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"""
Speaker Encoder Module
Extract speaker embeddings and compute similarity using Resemblyzer
"""
import numpy as np
import librosa
import torch
from pathlib import Path
from typing import Union, Tuple
import warnings
warnings.filterwarnings('ignore')
try:
from resemblyzer import VoiceEncoder, preprocess_wav
except ImportError:
print("Warning: resemblyzer not installed. Run: pip install resemblyzer")
VoiceEncoder = None
preprocess_wav = None
class SpeakerEncoder:
"""
Speaker embedding extraction and similarity computation
Features:
- Extract 256-dimensional speaker embeddings
- Compute speaker similarity (cosine similarity)
- Support for multiple audio formats
"""
def __init__(self, device: str = "cuda"):
"""
Initialize Speaker Encoder
Args:
device: Device to run on ('cuda' or 'cpu')
"""
if VoiceEncoder is None:
raise ImportError("resemblyzer not installed. Run: pip install resemblyzer")
self.device = device if torch.cuda.is_available() else "cpu"
print(f"🎯 Initializing Speaker Encoder on {self.device}...")
try:
self.encoder = VoiceEncoder(device=self.device)
print("✓ Speaker Encoder initialized successfully!")
except Exception as e:
print(f"❌ Error initializing Speaker Encoder: {e}")
raise
def extract_embedding(
self,
audio_path: Union[str, Path],
normalize: bool = True
) -> np.ndarray:
"""
Extract speaker embedding from audio
Args:
audio_path: Path to audio file
normalize: Normalize the embedding to unit length
Returns:
256-dimensional speaker embedding
"""
audio_path = Path(audio_path)
if not audio_path.exists():
raise FileNotFoundError(f"Audio file not found: {audio_path}")
try:
# Load and preprocess audio
wav = preprocess_wav(audio_path)
# Extract embedding
embedding = self.encoder.embed_utterance(wav)
# Normalize if requested
if normalize:
embedding = embedding / (np.linalg.norm(embedding) + 1e-8)
return embedding
except Exception as e:
print(f"❌ Error extracting embedding from {audio_path.name}: {e}")
raise
def extract_embeddings_batch(
self,
audio_paths: list,
normalize: bool = True
) -> np.ndarray:
"""
Extract embeddings from multiple audio files
Args:
audio_paths: List of audio file paths
normalize: Normalize embeddings
Returns:
Array of shape (n_files, 256)
"""
embeddings = []
print(f"📊 Extracting embeddings from {len(audio_paths)} files...")
for audio_path in audio_paths:
try:
emb = self.extract_embedding(audio_path, normalize=normalize)
embeddings.append(emb)
except Exception as e:
print(f"⚠️ Skipping {audio_path}: {e}")
embeddings.append(np.zeros(256)) # Placeholder
return np.array(embeddings)
def compute_similarity(
self,
audio_path1: Union[str, Path],
audio_path2: Union[str, Path]
) -> float:
"""
Compute speaker similarity between two audio files
Args:
audio_path1: First audio file
audio_path2: Second audio file
Returns:
Cosine similarity score (0-1, higher is more similar)
"""
# Extract embeddings
emb1 = self.extract_embedding(audio_path1, normalize=True)
emb2 = self.extract_embedding(audio_path2, normalize=True)
# Compute cosine similarity
similarity = np.dot(emb1, emb2)
return float(similarity)
def compute_similarity_matrix(
self,
audio_paths: list
) -> np.ndarray:
"""
Compute pairwise similarity matrix for multiple audio files
Args:
audio_paths: List of audio file paths
Returns:
Similarity matrix of shape (n_files, n_files)
"""
# Extract all embeddings
embeddings = self.extract_embeddings_batch(audio_paths, normalize=True)
# Compute similarity matrix
similarity_matrix = np.dot(embeddings, embeddings.T)
return similarity_matrix
def find_most_similar(
self,
query_audio: Union[str, Path],
candidate_audios: list,
top_k: int = 5
) -> list:
"""
Find most similar speakers to a query audio
Args:
query_audio: Query audio file
candidate_audios: List of candidate audio files
top_k: Number of top matches to return
Returns:
List of (audio_path, similarity_score) tuples
"""
# Extract query embedding
query_emb = self.extract_embedding(query_audio, normalize=True)
# Extract candidate embeddings
candidate_embs = self.extract_embeddings_batch(candidate_audios, normalize=True)
# Compute similarities
similarities = np.dot(candidate_embs, query_emb)
# Get top-k indices
top_indices = np.argsort(similarities)[::-1][:top_k]
# Return results
results = [
(candidate_audios[idx], float(similarities[idx]))
for idx in top_indices
]
return results
def verify_speaker(
self,
audio_path1: Union[str, Path],
audio_path2: Union[str, Path],
threshold: float = 0.75
) -> Tuple[bool, float]:
"""
Verify if two audio files are from the same speaker
Args:
audio_path1: First audio file
audio_path2: Second audio file
threshold: Similarity threshold for same speaker (default: 0.75)
Returns:
Tuple of (is_same_speaker, similarity_score)
"""
similarity = self.compute_similarity(audio_path1, audio_path2)
is_same = similarity >= threshold
return is_same, similarity
def interpolate_embeddings(
self,
audio_path1: Union[str, Path],
audio_path2: Union[str, Path],
alpha: float = 0.5
) -> np.ndarray:
"""
Interpolate between two speaker embeddings
Useful for creating synthetic speaker characteristics
Args:
audio_path1: First audio file
audio_path2: Second audio file
alpha: Interpolation factor (0=speaker1, 1=speaker2)
Returns:
Interpolated embedding
"""
emb1 = self.extract_embedding(audio_path1, normalize=True)
emb2 = self.extract_embedding(audio_path2, normalize=True)
# Linear interpolation
interpolated = (1 - alpha) * emb1 + alpha * emb2
# Normalize
interpolated = interpolated / (np.linalg.norm(interpolated) + 1e-8)
return interpolated
@staticmethod
def load_audio(
audio_path: Union[str, Path],
sr: int = 16000
) -> Tuple[np.ndarray, int]:
"""
Load audio file
Args:
audio_path: Path to audio file
sr: Target sample rate
Returns:
Tuple of (audio_array, sample_rate)
"""
audio, sample_rate = librosa.load(str(audio_path), sr=sr)
return audio, sample_rate
def __repr__(self):
return f"SpeakerEncoder(device={self.device})"
def main():
"""Demo usage of SpeakerEncoder"""
print("=" * 60)
print("Speaker Encoder Demo")
print("=" * 60)
# Initialize
encoder = SpeakerEncoder(device="cuda")
print("\n✓ Speaker Encoder ready!")
print(" Embedding dimension: 256")
print(" Use for:")
print(" - Extract speaker embeddings")
print(" - Compute speaker similarity")
print(" - Verify speaker identity")
print(" - Interpolate between speakers")
print("\n" + "=" * 60)
if __name__ == "__main__":
main()
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