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import torch |
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import os |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import text_to_sequence |
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from scipy.io.wavfile import write |
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import gradio as gr |
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print("Running GRadio", gr.__version__) |
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model_path = "vits2_pytorch/G_390000.pth" |
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config_path = "vits2_pytorch/vits2_vctk_cat_inference.json" |
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hps = utils.get_hparams_from_file(config_path) |
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if ( |
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"use_mel_posterior_encoder" in hps.model.keys() |
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and hps.model.use_mel_posterior_encoder == True |
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): |
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print("Using mel posterior encoder for VITS2") |
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posterior_channels = 80 |
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hps.data.use_mel_posterior_encoder = True |
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else: |
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print("Using lin posterior encoder for VITS1") |
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posterior_channels = hps.data.filter_length // 2 + 1 |
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hps.data.use_mel_posterior_encoder = False |
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net_g = SynthesizerTrn( |
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len(symbols), |
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posterior_channels, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model |
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) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(model_path, net_g, None) |
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def get_text(text, hps): |
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text_norm = text_to_sequence(text, hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = torch.LongTensor(text_norm) |
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return text_norm |
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def tts(text:str, speaker_id:int, speed:float, noise_scale:float=0.667, noise_scale_w:float=0.8): |
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stn_tst = get_text(text, hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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sid = torch.LongTensor([speaker_id]) |
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waveform = ( |
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net_g.infer( |
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x_tst, |
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x_tst_lengths, |
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sid=sid, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=1/speed, |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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return gr.make_waveform((22050, waveform)) |
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title = """ |
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<div style="text-align: center; max-width: 700px; margin: 0 auto;"> |
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<div |
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style="display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;" |
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> <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;"> |
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VITS2 TTS Catalan Demo |
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</h1> </div> |
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</div> |
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""" |
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description = """ |
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VITS2 is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. VITS2 improved the |
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training and inference efficiency and naturalness by introducing adversarial learning into the duration predictor. The transformer |
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block was added to the normalizing flows to capture the long-term dependency when transforming the distribution. |
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The synthesis quality was improved by incorporating Gaussian noise into the alignment search. |
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This model is being trained in openslr69 and festcat datasets |
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""" |
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article = "Model by Jungil Kong, et al. from SK telecom. Demo by BSC." |
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vits2_inference = gr.Interface( |
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fn=tts, |
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inputs=[ |
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gr.Textbox( |
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value="m'ha costat desenvolupar molt una veu, i ara que la tinc no estaré en silenci.", |
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max_lines=1, |
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label="Input text", |
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), |
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gr.Slider( |
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1, |
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47, |
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value=10, |
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step=1, |
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label="Speaker id", |
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info=f"This model is trained on 47 speakers. You can prompt the model using one of these speaker ids.", |
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), |
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gr.Slider( |
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0.5, |
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1.5, |
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value=1, |
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step=0.1, |
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label="Speed", |
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), |
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gr.Slider( |
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0.2, |
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2.0, |
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value=0.667, |
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step=0.01, |
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label="Noise scale", |
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), |
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gr.Slider( |
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0.2, |
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2.0, |
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value=0.8, |
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step=0.01, |
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label="Noise scale w", |
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), |
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], |
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outputs=gr.Audio(), |
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) |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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gr.TabbedInterface([vits2_inference], ["Multispeaker"]) |
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gr.Markdown(article) |
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demo.queue(max_size=10) |
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demo.launch(show_api=False, server_name="0.0.0.0", server_port=7860) |
