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import importlib | |
import re | |
import gradio as gr | |
import yaml | |
from gradio.inputs import Textbox, Audio | |
from inference.base_tts_infer import BaseTTSInfer | |
from utils.hparams import set_hparams | |
from utils.hparams import hparams as hp | |
import numpy as np | |
from data_gen.tts.data_gen_utils import is_sil_phoneme, PUNCS | |
class GradioInfer: | |
def __init__(self, exp_name, config, inference_cls, title, description, article, example_inputs): | |
self.exp_name = exp_name | |
self.config = config | |
self.title = title | |
self.description = description | |
self.article = article | |
self.example_inputs = example_inputs | |
pkg = ".".join(inference_cls.split(".")[:-1]) | |
cls_name = inference_cls.split(".")[-1] | |
self.inference_cls = getattr(importlib.import_module(pkg), cls_name) | |
def greet(self, text, audio): | |
sents = re.split(rf'([{PUNCS}])', text.replace('\n', ',')) | |
if sents[-1] not in list(PUNCS): | |
sents = sents + ['.'] | |
audio_outs = [] | |
s = "" | |
for i in range(0, len(sents), 2): | |
if len(sents[i]) > 0: | |
s += sents[i] + sents[i + 1] | |
if len(s) >= 400 or (i >= len(sents) - 2 and len(s) > 0): | |
audio_out = self.infer_ins.infer_once({ | |
'text': s, | |
'ref_audio': audio | |
}) | |
audio_out = audio_out * 32767 | |
audio_out = audio_out.astype(np.int16) | |
audio_outs.append(audio_out) | |
audio_outs.append(np.zeros(int(hp['audio_sample_rate'] * 0.3)).astype(np.int16)) | |
s = "" | |
audio_outs = np.concatenate(audio_outs) | |
return hp['audio_sample_rate'], audio_outs | |
def run(self): | |
set_hparams(exp_name=self.exp_name, config=self.config) | |
infer_cls = self.inference_cls | |
self.infer_ins: BaseTTSInfer = infer_cls(hp) | |
example_inputs = self.example_inputs | |
iface = gr.Interface(fn=self.greet, | |
inputs=[ | |
Textbox(lines=10, placeholder=None, default=example_inputs[0], label="input text"), | |
Audio(label="reference audio"), | |
], | |
outputs="audio", | |
allow_flagging="never", | |
title=self.title, | |
description=self.description, | |
article=self.article, | |
examples=example_inputs, | |
enable_queue=True) | |
iface.launch() | |
if __name__ == '__main__': | |
gradio_config = yaml.safe_load(open('inference/gradio/gradio_settings.yaml')) | |
g = GradioInfer(**gradio_config) | |
g.run() | |