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# DocVoice.py
import torch
from transformers import pipeline
import soundfile as sf
# -------------------
# 1️⃣ Detect GPU
# -------------------
use_cuda = torch.cuda.is_available()
device = 0 if use_cuda else -1
print(f"🌟 Using {'GPU' if use_cuda else 'CPU'}")
# -------------------
# 2️⃣ Load TTS model
# -------------------
tts_model_id = "microsoft/speecht5_tts" # Compatible TTS model
tts_pipe = pipeline(
"text-to-speech",
model=tts_model_id,
device=device
)
print("🔊 TTS pipeline ready using Hugging Face.")
# -------------------
# 3️⃣ TTS Helper Function
# -------------------
def text_to_speech(text: str, filename="assistant_response.wav"):
"""
Generate speech from text and save as WAV file.
"""
if not text.strip():
return None
print(f"📝 Generating audio for: {text}")
speech_array = tts_pipe(text)[0]["array"] # returns numpy array
sample_rate = tts_pipe.model.config.sampling_rate if hasattr(tts_pipe.model.config, "sampling_rate") else 16000
# Save audio
sf.write(filename, speech_array, sample_rate)
print(f"✅ Audio saved as {filename}")
return filename
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