|
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 = {} |
|
|
|
|
|
d = ModelDownloader() |
|
kwargs = d.download_and_unpack(model_tag) |
|
|
|
|
|
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') |
|
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", "english"], type="value", default="gronings", label="language")], |
|
[gr.outputs.Audio(type="file", label="Output"), gr.outputs.File()], |
|
title=title, |
|
examples=examples |
|
).launch(enable_queue=True, debug=True) |
|
|
