| import gradio as gr | |
| import time | |
| import urllib.request | |
| from pathlib import Path | |
| import os | |
| import torch | |
| import scipy.io.wavfile | |
| from espnet2.bin.tts_inference import Text2Speech | |
| from espnet2.utils.types import str_or_none | |
| # def load_model(model_tag, vocoder_tag): | |
| # from espnet_model_zoo.downloader import ModelDownloader | |
| # kwargs = {} | |
| # # Model | |
| # d = ModelDownloader() | |
| # kwargs = d.download_and_unpack(model_tag) | |
| # # Vocoder | |
| # download_dir = Path(os.path.expanduser("~/.cache/parallel_wavegan")) | |
| # vocoder_dir = download_dir / vocoder_tag | |
| # os.makedirs(vocoder_dir, exist_ok=True) | |
| # kwargs["vocoder_config"] = vocoder_dir / "config.yml" | |
| # if not kwargs["vocoder_config"].exists(): | |
| # urllib.request.urlretrieve(f"https://huggingface.co/{vocoder_tag}/resolve/main/config.yml", kwargs["vocoder_config"]) | |
| # kwargs["vocoder_file"] = vocoder_dir / "checkpoint-50000steps.pkl" | |
| # if not kwargs["vocoder_file"].exists(): | |
| # urllib.request.urlretrieve(f"https://huggingface.co/{vocoder_tag}/resolve/main/checkpoint-50000steps.pkl", kwargs["vocoder_file"]) | |
| # return Text2Speech( | |
| # **kwargs, | |
| # device="cpu", | |
| # threshold=0.5, | |
| # minlenratio=0.0, | |
| # maxlenratio=10.0, | |
| # use_att_constraint=True, | |
| # backward_window=1, | |
| # forward_window=4, | |
| # ) | |
| # gos_text2speech = load_model('https://huggingface.co/wietsedv/tacotron2-gronings/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip', 'wietsedv/parallelwavegan-gronings') | |
| # nld_text2speech = load_model('https://huggingface.co/wietsedv/tacotron2-dutch/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip', 'wietsedv/parallelwavegan-dutch') | |
| gos_text2speech = Text2Speech.from_pretrained( | |
| model_tag="https://huggingface.co/wietsedv/tacotron2-gronings/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip", | |
| vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3", | |
| device="cpu", | |
| threshold=0.5, | |
| minlenratio=0.0, | |
| maxlenratio=10.0, | |
| use_att_constraint=True, | |
| backward_window=1, | |
| forward_window=4, | |
| ) | |
| nld_text2speech = Text2Speech.from_pretrained( | |
| model_tag="https://huggingface.co/wietsedv/tacotron2-dutch/resolve/main/tts_ljspeech_finetune_tacotron2.v5_train.loss.ave.zip", | |
| vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3", | |
| device="cpu", | |
| threshold=0.5, | |
| minlenratio=0.0, | |
| maxlenratio=10.0, | |
| use_att_constraint=True, | |
| backward_window=1, | |
| forward_window=4, | |
| ) | |
| #eng_text2speech = Text2Speech.from_pretrained( | |
| # model_tag="kan-bayashi/ljspeech_tacotron2", | |
| # vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v3", | |
| # device="cpu", | |
| # threshold=0.5, | |
| # minlenratio=0.0, | |
| # maxlenratio=10.0, | |
| # use_att_constraint=True, | |
| # backward_window=1, | |
| # forward_window=4, | |
| #) | |
| def inference(text,lang): | |
| with torch.no_grad(): | |
| if lang == "gronings": | |
| wav = gos_text2speech(text)["wav"] | |
| scipy.io.wavfile.write("out.wav", gos_text2speech.fs , wav.view(-1).cpu().numpy()) | |
| if lang == "dutch": | |
| wav = nld_text2speech(text)["wav"] | |
| scipy.io.wavfile.write("out.wav", nld_text2speech.fs , wav.view(-1).cpu().numpy()) | |
| #if lang == "english": | |
| # wav = eng_text2speech(text)["wav"] | |
| # scipy.io.wavfile.write("out.wav", eng_text2speech.fs , wav.view(-1).cpu().numpy()) | |
| return "out.wav", "out.wav" | |
| title = "GroTTS" | |
| examples = [ | |
| ['Ze gingen mit klas noar waddendiek, over en deur bragel lopen.', 'gronings'] | |
| ] | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Textbox(label="input text", lines=3), gr.inputs.Radio(choices=["gronings", "dutch"], type="value", default="gronings", label="language")], | |
| [gr.outputs.Audio(type="file", label="Output"), gr.outputs.File()], | |
| title=title, | |
| examples=examples | |
| ).launch(enable_queue=True) | |