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import gradio as gr
import torch
import numpy as np
from soprano import SopranoTTS
from scipy.io.wavfile import write as wav_write
import tempfile
import os
import spaces
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(DEVICE)
# Load model once
model = SopranoTTS(
backend="auto",
device=DEVICE,
cache_size_mb=100,
decoder_batch_size=1,
)
SAMPLE_RATE = 32000
@spaces.GPU
def tts_stream(text, temperature, top_p, repetition_penalty, state):
if not text.strip():
yield None, state
return
chunks = []
stream = model.infer_stream(
text,
chunk_size=1,
temperature=temperature,
top_p=top_p,
repetition_penalty=repetition_penalty,
)
for chunk in stream:
if isinstance(chunk, torch.Tensor):
audio_np = chunk.detach().cpu().numpy().astype(np.float32)
chunks.append(audio_np)
# stream partial audio
yield (SAMPLE_RATE, audio_np), np.concatenate(chunks)
if chunks:
final_audio = np.concatenate(chunks)
yield (SAMPLE_RATE, final_audio), final_audio
def save_audio(state):
if state is None or len(state) == 0:
return None
fd, path = tempfile.mkstemp(suffix=".wav")
os.close(fd)
wav_write(path, SAMPLE_RATE, state)
return path
with gr.Blocks() as demo:
state_audio = gr.State(None)
with gr.Row():
with gr.Column():
gr.Markdown("## Soprano Demo")
text_in = gr.Textbox(
label="Input Text",
placeholder="Enter text to synthesize...",
lines=4,
)
with gr.Accordion("Advanced options", open=False):
temperature = gr.Slider(
0.0, 1.0, value=0.3, step=0.05, label="Temperature"
)
top_p = gr.Slider(
0.0, 1.0, value=0.95, step=0.01, label="Top-p"
)
repetition_penalty = gr.Slider(
0.5, 2.0, value=1.2, step=0.05, label="Repetition penalty"
)
gen_btn = gr.Button("Generate")
with gr.Column():
audio_out = gr.Audio(
label="Output Audio",
autoplay=True,
streaming=True,
)
download_btn = gr.Button("Download")
file_out = gr.File(label="Download file")
gr.Markdown(
"Usage tips: (placeholder)\n\n"
"- Tip 1\n"
"- Tip 2\n"
"- Tip 3"
)
gen_btn.click(
fn=tts_stream,
inputs=[text_in, temperature, top_p, repetition_penalty, state_audio],
outputs=[audio_out, state_audio],
)
download_btn.click(
fn=save_audio,
inputs=[state_audio],
outputs=[file_out],
)
demo.queue()
demo.launch() |