| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForPreTraining | |
| import soundfile as sf | |
| # Load the tokenizer and model for Bulgarian TTS (Text-to-Speech) | |
| tokenizer = AutoTokenizer.from_pretrained("Opit/mms_tts_bulgarian_finetuning") | |
| model = AutoModelForPreTraining.from_pretrained("Opit/mms_tts_bulgarian_finetuning") | |
| # TTS λ³ν ν¨μ (text-to-speech conversion) | |
| def tts_generate(text): | |
| inputs = tokenizer(text, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Convert the model outputs to audio format (you need to implement this depending on model specifics) | |
| # This will depend on how the model's outputs are structured | |
| # For now, let's assume you need a simple conversion to waveform/audio | |
| # Placeholder: Assuming `outputs` contains audio data that can be returned directly as .wav format | |
| # You might need to adjust this based on how the TTS model is structured and how it outputs speech | |
| audio = outputs['logits'] # Adjust according to your model's output structure | |
| # Return audio output (in numpy format) and the sample rate (this might be specific to your model) | |
| return audio.numpy(), 22050 # Assuming the output is sampled at 22050 Hz | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=tts_generate, | |
| inputs="text", | |
| outputs="audio", | |
| title="Bulgarian TTS (Text-to-Speech)", | |
| description="Enter text to generate speech in Bulgarian." | |
| ) | |
| # Run the interface | |
| if __name__ == "__main__": | |
| iface.launch() | |