text-to-speech init

.gitignore

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+.gradio

app.py

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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
+
+
+class CustomFlagging(gr.FlaggingCallback):
+    def setup(self, *args, **kwargs):
+        pass  # Optional setup steps
+
+    def flag(self, flag_data, flag_option=None, username=None):
+        print(f"Аудио: {flag_data}, Сообщение: {flag_option}")
+
+
+# Custom options
+flagging_callback = CustomFlagging()
+flagging_options = ["Хорошая озвучка", "Слышен механический треск", "Не совпадает произношение букв", 'Проглочены буквы']  # Customize options
+
+
+# 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)
+
+tts_pipeline = pipeline("text-to-speech", model=model_name)
+
+
+def tts_function(input_text):
+    inputs = tokenizer(text=input_text, return_tensors="pt")
+    speech = tts_pipeline(input_text)
+    set_seed(900)  
+
+    # make speech faster and more noisy
+    model.speaking_rate = 0.9
+    model.noise_scale = 0
+
+    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"),
+    outputs=gr.Audio(label="Аудио"),
+    title="Text-to-speech Лезги ЧIалал",
+    flagging_mode="auto",  # Enable the flagging button
+)
+
+# Launch the app
+interface.launch()

requirements.txt

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+gradio
+transformers
+scipy
+torch