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98ebf7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import torch
import soundfile as sf
import gradio as gr
from transformers import AutoModel, AutoProcessor
# ==========================================
# Audio8 Model
# ==========================================
MODEL_ID = "Audio8/Audio8-TTS-Preview-0.6b"
# Select GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
# Use BF16 on GPU, FP32 on CPU
dtype = torch.bfloat16 if device == "cuda" else torch.float32
print("=" * 60)
print("Audio8 Text-to-Speech")
print("=" * 60)
print("PyTorch:", torch.__version__)
print("CUDA Available:", torch.cuda.is_available())
if torch.cuda.is_available():
print("GPU:", torch.cuda.get_device_name(0))
# ==========================================
# Load Processor
# ==========================================
print("\nLoading processor...")
processor = AutoProcessor.from_pretrained(
MODEL_ID,
trust_remote_code=True
)
# ==========================================
# Load Model
# ==========================================
print("Loading Audio8 model...")
model = AutoModel.from_pretrained(
MODEL_ID,
trust_remote_code=True,
dtype=dtype
).eval().to(device)
print("✅ Audio8 model loaded successfully!")
# ==========================================
# Generate Speech
# ==========================================
def generate_speech(text):
# Check empty input
if not text or not text.strip():
return None
print("\n" + "=" * 50)
print("Generating speech...")
print("Text:", text)
# Process text
inputs = processor(
text=[text],
return_tensors="pt"
)
# Move inputs to GPU/CPU
inputs = {
name: value.to(device)
for name, value in inputs.items()
}
# Generate speech tokens
with torch.inference_mode():
output = model.generate(
**inputs,
max_new_tokens=1024,
temperature=0.8,
top_p=0.95,
top_k=50,
do_sample=True,
return_dict_in_generate=True,
)
# Decode generated audio
waveforms, waveform_lengths = model.decode_audio(
output.codes
)
# Convert to NumPy
audio = waveforms[
0,
:int(waveform_lengths[0])
].float().cpu().numpy()
# ==========================================
# Normalize Audio Volume
# ==========================================
peak = abs(audio).max()
if peak > 0:
audio = audio / peak
audio = audio * 0.95
# ==========================================
# Save Audio
# ==========================================
output_file = "output.wav"
sf.write(
output_file,
audio,
model.config.codec_sample_rate
)
print("✅ Speech generated!")
print("🎵 Saved:", output_file)
return output_file
# ==========================================
# Gradio User Interface
# ==========================================
with gr.Blocks(title="SmartVoiceAI") as app:
gr.Markdown(
"""
# 🔊 SmartVoiceAI
### AI Text-to-Speech
Enter your text below and generate natural-sounding speech
using the Audio8 TTS model.
"""
)
# Text input
text_input = gr.Textbox(
label="Enter Text",
placeholder="Type something here...",
lines=6
)
# Generate button
generate_button = gr.Button(
"🔊 Generate Speech",
variant="primary"
)
# Audio output
audio_output = gr.Audio(
label="Generated Voice",
type="filepath",
autoplay=True
)
# Button action
generate_button.click(
fn=generate_speech,
inputs=text_input,
outputs=audio_output
)
gr.Markdown(
"""
---
**Model:** Audio8-TTS-Preview-0.6b
**Device:** CUDA / CPU
"""
)
# ==========================================
# Start Application
# ==========================================
if __name__ == "__main__":
app.launch() |