorted(self.voices, key=lambda x: x["results"]["score"],reverse=True) @@ -62,7 +87,6 @@ def interpolate_search(self,population_limit: int) -> list[torch.Tensor]: for voice in voices: print(f'{voice["name"]:<20} Target Sim:{voice["results"]["target_similarity"]:.3f}, Self Sim:{voice["results"]["self_similarity"]:.3f}, Feature Sim:{voice["results"]["feature_similarity"]:.2f}, Score: {voice["results"]["score"]:.2f}') - res = {} print("Interpolating Best Voices:") for i in range(len(voices)): @@ -70,9 +94,12 @@ def interpolate_search(self,population_limit: int) -> list[torch.Tensor]: for iter in np.arange(-1.5,1.5 + 0.01,0.1): voice = interpolate(voices[i]["voice"], voices[j]["voice"], iter) audio = self.speech_generator.generate_audio(self.target_text, voice) - audio2 = self.speech_generator.generate_audio(self.other_text, voice) - target_similarity = self.fitness_scorer.target_similarity(audio) - results = self.fitness_scorer.hybrid_similarity(audio,audio2,target_similarity) + target_similarity, audio_float_tensor, audio_embed1 = self.fitness_scorer.target_similarity(audio) + results, _, _, _ = self.fitness_scorer.hybrid_similarity(best_results, audio_float_tensor, + audio_embed1, + self.other_text, voice, target_similarity, + results) + print(f'{i:<3} {j:<3} {iter:<4.2f} {voices[i]["name"] or "N/A":<10} {voices[j]["name"] or "N/A":<10} Target Sim:{results.get("target_similarity", 0):.3f}, Self Sim:{results.get("self_similarity", 0):.3f}, Feature Sim:{results.get("feature_similarity", 0):.2f}, Score:{results.get("score", 0):.2f}') if i not in res and iter <= 0.0: diff --git a/utilities/kvoicewalk.py b/utilities/kvoicewalk.py index 9459d10..49f5b3e 100644 --- a/utilities/kvoicewalk.py +++ b/utilities/kvoicewalk.py @@ -1,15 +1,19 @@ import datetime +import gc import os import random +import traceback from pathlib import Path from typing import Any import soundfile as sf import torch +from torch import Tensor from tqdm import tqdm from utilities.fitness_scorer import FitnessScorer from utilities.initial_selector import InitialSelector +from utilities.kvw_informer import KVW_Informer from utilities.path_router import OUT_DIR from utilities.speech_generator import SpeechGenerator from utilities.voice_generator import VoiceGenerator @@ -17,33 +21,64 @@ class KVoiceWalk: def __init__(self, target_audio: Path, target_text: str, other_text: str, voice_folder: str, - interpolate_start: bool, population_limit: int, starting_voice: str, output_name: str) -> None: + interpolate_start: bool, population_limit: int, starting_voice: str, output_name: str, + kvw_informer: KVW_Informer) -> None: try: + self.kvw_informer = kvw_informer + self.log_view = self.kvw_informer.settings['scoring_results_logs'] + self.process_times = self.kvw_informer.settings['tps_reports'] + self.memcache_clear_freq = self.kvw_informer.settings['memcache_clear_iteration_freq'] self.target_audio = target_audio self.target_text = target_text self.other_text = other_text - self.initial_selector = InitialSelector(str(target_audio), target_text, other_text, - voice_folder=voice_folder) - voices: list[torch.Tensor] = [] + if self.log_view is True: self.kvw_informer.log_gpu_memory("Initializing Speech Generator", self.log_view) + self.speech_generator = SpeechGenerator(kvw_informer=self.kvw_informer, target_text=target_text, + other_text=other_text) + if self.log_view is True: self.kvw_informer.log_gpu_memory("Scoring target audio", self.log_view) + self.fitness_scorer = FitnessScorer(str(target_audio), kvw_informer=self.kvw_informer, + speech_generator=self.speech_generator) + try: + if self.log_view is True: self.kvw_informer.log_gpu_memory("Selecting voices", self.log_view) + self.initial_selector = InitialSelector(self.fitness_scorer, self.speech_generator, str(target_audio), + target_text, other_text, kvw_informer, + voice_folder=voice_folder) + except Exception as e: + raise Exception(f"Error: {e}") + + if self.log_view is True: self.kvw_informer.log_gpu_memory("Selecting starting voices", self.log_view) if interpolate_start: voices = self.initial_selector.interpolate_search(population_limit) else: voices = self.initial_selector.top_performer_start(population_limit) - self.speech_generator = SpeechGenerator() - self.fitness_scorer = FitnessScorer(str(target_audio)) - self.voice_generator = VoiceGenerator(voices, starting_voice) - # Either the mean or the supplied voice tensor + if self.log_view is True: self.kvw_informer.log_gpu_memory("Initializing Voice Generator", self.log_view) + self.voice_generator = VoiceGenerator(kvw_informer, voices, starting_voice) self.starting_voice = self.voice_generator.starting_voice + self.clear_losers_from_memory() + if self.log_view is True: self.kvw_informer.log_gpu_memory("After voice_clear during initialization", + self.log_view) self.output_name = output_name + except Exception as e: + print("FULL TRACEBACK:") + traceback.print_exc() + print(f"\nERROR: {e}") + print(f"ERROR TYPE: {type(e)}") print(f"Error initializing KVoicewalk: {e}") + raise SystemExit def random_walk(self,step_limit: int): - + if self.log_view is True: self.kvw_informer.log_gpu_memory("Scoring initial voice", self.log_view) # Score Initial Voice - best_voice = self.starting_voice - best_results = self.score_voice(self.starting_voice) t = tqdm() + best_voice = self.starting_voice + best_results = { + "score": 0.0, + "target_similarity": 0.0, + "self_similarity": 0.0, + "feature_similarity": 0.0, + } + best_results, tps_report = self.score_voice(best_results=best_results, voice_tensor=self.starting_voice, + min_similarity=-100.0) t.write(f'Target Sim:{best_results["target_similarity"]:.3f}, Self Sim:{best_results["self_similarity"]:.3f}, Feature Sim:{best_results["feature_similarity"]:.2f}, Score:{best_results["score"]:.2f}') # Create Results Directory @@ -52,52 +87,120 @@ def random_walk(self,step_limit: int): os.makedirs(results_dir, exist_ok=True) # Random Walk Loop - - for i in tqdm(range(step_limit)): + if self.log_view is True: self.kvw_informer.log_gpu_memory("Starting random walk run", self.log_view) + progress_bar = tqdm(range(step_limit), desc="KVoiceWalk Progress") + for i in progress_bar: # TODO: Expose to CLI diversity = random.uniform(0.01,0.15) - voice = self.voice_generator.generate_voice(best_voice,diversity) + if self.log_view is True: self.kvw_informer.log_gpu_memory("Generating best voice comparison", + self.log_view) + voice = self.voice_generator.generate_voice(best_voice, diversity) # Early function return saves audio generation compute min_similarity = best_results["target_similarity"] * 0.98 - voice_results = self.score_voice(voice,min_similarity) + if self.log_view is True: self.kvw_informer.log_gpu_memory("Scoring Voice results", self.log_view) + voice_results, tps_report = self.score_voice(best_results, voice, min_similarity) + + # Check GPU memory + info = self.kvw_informer.log_gpu_memory("GPU Stats", view=self.log_view, console=True) + progress_bar.set_postfix_str(f"{info}]") + # Per config every # of steps, clear Kpipeline cache + if i % self.memcache_clear_freq == 0 and i > 0: + self.clear_losers_from_memory() # Set new winner if score is better if voice_results["score"] > best_results["score"]: best_results = voice_results - best_voice = voice - t.write(f'Step:{i:<4} Target Sim:{best_results["target_similarity"]:.3f} Self Sim:{best_results["self_similarity"]:.3f} Feature Sim:{best_results["feature_similarity"]:.3f} Score:{best_results["score"]:.2f} Diversity:{diversity:.2f}') + best_voice = voice.cpu() + try: + t.write( + f'Step:{i:<4} Target Sim:{best_results["target_similarity"]:.3f} Self Sim:{best_results["self_similarity"]:.3f} Feature Sim:{best_results["feature_similarity"]:.3f} Score:{best_results["score"]:.2f} Diversity:{diversity:.2f}') + if self.process_times is True: t.set_postfix_str(f"{info}\n{tps_report}") + except Exception: + print("") # Save results so folks can listen + best_voice_name = f'{results_dir}/{self.output_name}_{i}_{best_results["score"]:.2f}_{best_results["target_similarity"]:.2f}_{self.target_audio.stem}.pt' torch.save(best_voice, - f'{results_dir}/{self.output_name}_{i}_{best_results["score"]:.2f}_{best_results["target_similarity"]:.2f}_{self.target_audio.stem}.pt') + best_voice_name) sf.write( f'{results_dir}/{self.output_name}_{i}_{best_results["score"]:.2f}_{best_results["target_similarity"]:.2f}_{self.target_audio.stem}.wav', best_results["audio"], 24000) # TODO: Add config