| from parler_tts import ParlerTTSForConditionalGeneration | |
| from transformers import AutoTokenizer | |
| import soundfile as sf | |
| import pygame | |
| from dora import DoraStatus | |
| model = ParlerTTSForConditionalGeneration.from_pretrained( | |
| "parler-tts/parler_tts_mini_v0.1" | |
| ).to("cuda:0") | |
| tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1") | |
| pygame.mixer.init() | |
| input_ids = tokenizer( | |
| "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast.", | |
| return_tensors="pt", | |
| ).input_ids.to("cuda:0") | |
| class Operator: | |
| def on_event( | |
| self, | |
| dora_event, | |
| send_output, | |
| ): | |
| if dora_event["type"] == "INPUT": | |
| generation = model.generate( | |
| max_new_tokens=300, | |
| input_ids=input_ids, | |
| prompt_input_ids=tokenizer( | |
| dora_event["value"][0].as_py(), return_tensors="pt" | |
| ).input_ids.to("cuda:0"), | |
| ) | |
| print(dora_event["value"][0].as_py(), flush=True) | |
| sf.write( | |
| f"parler_tts_out.wav", | |
| generation.cpu().numpy().squeeze(), | |
| model.config.sampling_rate, | |
| ) | |
| while pygame.mixer.get_busy(): | |
| pass | |
| pygame.mixer.music.load(f"parler_tts_out.wav") | |
| pygame.mixer.music.play() | |
| return DoraStatus.CONTINUE | |