Spaces:
Running
Running
File size: 2,462 Bytes
a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b a132885 11bfd4b |
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 |
import gradio as gr
import torch
import numpy as np
from kokoro import KModel, KPipeline
# Check if CUDA is available
CUDA_AVAILABLE = torch.cuda.is_available()
# Initialize the model
model = KModel().to('cuda' if CUDA_AVAILABLE else 'cpu').eval()
# Initialize pipelines for different language codes (using 'a' for English)
pipelines = {'a': KPipeline(lang_code='a', model=False)}
# Custom pronunciation for "kokoro"
pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO'
def text_to_audio(text, speed=1.0):
"""Convert text to audio using Kokoro model"""
if not text:
return None
pipeline = pipelines['a'] # Use English pipeline
voice = "af_heart" # Default voice (US English, female, Heart)
# Process the text
pack = pipeline.load_voice(voice)
for _, ps, _ in pipeline(text, voice, speed):
ref_s = pack[len(ps)-1]
# Generate audio
try:
audio = model(ps, ref_s, speed)
except Exception as e:
raise gr.Error(f"Error generating audio: {str(e)}")
# Return the audio with 24kHz sample rate
return 24000, audio.numpy()
return None
# Create Gradio interface
with gr.Blocks(title="Kokoro Text-to-Audio") as app:
gr.Markdown("# 🎵 Kokoro Text-to-Audio Converter")
gr.Markdown("Convert text to speech using the Kokoro-82M model")
with gr.Row():
with gr.Column():
text_input = gr.Textbox(
label="Enter your text",
placeholder="Type something to convert to audio...",
lines=5
)
speed_slider = gr.Slider(
minimum=0.5,
maximum=2.0,
value=1.0,
step=0.1,
label="Speech Speed"
)
submit_btn = gr.Button("Generate Audio")
with gr.Column():
audio_output = gr.Audio(label="Generated Audio", type="numpy")
submit_btn.click(
fn=text_to_audio,
inputs=[text_input, speed_slider],
outputs=[audio_output]
)
gr.Markdown("### Usage Tips")
gr.Markdown("- For best results, keep your text reasonably short (up to ~500 characters)")
gr.Markdown("- Adjust the speed slider to modify the pace of speech")
gr.Markdown("- The model may take a moment to load on first use")
# Launch the app
if __name__ == "__main__":
app.launch() |