file for easy restarting runs from last save point # Print Final Results for Random Walk - print(f"Random Walk Final Results for {self.output_name}") - print(f"Duration: {t.format_dict['elapsed']}") - # print(f"Best Voice: {best_voice}") #TODO: add best voice model name - print(f"Best Score: {best_results['score']:.2f}_") - print(f"Best Similarity: {best_results['target_similarity']:.2f}_") + print(f"\n\nRandom Walk Final Results for {self.output_name}") + print(f"Duration: {(t.format_dict['elapsed'] / 60):.2f} minutes") + print(f"Best Voice: {best_voice_name}") + print(f"Best Score: {best_results['score']:.2f}") + print(f"Best Similarity: {best_results['target_similarity']:.2f}") print(f"Random Walk pt and wav files ---> {results_dir}") + # clear memory at completion + gc.collect() + torch.cuda.empty_cache() return - def score_voice(self,voice: torch.Tensor,min_similarity: float = 0.0) -> dict[str,Any]: - """Using a harmonic mean calculation to provide a score for the voice in similarity""" - audio = self.speech_generator.generate_audio(self.target_text, voice) - target_similarity = self.fitness_scorer.target_similarity(audio) - results: dict[str,Any] = { + # TODO: Move function to fitness_scorer + def score_voice(self, best_results: dict[str, Any], voice_tensor: torch.Tensor, min_similarity: float = 0.0) -> \ + tuple[dict[str, Any], str]: + start_time = datetime.datetime.now() + + if self.log_view is True: self.kvw_informer.log_gpu_memory("Generating iterated voice audio", self.log_view) + # Time audio generation + audio_start = datetime.datetime.now() + audio = self.speech_generator.generate_audio(self.target_text, voice_tensor) + audio = Tensor.cpu(audio).numpy() + audio_time = datetime.datetime.now() - audio_start + results: dict[str, Any] = { 'audio': audio } - # Bail early and save the compute if the similarity sucks - if target_similarity > min_similarity: - audio2 = self.speech_generator.generate_audio(self.other_text, voice) - results.update(self.fitness_scorer.hybrid_similarity(audio,audio2,target_similarity)) + if self.log_view is True: self.kvw_informer.log_gpu_memory("Evaluating Target Sim", self.log_view) + # Time target similarity calculation + target_sim_start = datetime.datetime.now() + # Pass embedding for reuse in self_sim + target_sim, audio_float_tensor, audio_embed1 = self.fitness_scorer.target_similarity(audio) + target_sim_time = datetime.datetime.now() - target_sim_start + + if self.log_view is True: self.kvw_informer.log_gpu_memory("Checking target vs min sim", self.log_view) + if target_sim > min_similarity: + # Time hybrid sim scoring + if self.log_view is True: self.kvw_informer.log_gpu_memory("Evaluating Hybrid Sim", self.log_view) + results, feature_sim_time, audio2_time, self_sim_time = ( + self.fitness_scorer.hybrid_similarity(best_results, audio_float_tensor, audio_embed1, + self.other_text, voice_tensor, target_sim, results)) + total_time = datetime.datetime.now() - start_time + if audio2_time != 0.0 and self_sim_time != 0.0: + if self.log_view is True: self.kvw_informer.log_gpu_memory( + "Returning success results (target sim > min sim)", self.log_view) + if self.process_times is True: tps_report = str( + f"Process Times: Audio1 gen: {audio_time.total_seconds():3f}s, Audio2 gen: {audio2_time.total_seconds():3f}s, Target Sim: {target_sim_time.total_seconds():3f}s, Self Sim: {self_sim_time.total_seconds():3f}s, Feat Sim: {feature_sim_time.total_seconds():3f}s, Total: {total_time.total_seconds():3f}s") + return results, tps_report + else: + if self.log_view is True: self.kvw_informer.log_gpu_memory("target sim < min sim)", self.log_view) else: - results["score"] = 0.0 - results["target_similarity"] = target_similarity + if self.log_view is True: self.kvw_informer.log_gpu_memory("target sim < min sim)", self.log_view) + results["score"] = 0.0 + results["target_similarity"] = target_sim + tps_report = '' + return results, tps_report + + def clear_losers_from_memory(self): + # Get reference to the voices cache + voices_cache = self.speech_generator.pipeline.voices + + # Create list of voices to keep (winners) + voices_to_keep = {self.starting_voice} # Always keep starting voice + + # Add current best voice to exempt list + if hasattr(self, 'best_voice') and self.best_voice: + voices_to_keep.add(self.best_voice) + + # Clean up all other voices + voices_to_delete = [] + for voice_name in voices_cache: + if voice_name not in voices_to_keep: + voices_to_delete.append(voice_name) - return results + # Delete the non-winners + for voice_name in voices_to_delete: + del voices_cache[voice_name] diff --git a/utilities/kvw_config.json b/utilities/kvw_config.json new file mode 100644 index 0000000..eb7ba3b --- /dev/null +++ b/utilities/kvw_config.json @@ -0,0 +1,14 @@ +{ + "preprocessing_logs": false, + "voice_loading_logs": false, + "voice_gen_logs": false, + "speech_gen_logs": false, + "fitness_logs": false, + "scoring_results_logs": false, + "tps_reports": true, + "feature_times": false, + "use_cached": false, + "cap_memory": true, + "cap_memory_frac": 0.2, + "memcache_clear_iteration_freq": 5000 +} \ No newline at end of file diff --git a/utilities/kvw_informer.py b/utilities/kvw_informer.py new file mode 100644 index 0000000..e5c60a2 --- /dev/null +++ b/utilities/kvw_informer.py @@ -0,0 +1,69 @@ +import gc +import json +from collections import defaultdict + +import torch + + +# TODO include settings for results recording, savepoints creation, manage backups, debugging files + +class KVW_Informer(): + def __init__(self): + try: + with open('./utilities/kvw_config.json', 'r') as file: + self.settings = json.load(file) + except FileNotFoundError: + print(f"Error: The file kvw_config.json was not found.") + except json.JSONDecodeError: + print(f"Error: The file kvw_config.json is not a valid JSON file.") + + def log_gpu_memory(self, step_name: str, view=False, console=False) -> str | None: + if view is True or console is True: + if torch.cuda.is_available(): + allocated = torch.cuda.memory_allocated() / 1e9 + reserved = torch.cuda.memory_reserved() / 1e9 + info = f"{step_name}: {allocated:.4f}GB allocated, {reserved:.4f}GB reserved" + else: + info = f"{step_name}: No CUDA available" + if view is True: + print(f"{info}") + elif console is True: + return info + + def gpu_memory_analysis(self, view=False): + if view is True: + # Detailed GPU memory analysis + print(torch.cuda.memory_summary()) + # List all tensors currently on GPU + gpu_tensors = [] + for obj in gc.get_objects(): + if isinstance(obj, torch.Tensor) and obj.is_cuda: + gpu_tensors.append((type(obj), obj.shape, obj.element_size() * obj.numel())) + + # Sort by size + gpu_tensors.sort(key=lambda x: x[2], reverse=True) + for i, (tensor_type, shape, size_bytes) in enumerate(gpu_tensors[:10]): # Top 10 + print(f"{i + 1}. Shape: {shape}, Size: {size_bytes / 1e6:.1f}MB") + + def track_gpu_objects(self): + """Safely track GPU objects without library loading issues""" + gpu_objects = defaultdict(int) + total_size = 0 + + # Create a snapshot of current objects to avoid weak reference issues + current_objects = list(gc.get_objects()) + + for obj in current_objects: + try: + # First check if it's actually a tensor + if isinstance(obj, torch.Tensor): + if obj.is_cuda: + obj_type = type(obj).__name__ + size = obj.element_size() * obj.numel() + gpu_objects[f"\n{obj_type}_{obj.shape}"] += 1 + total_size += size + except (ReferenceError, RuntimeError, OSError, AttributeError): + # Skip any problematic objects + continue + + return dict(gpu_objects), total_size diff --git a/utilities/pytorch_sanitizer.py b/utilities/pytorch_sanitizer.py index 5c3aaab..f17242d 100644 --- a/utilities/pytorch_sanitizer.py +++ b/utilities/pytorch_sanitizer.py @@ -24,7 +24,7 @@ def safe_load_pt_file(self, file_path: str) -> Union[torch.Tensor, Dict[str, Any # Try safe loading first try: data = torch.load(file_path, weights_only=True) - print(f"✅ Safely Loaded {file_path} with weights_only=True") + # print(f"✅ Safely Loaded {file_path} with weights_only=True") return data except Exception as safe_error: print(f"⚠️ Safe loading failed: {safe_error}") @@ -38,7 +38,7 @@ def safe_load_pt_file(self, file_path: str) -> Union[torch.Tensor, Dict[str, Any np.core.multiarray.scalar, ]): data = torch.load(file_path) - print(f"✅ Safely Loaded {file_path} with numpy globals allowed") + # print(f"✅ Safely Loaded {file_path} with numpy globals allowed") return data except