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import tempfile
import numpy as np
from scipy.io.wavfile import write
import gradio as gr
from transformers import VitsTokenizer, VitsModel, set_seed, pipeline
from numToLez import numToLez
import spaces
# Load your fine-tuned model
model_name = "leks-forever/vits_lez_tts" # Replace with your Hugging Face model name
tokenizer = VitsTokenizer.from_pretrained(model_name)
model = VitsModel.from_pretrained(model_name)
model.to("cuda")
tts_pipeline = pipeline("text-to-speech", model=model_name)
tts_pipeline.to("cuda")
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
@spaces.GPU()
def tts_function(input_text, speaking_rate, noise_scale, add_pauses):
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")
speech = tts_pipeline(input_text)
set_seed(900)
# make speech faster and more noisy
model.speaking_rate = speaking_rate
model.noise_scale = noise_scale
sampling_rate = speech["sampling_rate"]
outputs = model(**inputs)
waveform = outputs.waveform[0]
waveform = waveform.detach().cpu().float().numpy()
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmpfile:
write(tmpfile.name, rate=sampling_rate, data=waveform)
return tmpfile.name # Return the filepath
# interface = gr.Interface(
# fn=tts_function,
# inputs=[
# gr.Textbox(label="Введите текст на лезгинском"),
# gr.Slider(label="Скорость речи", minimum=0, maximum=2, step=0.1, value=0.9),
# gr.Slider(label="Шум", minimum=0, maximum=5, step=0.1, value=0),
# gr.Checkbox(label="Сделать паузы длиннее", value=False),
# ],
# outputs=gr.Audio(label="Аудио"),
# title="Text-to-speech Лезги ЧIалал",
# submit_button=gr.Button("Сгенерировать"),
# flagging_mode="auto", # Enable the flagging button
# )
with gr.Blocks() as interface:
gr.Markdown("### Text-to-speech Лезги ЧIалал")
with gr.Row():
# Left Column: Inputs
with gr.Column():
input_text = gr.Textbox(label="Введите текст на лезгинском", elem_id="custom-input")
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")
# Right Column: Output
with gr.Column():
output_audio = gr.Audio(label="Аудио")
# Link function to button
submit_button.click(
fn=tts_function,
inputs=[input_text, speaking_rate, noise_scale, add_pauses],
outputs=output_audio,
)
# Launch the app
interface.launch()
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