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import gradio as gr
from transformers import AutoProcessor, BarkModel
import scipy.io.wavfile
import torch
import os
from typing import Optional
import numpy as np
# Check for CUDA availability and set device
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize model and processor globally with optimizations
processor = AutoProcessor.from_pretrained("suno/bark")
model = BarkModel.from_pretrained("suno/bark", torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32)
model.to(DEVICE)
# Enable model optimizations
if DEVICE == "cuda":
torch.backends.cudnn.benchmark = True
model.eval() # Set to evaluation mode
# Cache for storing generated audio files
CACHE_DIR = "audio_cache"
os.makedirs(CACHE_DIR, exist_ok=True)
def get_cache_path(text: str, voice_preset: str) -> str:
"""Generate a unique cache path for the given text and voice preset."""
import hashlib
hash_key = hashlib.md5(f"{text}_{voice_preset}".encode()).hexdigest()
return os.path.join(CACHE_DIR, f"{hash_key}.wav")
@torch.inference_mode() # More efficient than no_grad for inference
def text_to_speech(text: str, voice_preset: str = "v2/hi_speaker_2") -> Optional[str]:
try:
# Check cache first
cache_path = get_cache_path(text, voice_preset)
if os.path.exists(cache_path):
return cache_path
# Generate audio from text
inputs = processor(text, voice_preset=voice_preset)
# Move inputs to device
inputs = {k: v.to(DEVICE) if isinstance(v, torch.Tensor) else v
for k, v in inputs.items()}
# Generate audio with optimized settings
with torch.cuda.amp.autocast() if DEVICE == "cuda" else torch.no_grad():
audio_array = model.generate(**inputs,
do_sample=True,
guidance_scale=2.5,
temperature=0.7)
# Move to CPU and convert to numpy
audio_array = audio_array.cpu().numpy().squeeze()
# Normalize audio
audio_array = np.clip(audio_array, -1, 1)
# Get sample rate from model config
sample_rate = model.generation_config.sample_rate
# Save audio file to cache
scipy.io.wavfile.write(cache_path, rate=sample_rate, data=audio_array)
return cache_path
except Exception as e:
print(f"Error generating audio: {str(e)}")
return None
# Define available voice presets
voice_presets = [
"v2/hi_speaker_1",
"v2/hi_speaker_2",
"v2/hi_speaker_3",
"v2/hi_speaker_4",
"v2/hi_speaker_5"
]
# Create Gradio interface with optimized settings
demo = gr.Interface(
fn=text_to_speech,
inputs=[
gr.Textbox(label="Enter text (Hindi or English)"),
gr.Dropdown(choices=voice_presets, value="v2/hi_speaker_2", label="Select Voice")
],
outputs=gr.Audio(label="Generated Speech"),
title="Bark Text-to-Speech",
description="Convert text to speech using the Bark model. Supports Hindi and English text.",
cache_examples=True,
)
# Launch the app with optimized settings
if __name__ == "__main__":
demo.launch()
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