| | from parler_tts import ParlerTTSForConditionalGeneration |
| | from transformers import AutoTokenizer |
| | import soundfile as sf |
| | import pygame |
| | from dora import DoraStatus |
| |
|
| | model = ParlerTTSForConditionalGeneration.from_pretrained( |
| | "/mnt/c/parler-tts-mini-jenny-30H" |
| | ).to("cuda:0") |
| | tokenizer = AutoTokenizer.from_pretrained("/mnt/c/parler-tts-mini-jenny-30H") |
| |
|
| | pygame.mixer.init() |
| |
|
| | input_ids = tokenizer( |
| | "Jenny delivers her words quite expressively, in a very confined sounding environment with clear audio quality.", |
| | 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( |
| | input_ids=input_ids, |
| | min_new_tokens=100, |
| | 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, |
| | ) |
| |
|
| | pygame.mixer.music.load(f"parler_tts_out.wav") |
| | pygame.mixer.music.play() |
| | while pygame.mixer.get_busy(): |
| | pass |
| |
|
| | return DoraStatus.CONTINUE |
| |
|
| |
|
| | |
| | |
| |
|
| | |
| |
|
