Create app.py

app.py

@@ -0,0 +1,141 @@
+import importlib
+import re
+
+import gradio as gr
+import yaml
+from gradio.components import Textbox, Dropdown
+
+from inference.m4singer.base_svs_infer import BaseSVSInfer
+from utils.hparams import set_hparams
+from utils.hparams import hparams as hp
+import numpy as np
+from inference.m4singer.gradio.share_btn import community_icon_html, loading_icon_html, share_js
+
+class GradioInfer:
+    def __init__(self, exp_name, inference_cls, title, description, article, example_inputs):
+        self.exp_name = exp_name
+        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, singer, text, notes, notes_duration):
+        PUNCS = '。？；：'
+        sents = re.split(rf'([{PUNCS}])', text.replace('\n', ','))
+        sents_notes = re.split(rf'([{PUNCS}])', notes.replace('\n', ','))
+        sents_notes_dur = re.split(rf'([{PUNCS}])', notes_duration.replace('\n', ','))
+
+        if sents[-1] not in list(PUNCS):
+            sents = sents + ['']
+            sents_notes = sents_notes + ['']
+            sents_notes_dur = sents_notes_dur + ['']
+
+        audio_outs = []
+        s, n, n_dur = "", "", ""
+        for i in range(0, len(sents), 2):
+            if len(sents[i]) > 0:
+                s += sents[i] + sents[i + 1]
+                n += sents_notes[i] + sents_notes[i+1]
+                n_dur += sents_notes_dur[i] + sents_notes_dur[i+1]
+            if len(s) >= 400 or (i >= len(sents) - 2 and len(s) > 0):
+                audio_out = self.infer_ins.infer_once({
+                    'spk_name': singer,
+                    'text': s,
+                    'notes': n,
+                    'notes_duration': n_dur,
+                })
+                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 = ""
+                n = ""
+        audio_outs = np.concatenate(audio_outs)
+        return (hp['audio_sample_rate'], audio_outs), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+
+    def run(self):
+        set_hparams(config=f'checkpoints/{self.exp_name}/config.yaml', exp_name=self.exp_name, print_hparams=False)
+        infer_cls = self.inference_cls
+        self.infer_ins: BaseSVSInfer = infer_cls(hp)
+        example_inputs = self.example_inputs
+        for i in range(len(example_inputs)):
+            singer, text, notes, notes_dur = example_inputs[i].split('<sep>')
+            example_inputs[i] = [singer, text, notes, notes_dur]
+
+        singerList = \
+            [
+            'Tenor-1', 'Tenor-2', 'Tenor-3', 'Tenor-4', 'Tenor-5', 'Tenor-6', 'Tenor-7',
+            'Alto-1', 'Alto-2', 'Alto-3', 'Alto-4', 'Alto-5', 'Alto-6', 'Alto-7',
+            'Soprano-1', 'Soprano-2', 'Soprano-3',
+            'Bass-1',  'Bass-2',  'Bass-3',
+            ]
+
+        css = """
+        #share-btn-container {
+            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
+        }
+        #share-btn {
+            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
+        }
+        #share-btn * {
+            all: unset;
+        }
+        #share-btn-container div:nth-child(-n+2){
+            width: auto !important;
+            min-height: 0px !important;
+        }
+        #share-btn-container .wrap {
+            display: none !important;
+        }
+        """
+        with gr.Blocks(css=css) as demo:
+            gr.HTML("""<div style="text-align: center; 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: 10px; margin-top: 14px;">
+                              M4Singer
+                          </h1>
+                          </div>
+                        </div>
+                        """
+                    )
+            gr.Markdown(self.description)
+            with gr.Row():
+                with gr.Column():
+                    singer_l = Dropdown(choices=singerList, value=example_inputs[0][0], label="SingerID", elem_id="inp_singer")
+                    inp_text = Textbox(lines=2, placeholder=None, value=example_inputs[0][1], label="input text", elem_id="inp_text")
+                    inp_note = Textbox(lines=2, placeholder=None, value=example_inputs[0][2], label="input note", elem_id="inp_note")
+                    inp_duration = Textbox(lines=2, placeholder=None, value=example_inputs[0][3], label="input duration", elem_id="inp_duration")
+                    generate = gr.Button("Generate Singing Voice from Musical Score")
+                with gr.Column():
+                    singing_output = gr.Audio(label="Result", type="numpy", elem_id="music-output")
+
+                    with gr.Group(elem_id="share-btn-container"):
+                        community_icon = gr.HTML(community_icon_html, visible=False)
+                        loading_icon = gr.HTML(loading_icon_html, visible=False)
+                        share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
+            gr.Examples(examples=self.example_inputs,
+                        inputs=[singer_l, inp_text, inp_note, inp_duration],
+                        outputs=[singing_output, share_button, community_icon, loading_icon],
+                        fn=self.greet,
+                        cache_examples=True)
+            gr.Markdown(self.article)
+            generate.click(self.greet,
+                               inputs=[singer_l, inp_text, inp_note, inp_duration],
+                               outputs=[singing_output, share_button, community_icon, loading_icon],)
+        demo.queue().launch(share=True)
+
+
+if __name__ == '__main__':
+    gradio_config = yaml.safe_load(open('inference/m4singer/gradio/gradio_settings.yaml'))
+    g = GradioInfer(**gradio_config)
+    g.run()