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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() | |