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"""Voice embeddings for consistent voice generation."""
import os
import torch
import torchaudio
import numpy as np
from typing import Dict
# Path to store voice samples
VOICE_SAMPLES_DIR = os.path.join(os.path.dirname(__file__), "voice_samples")
os.makedirs(VOICE_SAMPLES_DIR, exist_ok=True)
# Dictionary to store voice embeddings/samples
VOICE_DICT: Dict[str, torch.Tensor] = {}
def initialize_voices(sample_rate: int = 24000):
"""Initialize voice dictionary with consistent samples."""
# Generate consistent seed audio for each voice
for voice_id in range(6):
voice_name = ["alloy", "echo", "fable", "onyx", "nova", "shimmer"][voice_id]
# Create deterministic audio sample for each voice
np.random.seed(voice_id + 42) # Use a fixed seed based on voice ID
# Generate 1 second of "seed" audio with deterministic characteristics
# This differs per voice but remains consistent across runs
duration = 1.0 # seconds
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
# Create a distinctive waveform for each voice
if voice_id == 0: # alloy - rich mid tones
freq1, freq2 = 220, 440
audio = 0.5 * np.sin(2 * np.pi * freq1 * t) + 0.3 * np.sin(2 * np.pi * freq2 * t)
elif voice_id == 1: # echo - reverberant
freq = 330
audio = np.sin(2 * np.pi * freq * t) * np.exp(-t * 3)
elif voice_id == 2: # fable - bright, higher pitch
freq = 523
audio = 0.7 * np.sin(2 * np.pi * freq * t)
elif voice_id == 3: # onyx - deep and resonant
freq = 165
audio = 0.8 * np.sin(2 * np.pi * freq * t)
elif voice_id == 4: # nova - warm and smooth
freq1, freq2 = 392, 196
audio = 0.4 * np.sin(2 * np.pi * freq1 * t) + 0.4 * np.sin(2 * np.pi * freq2 * t)
else: # shimmer - airy and light
freq1, freq2, freq3 = 587, 880, 1174
audio = 0.3 * np.sin(2 * np.pi * freq1 * t) + 0.2 * np.sin(2 * np.pi * freq2 * t) + 0.1 * np.sin(2 * np.pi * freq3 * t)
# Normalize
audio = audio / np.max(np.abs(audio))
# Convert to tensor
audio_tensor = torch.tensor(audio, dtype=torch.float32)
# Store the audio tensor
VOICE_DICT[voice_name] = audio_tensor
# Save as wav for reference
save_path = os.path.join(VOICE_SAMPLES_DIR, f"{voice_name}_seed.wav")
torchaudio.save(save_path, audio_tensor.unsqueeze(0), sample_rate)
print(f"Initialized voice seed for {voice_name}")
def get_voice_sample(voice_name: str) -> torch.Tensor:
"""Get the voice sample for a given voice name."""
if not VOICE_DICT:
initialize_voices()
if voice_name in VOICE_DICT:
return VOICE_DICT[voice_name]
# Default to alloy if voice not found
print(f"Voice {voice_name} not found, defaulting to alloy")
return VOICE_DICT["alloy"]
def update_voice_sample(voice_name: str, audio: torch.Tensor):
"""Update the voice sample with recently generated audio."""
# Only update if we've already initialized
if VOICE_DICT:
# Take the last second of audio (or whatever is available)
sample_length = min(24000, audio.shape[0])
VOICE_DICT[voice_name] = audio[-sample_length:].detach().cpu()
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