add gradio app

app.py

@@ -0,0 +1,150 @@
+## VCTK
+import torch
+import os
+
+import commons
+import utils
+from models import SynthesizerTrn
+from text.symbols import symbols
+from text import text_to_sequence
+
+from scipy.io.wavfile import write
+import gradio as gr 
+
+print("Running GRadio", gr.__version__)
+
+model_path = "vits2_pytorch/G_390000.pth"
+config_path = "vits2_pytorch/vits2_vctk_cat_inference.json"
+
+hps = utils.get_hparams_from_file(config_path)
+
+if (
+    "use_mel_posterior_encoder" in hps.model.keys()
+    and hps.model.use_mel_posterior_encoder == True
+):
+    print("Using mel posterior encoder for VITS2")
+    posterior_channels = 80  # vits2
+    hps.data.use_mel_posterior_encoder = True
+else:
+    print("Using lin posterior encoder for VITS1")
+    posterior_channels = hps.data.filter_length // 2 + 1
+    hps.data.use_mel_posterior_encoder = False
+
+net_g = SynthesizerTrn(
+    len(symbols),
+    posterior_channels,
+    hps.train.segment_size // hps.data.hop_length,
+    n_speakers=hps.data.n_speakers,
+    **hps.model
+)
+_ = net_g.eval()
+
+_ = utils.load_checkpoint(model_path, net_g, None)
+
+
+def get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    #text_norm = cleaned_text_to_sequence(text) # if model was trained with text
+
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+def tts(text:str, speaker_id:int, speed:float, noise_scale:float=0.667, noise_scale_w:float=0.8):
+
+    stn_tst = get_text(text, hps)
+    with torch.no_grad():
+        x_tst = stn_tst.unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
+        sid = torch.LongTensor([speaker_id])
+        waveform = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                sid=sid,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=1/speed,
+            )[0][0, 0]
+            .data.cpu()
+            .float()
+            .numpy()
+        )
+    
+    return gr.make_waveform((22050, waveform))
+
+## GUI space
+
+title = """
+<div style="text-align: center; max-width: 700px; margin: 0 auto;">
+    <div
+        style="display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;"
+    > <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
+        VITS2 TTS Catalan Demo
+    </h1> </div>
+</div>
+ """
+
+description = """
+VITS2 is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. VITS2 improved the
+training and inference efficiency and naturalness by introducing adversarial learning into the duration predictor. The transformer
+block was added to the normalizing flows to capture the long-term dependency when transforming the distribution.
+The synthesis quality was improved by incorporating Gaussian noise into the alignment search. 
+
+This model is being trained in openslr69 and festcat datasets
+"""
+
+article = "Model by Jungil Kong, et al. from SK telecom. Demo by BSC."
+
+vits2_inference = gr.Interface(
+    fn=tts,
+    inputs=[
+        gr.Textbox(
+            value="m'ha costat desenvolupar molt una veu, i ara que la tinc no estaré en silenci.",
+            max_lines=1,
+            label="Input text",
+        ),
+        gr.Slider(
+            1,
+            47,
+            value=10,
+            step=1,
+            label="Speaker id",
+            info=f"This model is trained on 47 speakers. You can prompt the model using one of these speaker ids.",
+        ),
+        gr.Slider(
+            0.5,
+            1.5,
+            value=1,
+            step=0.1,
+            label="Speed",
+        ),
+        gr.Slider(
+            0.2,
+            2.0,
+            value=0.667,
+            step=0.01,
+            label="Noise scale",
+        ),
+        gr.Slider(
+            0.2,
+            2.0,
+            value=0.8,
+            step=0.01,
+            label="Noise scale w",
+        ),
+    ],
+    outputs=gr.Audio(),
+)
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    gr.TabbedInterface([vits2_inference], ["Multispeaker"])
+    gr.Markdown(article)
+
+demo.queue(max_size=10)
+demo.launch(show_api=False, server_name="0.0.0.0", server_port=7860)