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| import torch | |
| import scipy.io.wavfile | |
| from espnet2.bin.tts_inference import Text2Speech | |
| from espnet2.utils.types import str_or_none | |
| tagen = 'kan-bayashi/ljspeech_vits' | |
| vocoder_tagen = "none" | |
| text2speechen = Text2Speech.from_pretrained( | |
| model_tag=str_or_none(tagen), | |
| vocoder_tag=str_or_none(vocoder_tagen), | |
| device="cpu", | |
| # Only for Tacotron 2 & Transformer | |
| threshold=0.5, | |
| # Only for Tacotron 2 | |
| minlenratio=0.0, | |
| maxlenratio=10.0, | |
| use_att_constraint=False, | |
| backward_window=1, | |
| forward_window=3, | |
| # Only for FastSpeech & FastSpeech2 & VITS | |
| speed_control_alpha=1.0, | |
| # Only for VITS | |
| noise_scale=0.333, | |
| noise_scale_dur=0.333, | |
| ) | |
| def inference(text, lang): | |
| print("Converting to Audio") | |
| with torch.no_grad(): | |
| if lang == "english": | |
| wav = text2speechen(text)["wav"] | |
| scipy.io.wavfile.write( | |
| "./audio/out.wav", text2speechen.fs, wav.view(-1).cpu().numpy()) | |
| return "./audio/out.wav" | |