Exception as numpy_error: print(f"⚠️ Numpy-safe loading failed: {numpy_error}") @@ -127,7 +127,7 @@ def convert_loaded_data_to_tensor(self, data: Union[torch.Tensor, Dict, np.ndarr def load_voice_safely(self, file_path: str) -> Optional[torch.Tensor]: """Complete safe loading pipeline for voice files""" - print(f"Loading voice file: {file_path}") + # print(f"Loading voice file: {file_path}") try: # Step 1: Safe load the file @@ -147,7 +147,7 @@ def load_voice_safely(self, file_path: str) -> Optional[torch.Tensor]: print(f"⏭️ Skipped: {e}") return None - def get_risk_report(self) -> str: + def get_risk_report(self) -> str | None: """Generate a report of risky files""" report = [] @@ -157,8 +157,8 @@ def get_risk_report(self) -> str: for file in self.risky_files: report.append(f" - {file}") - return "\n".join(report) - + return "\n".join(report) + return # Usage functions for backward compatibility def load_voice_safely(file_path: str, @@ -170,8 +170,7 @@ def load_voice_safely(file_path: str, # Print report if there were any issues report = loader.get_risk_report() - if "All files loaded safely!" not in report: - print(f"\n{report}") + if report: print(f"\n{report}") return result diff --git a/utilities/speech_generator.py b/utilities/speech_generator.py index b8049a5..e9bfdfb 100644 --- a/utilities/speech_generator.py +++ b/utilities/speech_generator.py @@ -1,34 +1,91 @@ +import re import warnings -import numpy as np import torch from kokoro import KPipeline +from utilities.kvw_informer import KVW_Informer + +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=FutureWarning) +warnings.filterwarnings("ignore", category=UserWarning) + class SpeechGenerator: - def __init__(self): - surpressWarnings() - self.pipeline = KPipeline(lang_code="a",repo_id='hexgrad/Kokoro-82M') - - def generate_audio(self, text: str, voice: torch.Tensor,speed: float = 1.0) -> np.typing.NDArray[np.float32]: - generator = self.pipeline(text, voice, speed) - audio = [] - for gs, ps, chunk in generator: - audio.append(chunk) - return np.concatenate(audio) - -def surpressWarnings(): - # Surpress all these warnings showing up from libraries cluttering the console - warnings.filterwarnings( - "ignore", - message=".*RNN module weights are not part of single contiguous chunk of memory.*", - category=UserWarning, - ) - warnings.filterwarnings( - "ignore", message=".*is deprecated in favor of*", category=FutureWarning - ) - warnings.filterwarnings( - "ignore", - message=".*dropout option adds dropout after all but last recurrent layer*", - category=UserWarning, - ) + def __init__(self, kvw_informer: KVW_Informer, target_text: str, other_text: str): + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.kvw_informer = kvw_informer + self.log_view = kvw_informer.settings['speech_gen_logs'] + self.process_times = self.kvw_informer.settings['tps_reports'] + + if self.log_view is True: + kvw_informer.log_gpu_memory(f"DEBUG: Initializing pipeline with device: {device}", self.log_view) + + self.pipeline = KPipeline(lang_code="a", repo_id='hexgrad/Kokoro-82M', device=device) + self.device = device + + # Verify model is actually on GPU + if self.log_view is True and self.pipeline.model: + print(f"DEBUG: Pipeline model device: {self.pipeline.model.device}") + print(f"DEBUG: Model parameters device: {next(self.pipeline.model.parameters()).device}") + + # Preprocess BOTH texts at initialization + self.target_text = target_text + self.other_text = other_text + self.target_segments = self._preprocess_text(target_text) + self.other_segments = self._preprocess_text(other_text) + + if self.log_view: + print(f"DEBUG: Preprocessed target text: {len(self.target_segments)} segments") + print(f"DEBUG: Preprocessed other text: {len(self.other_segments)} segments") + + def _preprocess_text(self, text: str): + """Private helper to preprocess text once""" + if self.log_view: + print(f"DEBUG: Preprocessing text: {text[:50]}...") + + # Same logic as pipeline.