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import torch
import asyncio
import logging
import base64
import io
import numpy as np
from typing import Optional
from backend.config import settings
try:
from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor
VIBEVOICE_AVAILABLE = True
except ImportError:
VIBEVOICE_AVAILABLE = False
logger = logging.getLogger(__name__)
class VoiceSynthesizer:
def __init__(self):
self.voice_model = None
self.voice_processor = None
self.character_voice_configs = {}
async def initialize(self):
"""Initialize voice synthesis model"""
if not settings.ENABLE_VOICE:
logger.info("Voice synthesis disabled")
return False
if not VIBEVOICE_AVAILABLE:
logger.error("VibeVoice community package not available. Install with: pip install git+https://github.com/vibevoice-community/VibeVoice.git")
return False
logger.info("Loading VibeVoice model...")
try:
# Load VibeVoice model from HuggingFace
model_path = "vibevoice/VibeVoice-1.5B"
# Load processor
logger.info(f"Loading processor from {model_path}")
self.voice_processor = VibeVoiceProcessor.from_pretrained(model_path)
# Determine device and dtype
device = "cuda" if torch.cuda.is_available() else "cpu"
load_dtype = torch.bfloat16 if device == "cuda" else torch.float32
attn_impl = "flash_attention_2" if device == "cuda" else "sdpa"
logger.info(f"Loading model with device: {device}, dtype: {load_dtype}, attention: {attn_impl}")
# Load model
if device == "cuda":
self.voice_model = VibeVoiceForConditionalGenerationInference.from_pretrained(
model_path,
torch_dtype=load_dtype,
device_map="cuda",
attn_implementation=attn_impl,
)
else:
self.voice_model = VibeVoiceForConditionalGenerationInference.from_pretrained(
model_path,
torch_dtype=load_dtype,
device_map="cpu",
attn_implementation=attn_impl,
)
# Set inference steps
self.voice_model.eval()
self.voice_model.set_ddpm_inference_steps(num_steps=10)
# Configure character-specific voice parameters
self._setup_character_voices()
logger.info("VibeVoice synthesizer initialized successfully")
return True
except Exception as e:
logger.error(f"Failed to initialize VibeVoice model: {e}")
logger.info("Voice synthesis will be disabled")
return False
def _setup_character_voices(self):
"""Setup character-specific voice configurations"""
self.character_voice_configs = {
"moses": {
"style": "authoritative",
"speed": 0.9, # Slightly slower, more measured
"pitch": 0.8, # Deeper voice
"emotion": "wise"
},
"samsung_employee": {
"style": "professional",
"speed": 1.0, # Normal speed
"pitch": 1.0, # Normal pitch
"emotion": "friendly"
},
"jinx": {
"style": "energetic",
"speed": 1.2, # Faster, more manic
"pitch": 1.3, # Higher pitch
"emotion": "playful"
}
}
async def synthesize(self, text: str, character_id: str) -> Optional[str]:
"""Synthesize speech for given text and character"""
if not settings.ENABLE_VOICE or not self.voice_model or not self.voice_tokenizer:
return None
try:
# Get character voice config
voice_config = self.character_voice_configs.get(
character_id,
self.character_voice_configs["samsung_employee"] # Default
)
# Prepare text for TTS
processed_text = self._preprocess_text(text, character_id)
# Process text with VibeVoice tokenizer
inputs = self.voice_tokenizer(
processed_text,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True
)
if settings.DEVICE == "cuda" and torch.cuda.is_available():
inputs = {k: v.cuda() for k, v in inputs.items()}
# Generate audio using VibeVoice
with torch.no_grad():
outputs = self.voice_model.generate(
**inputs,
max_length=1024,
num_beams=4,
do_sample=True,
temperature=0.8
)
# Convert outputs to audio waveform
audio_features = outputs
# Convert model outputs to audio waveform
audio_np = self._features_to_audio(audio_features, voice_config)
# Apply character-specific modifications
audio_np = self._apply_character_effects(audio_np, voice_config)
# Convert to base64 for web transmission
audio_base64 = self._audio_to_base64(audio_np)
return audio_base64
except Exception as e:
logger.error(f"Error in voice synthesis: {e}")
return None
