Merge pull request #11 from noisyle/upstream

Add a webui for Inference (need gradio)

webui.py

@@ -0,0 +1,95 @@
+import torch
+import argparse
+import commons
+import utils
+from models import SynthesizerTrn
+from text.symbols import symbols
+from text import cleaned_text_to_sequence, get_bert
+from text.cleaner import clean_text
+import gradio as gr
+import webbrowser
+
+def get_text(text, language_str, hps):
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    bert = get_bert(norm_text, word2ph, language_str)
+
+    assert bert.shape[-1] == len(phone)
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+
+    return bert, phone, tone, language
+
+def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
+    bert, phones, tones, lang_ids = get_text(text, "ZH", hps,)
+    with torch.no_grad():
+        x_tst=phones.to(device).unsqueeze(0)
+        tones=tones.to(device).unsqueeze(0)
+        lang_ids=lang_ids.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
+                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
+        return audio
+
+def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
+    with torch.no_grad():
+        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
+    return "Success", (hps.data.sampling_rate, audio)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-m", "--model", default="./logs/as/G_8000.pth", help="path of your model")
+    parser.add_argument("-c", "--config", default="./configs/config.json", help="path of your config file")
+    parser.add_argument("--share", default=False, help="make link public")
+
+    args = parser.parse_args()
+    hps = utils.get_hparams_from_file(args.config)
+
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model).to(device)
+    _ = net_g.eval()
+
+    _ = utils.load_checkpoint(args.model, net_g, None,skip_optimizer=True)
+
+    speaker_ids = hps.data.spk2id
+    speakers = list(speaker_ids.keys())
+    app = gr.Blocks()
+    with app:
+        with gr.Row():
+            with gr.Column():
+                text = gr.TextArea(label="Text", placeholder="Input Text Here",
+                                      value="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。")
+                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
+                sdp_ratio = gr.Slider(minimum=0.1, maximum=2, value=0.2, step=0.1, label='SDP Ratio')
+                noise_scale = gr.Slider(minimum=0.1, maximum=2, value=0.5, step=0.1, label='Noise Scale')
+                noise_scale_w = gr.Slider(minimum=0.1, maximum=2, value=0.6, step=0.1, label='Noise Scale W')
+                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1.2, step=0.1, label='Length Scale')
+                btn = gr.Button("Generate!", variant="primary")
+            with gr.Column():
+                text_output = gr.Textbox(label="Message")
+                audio_output = gr.Audio(label="Output Audio")
+
+        btn.click(tts_fn,
+                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
+                outputs=[text_output, audio_output])
+    
+    webbrowser.open("http://127.0.0.1:7860")
+    app.launch(share=args.share)