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import gradio as gr
import tempfile
from TTS.utils.synthesizer import Synthesizer
from huggingface_hub import hf_hub_download
import torch
CUDA = torch.cuda.is_available()
REPO_ID = "collectivat/catotron-ona"
my_title = "Catotron Text-to-Speech"
my_description = "This model is based on Fast Speech implemented in 🐸 [Coqui.ai](https://coqui.ai/)."
my_examples = [
["Catotron, sintesi de la parla obert i lliure en català."],
["Leonor Ferrer Girabau va ser una delineant, mestra i activista barcelonina, nascuda al carrer actual de la Concòrdia del Poble-sec, que es va convertir en la primera dona a obtenir el títol de delineant a Catalunya i a l’estat."],
["S'espera un dia anticiclònic amb temperatures suaus i vent fluix."]
]
my_inputs = [
gr.Textbox(lines=5, label="Input Text")
]
my_outputs = gr.Audio(type="filepath", label="Output Audio")
def tts(text: str, split_sentences: bool = True):
best_model_path = hf_hub_download(repo_id=REPO_ID, filename="fast-speech_best_model.pth")
config_path = hf_hub_download(repo_id=REPO_ID, filename="fast-speech_config.json")
vocoder_model = hf_hub_download(repo_id=REPO_ID, filename="ljspeech--hifigan_v2_model_file.pth")
vocoder_config = hf_hub_download(repo_id=REPO_ID, filename="ljspeech--hifigan_v2_config.json")
synthesizer = Synthesizer(
tts_checkpoint=best_model_path,
tts_config_path=config_path,
tts_speakers_file=None,
tts_languages_file=None,
vocoder_checkpoint=vocoder_model,
vocoder_config=vocoder_config,
encoder_checkpoint="",
encoder_config="",
use_cuda=CUDA
)
# replace oov characters
text = text.replace("\n", ". ")
text = text.replace("(", ",")
text = text.replace(")", ",")
text = text.replace(";", ",")
# create audio file
wavs = synthesizer.tts(text, split_sentences=split_sentences)
with tempfile.NamedTemporaryFile(suffix = ".wav", delete = False) as fp:
synthesizer.save_wav(wavs, fp)
return fp.name
iface = gr.Interface(
fn=tts,
inputs=my_inputs,
outputs=my_outputs,
title=my_title,
description = my_description,
examples = my_examples,
cache_examples=True
)
iface.launch()
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