def _preprocess_text(self, text: str, character_id: str) -> str:
"""Preprocess text for character-specific speech patterns"""
# Character-specific text modifications
if character_id == "moses":
# Add pauses for emphasis, make more formal
text = text.replace("!", ".") # Less exclamatory
text = text.replace("...", "... ") # Add pauses
elif character_id == "jinx":
# Make more energetic and expressive
text = text.replace(".", "!") # More excitement
text = text.replace(",", "... ") # Add dramatic pauses
# Clean up text
text = text.strip()
# Add character voice prompt for better synthesis
voice_prompts = {
"moses": f"[Speaking with wisdom and authority] {text}",
"samsung_employee": f"[Speaking professionally and clearly] {text}",
"jinx": f"[Speaking energetically and playfully] {text}"
}
return voice_prompts.get(character_id, text)
def _get_speaker_embedding(self, character_id: str) -> Optional[torch.Tensor]:
"""Get speaker embedding for character (simplified approach)"""
# Create different speaker embeddings for different characters
# This is a simplified approach - in practice, you'd train specific embeddings
embeddings = {
"moses": torch.randn(1, 512) * 0.1, # Deeper, more authoritative
"samsung_employee": torch.randn(1, 512) * 0.05, # Neutral, professional
"jinx": torch.randn(1, 512) * 0.15, # More varied, energetic
}
# Set seed for consistency
torch.manual_seed(hash(character_id) % 10000)
embedding = embeddings.get(character_id, embeddings["samsung_employee"])
return embedding
def _spectrogram_to_audio(self, spectrogram: torch.Tensor, voice_config: dict) -> np.ndarray:
"""Convert spectrogram to audio waveform (fallback method)"""
# This is a simplified conversion for when vocoder is not available
if spectrogram.is_cuda:
spectrogram = spectrogram.cpu()
spec_np = spectrogram.squeeze().numpy()
# Simple inverse spectrogram (placeholder implementation)
# In practice, this would use proper audio processing
duration = spec_np.shape[1] * 0.05 # Estimate duration
samples = int(duration * settings.SAMPLE_RATE)
# Generate audio based on spectral features
audio = np.zeros(samples)
for i in range(min(spec_np.shape[0], samples)):
if i < len(audio):
audio[i] = np.mean(spec_np[:, i % spec_np.shape[1]]) * 0.3
return audio.astype(np.float32)
def _apply_character_effects(self, audio: np.ndarray, voice_config: dict) -> np.ndarray:
"""Apply character-specific audio effects"""
# Apply speed changes
speed = voice_config.get("speed", 1.0)
if speed != 1.0:
audio = self._change_speed(audio, speed)
# Apply pitch changes (simplified)
pitch = voice_config.get("pitch", 1.0)
if pitch != 1.0:
audio = self._change_pitch(audio, pitch)
return audio
def _change_pitch(self, audio: np.ndarray, pitch_factor: float) -> np.ndarray:
"""Change pitch of audio (simplified implementation)"""
if pitch_factor == 1.0:
return audio
# Simple pitch shifting by resampling (not perfect but functional)
new_length = int(len(audio) / pitch_factor)
indices = np.linspace(0, len(audio) - 1, new_length)
return np.interp(indices, np.arange(len(audio)), audio)
def _change_speed(self, audio: np.ndarray, speed: float) -> np.ndarray:
"""Change audio playback speed"""
if speed == 1.0:
return audio
# Simple time stretching (placeholder)
new_length = int(len(audio) / speed)
indices = np.linspace(0, len(audio) - 1, new_length)
return np.interp(indices, np.arange(len(audio)), audio)
def _audio_to_base64(self, audio_data: np.ndarray) -> str:
"""Convert audio numpy array to base64 string"""
# Convert to 16-bit PCM
audio_int16 = (audio_data * 32767).astype(np.int16)
# Create WAV file in memory
buffer = io.BytesIO()
# Write WAV header and data
torchaudio.save(
buffer,
torch.from_numpy(audio_int16).unsqueeze(0).float() / 32767.0,
settings.SAMPLE_RATE,
format="wav"
)
# Get bytes and encode to base64
buffer.seek(0)
audio_bytes = buffer.read()
audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
return f"data:audio/wav;base64,{audio_base64}"
def get_character_voice_info(self, character_id: str) -> dict:
"""Get voice configuration for character"""
return self.character_voice_configs.get(character_id, {}) |