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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)
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