Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,400 Bytes
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import os
import gradio as gr
import spaces
from tts_model import TTSModel
import numpy as np
# Set HF_HOME for faster restarts with cached models/voices
os.environ["HF_HOME"] = "/data/.huggingface"
# Create TTS model instance
model = TTSModel()
@spaces.GPU(duration=10) # Quick initialization
def initialize_model():
"""Initialize model and get voices"""
if model.model is None:
if not model.initialize():
raise gr.Error("Failed to initialize model")
return model.list_voices()
# Get initial voice list
voice_list = initialize_model()
@spaces.GPU(duration=120) # Allow 5 minutes for processing
def generate_speech_from_ui(text, voice_name, speed):
"""Handle text-to-speech generation from the Gradio UI"""
try:
audio_array, duration = model.generate_speech(text, voice_name, speed)
# Convert float array to int16 range (-32768 to 32767)
audio_array = np.array(audio_array, dtype=np.float32)
audio_array = (audio_array * 32767).astype(np.int16)
return (24000, audio_array), f"Audio Duration: {duration:.2f} seconds\nProcessing complete - check console for detailed metrics"
except Exception as e:
raise gr.Error(str(e))
# Create Gradio interface
with gr.Blocks(title="Kokoro TTS Demo") as demo:
gr.HTML(
"""
<div style="text-align: center; max-width: 800px; margin: 0 auto;">
<h1>Kokoro TTS Demo</h1>
<p>Convert text to natural-sounding speech using various voices.</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=3):
# Input components
text_input = gr.TextArea(
label="Text to speak",
placeholder="Enter text here...",
lines=3,
value=open("the_time_machine_hgwells.txt").read()[:1000]
)
voice_dropdown = gr.Dropdown(
label="Voice",
choices=voice_list,
value=voice_list[0] if voice_list else None,
allow_custom_value=True # Allow custom values to avoid warnings
)
speed_slider = gr.Slider(
label="Speed",
minimum=0.5,
maximum=2.0,
value=1.0,
step=0.1
)
submit_btn = gr.Button("Generate Speech")
with gr.Column(scale=2):
# Output components
audio_output = gr.Audio(
label="Generated Speech",
type="numpy",
format="wav",
autoplay=False
)
duration_text = gr.Textbox(
label="Processing Info",
interactive=False,
lines=4
)
# Set up event handler
submit_btn.click(
fn=generate_speech_from_ui,
inputs=[text_input, voice_dropdown, speed_slider],
outputs=[audio_output, duration_text]
)
# Add voice descriptions
gr.Markdown("""
### Available Voices
- Adult Female (af): Base female voice
- Bella (af_bella): Warm and friendly
- Nicole (af_nicole): Warm and Whispered
- Sarah (af_sarah): Soft and gentle
- Sky (af_sky): You know her, you love her
- Adult Male (am): Base male voice
- Adam (am_adam): Clear and Friendly
- Michael (am_michael): Smooth and natural
- Young Female (bf):
- Emma (bf_emma): Sweet and cheerful
- Isabella (bf_isabella): Lively and expressive
- Young Male (bm):
- George (bm_george): Young and energetic
- Lewis (bm_lewis): Deep and confident
""")
# Add text analysis info
with gr.Row():
with gr.Column():
gr.Markdown("""
### Demo Text Info
The demo text is loaded from H.G. Wells' "The Time Machine". This classic text demonstrates the system's ability to handle long-form content through chunking.
""")
text_stats = gr.Textbox(
label="Text Statistics",
interactive=False,
value=f"Characters: {len(open('the_time_machine_hgwells.txt').read())}\nEstimated chunks: {len(open('the_time_machine_hgwells.txt').read()) // 300 + 1}"
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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