File size: 3,048 Bytes
38a727d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
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')
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)
|