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import os
from huggingface_hub import hf_hub_download
import gradio as gr
from piper import PiperVoice
from io import BytesIO
import wave
import numpy as np
def text_to_speech(text):
# Load voice data
model_path = hf_hub_download(repo_id="sekhan/luxembourgish-voice",
repo_type='dataset',
filename="high/lu_rtl_high3239.onnx",
token=os.environ['HF_TOKEN'])
config_path = hf_hub_download(repo_id="sekhan/luxembourgish-voice",
repo_type='dataset',
filename="high/lu_rtl_high3239.onnx.json",
token=os.environ['HF_TOKEN'])
# Load Lux. voice
voice = PiperVoice.load(model_path, config_path)
buffer = BytesIO()
with wave.open(buffer, 'wb') as wav_file:
wav_file.setframerate(voice.config.sample_rate)
wav_file.setsampwidth(2)
wav_file.setnchannels(1)
voice.synthesize(text, wav_file, sentence_silence=0.5, length_scale=1.1, noise_scale=0.75)
buffer.seek(0)
audio_data = np.frombuffer(buffer.read(), dtype=np.int16)
return audio_data.tobytes(), None
# Gradio Interface
with gr.Blocks(theme=gr.themes.Base(), css="footer {visibility: hidden}") as blocks:
gr.Markdown("# Luxembourgish Text-to-Speech Synthesizer")
gr.Markdown("Enter Luxembourgish text to synthesize it into speech. This is a very early demo. Your spontaneous text data are not saved and only used for the speech synthesis.")
input_text = gr.Textbox(label="Input Text", max_lines=3, placeholder="Enter text here...")
submit_button = gr.Button("Synthesize")
output_audio = gr.Audio(label="Synthesized Speech", type="numpy", show_download_button=False)
output_text = gr.Textbox(label="Output Text", visible=False)
def process_and_output(text):
audio, message = text_to_speech(text)
if message:
return audio, message
else:
return audio, None
submit_button.click(process_and_output, inputs=input_text, outputs=[output_audio, output_text])
blocks.launch()
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