| import importlib |
| import re |
|
|
| import gradio as gr |
| import yaml |
| from gradio.inputs import Textbox |
|
|
| 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): |
| 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, 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"), |
| outputs="audio", |
| allow_flagging="never", |
| title=self.title, |
| description=self.description, |
| article=self.article, |
| examples=example_inputs, |
| enable_queue=True) |
| iface.launch(share=True,cache_examples=True) |
|
|
|
|
| if __name__ == '__main__': |
| gradio_config = yaml.safe_load(open('inference/gradio/gradio_settings.yaml')) |
| g = GradioInfer(**gradio_config) |
| g.run() |
|
|
