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| import importlib | |
| import re | |
| import gradio as gr | |
| import yaml | |
| from gradio.inputs import Textbox | |
| from inference.tts.base_tts_infer import BaseTTSInfer | |
| from utils.commons.hparams import set_hparams | |
| from utils.commons.hparams import hparams as hp | |
| import numpy as np | |
| from utils.text.text_encoder import PUNCS | |
| class GradioInfer: | |
| def __init__(self, exp_name, inference_cls, title, description, article, example_inputs): | |
| self.exp_name = exp_name | |
| 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): | |
| 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 | |
| }) | |
| 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) | |
| 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"), | |
| outputs="audio", | |
| allow_flagging="never", | |
| title=self.title, | |
| description=self.description, | |
| article=self.article, | |
| examples=example_inputs, | |
| enable_queue=True) | |
| iface.launch(cache_examples=True) | |
| if __name__ == '__main__': | |
| gradio_config = yaml.safe_load(open('inference/tts/gradio/gradio_settings.yaml')) | |
| g = GradioInfer(**gradio_config) | |
| g.run() | |