__call__ but done once + split_pattern = r'\n+' + text_segments = re.split(split_pattern, text.strip()) if split_pattern else [text] + + segments = [] + for graphemes_index, graphemes in enumerate(text_segments): + if not graphemes.strip(): + continue + + # Do expensive g2p and tokenization once + if self.pipeline.lang_code in 'ab': # English processing + _, tokens = self.pipeline.g2p(graphemes) + for gs, ps, tks in self.pipeline.en_tokenize(tokens): + if not ps: + continue + elif len(ps) > 510: + ps = ps[:510] + segments.append((gs, ps, tks, graphemes_index)) + + return segments + + def generate_audio(self, text: str, voice: torch.Tensor, speed: float = 1.0) -> torch.Tensor: + """Returns GPU tensor, optimized for both target and other text""" + + # Check which preprocessed segments to use + if text == self.target_text: + segments = self.target_segments + elif text == self.other_text: + segments = self.other_segments + else: + # Fallback for unexpected different text (shouldn't happen in practice) + segments = self._preprocess_text(text) + + # use preprocessed segments with direct KPipeline.infer() + audio_chunks = [] + for gs, ps, tks, text_index in segments: + output = KPipeline.infer(self.pipeline.model, ps, voice, speed) + if output is not None and output.audio is not None: + audio_chunks.append(output.audio.to(self.device)) + + # Concatenate and return GPU tensor + if audio_chunks: + return torch.cat(audio_chunks, dim=0) + else: + return torch.tensor([], device=self.device) diff --git a/utilities/voice_generator.py b/utilities/voice_generator.py index 75cb338..5bf25ca 100644 --- a/utilities/voice_generator.py +++ b/utilities/voice_generator.py @@ -1,10 +1,15 @@ import torch +from utilities.kvw_informer import KVW_Informer +from utilities.pytorch_sanitizer import load_voice_safely + class VoiceGenerator: - def __init__(self, voices: list[torch.Tensor], starting_voice: str | None): + def __init__(self, kvw_informer: KVW_Informer, voices: list[torch.Tensor], starting_voice: str | None): self.voices = voices - + self.kvw_informer = kvw_informer + self.log_view = self.kvw_informer.settings['voice_gen_logs'] + self.process_times = self.kvw_informer.settings['tps_reports'] self.stacked = torch.stack(voices,dim=0) self.mean = self.stacked.mean(dim=0) self.std = self.stacked.std(dim=0) @@ -12,39 +17,40 @@ def __init__(self, voices: list[torch.Tensor], starting_voice: str | None): self.max = self.stacked.max(dim=0)[0] if starting_voice: - self.starting_voice = torch.load(starting_voice) + self.starting_voice = load_voice_safely(starting_voice) else: self.starting_voice = self.mean - def generate_voice(self,base_tensor: torch.Tensor | None,diversity: float = 1.0, device: str = "cpu", clip: bool = False): + def generate_voice(self, base_tensor: torch.Tensor | None, diversity: float = 1.0, device: str = "cuda", + clip: bool = False): """Generate a new voice tensor based on the base_tensor and diversity. Args: base_tensor (torch.Tensor | None): The base tensor to generate the new voice from. diversity (float, optional): The diversity of the new voice. Defaults to 1.0. - device (str, optional): The device to generate the new voice on. Defaults to "cpu". + device (str, optional): The device to generate the new voice on. Defaults to "cuda". clip (bool, optional): Whether to clip the new voice to the min and max values. Defaults to False. Returns: torch.Tensor: The new voice tensor. """ + + device = "cuda" if torch.cuda.is_available() else "cpu" if base_tensor is None: base_tensor = self.mean.to(device) else: base_tensor = base_tensor.clone().to(device) - # Generate random noise with same shape noise = torch.randn_like(base_tensor, device=device) - - # Scale noise by standard deviation and the noise_scale factor - scaled_noise = noise * self.std.to(device) * diversity - - # Add scaled noise to base tensor + std_tensor = self.std.to(device) + scaled_noise = noise * std_tensor * diversity new_tensor = base_tensor + scaled_noise if clip: - new_tensor = torch.clamp(new_tensor, self.min, self.max) + min_tensor = self.min.to(device) + max_tensor = self.max.to(device) + new_tensor = torch.clamp(new_tensor, min_tensor, max_tensor) - return new_tensor + return new_tensor.float() - #TODO: Make more voice genration functions + # TODO: Make more voice generation functions diff --git a/uv.lock b/uv.lock index 07cdf44..3b45040 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,8 @@ 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