| from huggingface_hub import hf_hub_download | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from TTS.tts.configs.xtts_config import XttsConfig | |
| from TTS.tts.models.xtts import Xtts | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| def load_model(): | |
| model_path = hf_hub_download( | |
| repo_id="tartuNLP/XTTS-v2-est", | |
| filename="model.pth", | |
| ) | |
| config_path = hf_hub_download( | |
| repo_id="tartuNLP/XTTS-v2-est", | |
| filename="config.json", | |
| ) | |
| vocab_path = hf_hub_download( | |
| repo_id="tartuNLP/XTTS-v2-est", | |
| filename="vocab.json", | |
| ) | |
| config = XttsConfig() | |
| config.load_json(config_path) | |
| XTTS_MODEL = Xtts.init_from_config(config) | |
| XTTS_MODEL.load_checkpoint(config, checkpoint_path=model_path, vocab_path=vocab_path, use_deepspeed=False) | |
| XTTS_MODEL.to(device) | |
| return XTTS_MODEL | |
| model = load_model() | |
| def predict(sentence, language, reference_clip): | |
| if not reference_clip or not reference_clip.split('.')[-1] in ['mp3', 'wav']: | |
| return | |
| gpt_cond_latent, speaker_embedding = model.get_conditioning_latents( | |
| audio_path=reference_clip, | |
| gpt_cond_len=model.config.gpt_cond_len, | |
| max_ref_length=model.config.max_ref_len, | |
| sound_norm_refs=model.config.sound_norm_refs, | |
| ) | |
| wav_chunks = [] | |
| for chunk in model.inference_stream( | |
| text=sentence, | |
| language=language, | |
| gpt_cond_latent=gpt_cond_latent, | |
| speaker_embedding=speaker_embedding, | |
| temperature=0.1, | |
| length_penalty=1.0, | |
| repetition_penalty=10.0, | |
| top_k=10, | |
| top_p=0.3, | |
| ): | |
| if chunk is not None: | |
| wav_chunks.append(chunk) | |
| return (22050, torch.cat(wav_chunks, dim=0).unsqueeze(0)[0].numpy()) | |
| demo = gr.Interface( | |
| title="XTTSv2-est Demo", | |
| description="To get the best results, provide a reference clip around the same length as the output sentence you want.", | |
| fn=predict, | |
| inputs=["text", gr.Dropdown(["et", "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn", "hu", "ko", "ja", "hi"]), gr.File()], | |
| outputs=[gr.Audio()], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |