lez-tts / app.py
alialek's picture
fix
334cc2e
import tempfile
from scipy.io.wavfile import write
import gradio as gr
from transformers import VitsTokenizer, VitsModel, set_seed, pipeline
import torch
from datetime import datetime
model_name = "leks-forever/vits_lez_tts"
tokenizer = VitsTokenizer.from_pretrained(model_name)
model = VitsModel.from_pretrained(model_name)
tts_pipeline = pipeline("text-to-speech", model=model_name)
new_sentence = '!.?'
in_sentence = ',-.:;'
def canonize_lez(text):
for abruptive_letter in ['к', 'К', 'п', 'П', 'т', 'Т', 'ц', 'Ц', 'ч', 'Ч']:
for abruptive_symbol in ['1', 'l', 'i', 'I', '|', 'ӏ', 'Ӏ', 'ӏ']:
text = text.replace(abruptive_letter+abruptive_symbol, abruptive_letter+'Ӏ')
return text
def tts_function(input_text, speaking_rate, noise_scale, add_pauses):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
fixed_text = canonize_lez(input_text)
if add_pauses:
for symb in new_sentence:
fixed_text = fixed_text.replace(symb, ' ')
for symb in in_sentence:
fixed_text = fixed_text.replace(symb, ' ')
inputs = tokenizer(text=fixed_text, return_tensors="pt")
inputs = {key: value.to(device) for key, value in inputs.items()}
model.to(device)
model.eval()
set_seed(900)
model.speaking_rate = speaking_rate
model.noise_scale = noise_scale
with torch.no_grad():
outputs = model(**inputs)
waveform = outputs.waveform[0]
waveform = waveform.detach().cpu().float().numpy()
sampling_rate = model.config.sampling_rate
timestamp = datetime.now().strftime("H%M%S")
filename_part = input_text[:20].replace(' ', '_')
filename = f"{filename_part}_{timestamp}.wav"
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmpfile:
write(filename, rate=sampling_rate, data=waveform)
return filename
with gr.Blocks() as interface:
gr.Markdown("### Text-to-speech Лезги ЧIалал")
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label="Введите текст на лезгинском", lines=4)
add_pauses = gr.Checkbox(label="Добавить больше пауз у знаков препинания", value=False)
speaking_rate = gr.Slider(label="Скорость речи (speaking_rate)", minimum=0, maximum=2, step=0.1, value=0.9)
noise_scale = gr.Slider(label="Шум (noise_scale)", minimum=0, maximum=5, step=0.1, value=0)
submit_button = gr.Button("Сгенерировать")
with gr.Column():
output_audio = gr.Audio(label="Аудио")
submit_button.click(
fn=tts_function,
inputs=[input_text, speaking_rate, noise_scale, add_pauses],
outputs=output_audio,
)
interface.launch()