Update app.py

app.py

@@ -1,3 +1,193 @@
+# import gradio as gr
+
+# gr.Interface.load("models/ulysses115/pmvoice").launch()
+
+import argparse
+import json
+import os
+import re
+import tempfile
+
+import librosa
+import numpy as np
+import torch
+from torch import no_grad, LongTensor
+import commons
+import utils
 import gradio as gr
+import gradio.utils as gr_utils
+import gradio.processing_utils as gr_processing_utils
+from models import SynthesizerTrn
+from text import text_to_sequence, _clean_text
+from mel_processing import spectrogram_torch
+
+limitation = False#os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
+
+
+def audio_postprocess(self, y):
+    if y is None:
+        return None
+
+    if gr_utils.validate_url(y):
+        file = gr_processing_utils.download_to_file(y, dir=self.temp_dir)
+    elif isinstance(y, tuple):
+        sample_rate, data = y
+        file = tempfile.NamedTemporaryFile(
+            suffix=".wav", dir=self.temp_dir, delete=False
+        )
+        gr_processing_utils.audio_to_file(sample_rate, data, file.name)
+    else:
+        file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir)
+
+    return gr_processing_utils.encode_url_or_file_to_base64(file.name)
+
+
+gr.Audio.postprocess = audio_postprocess
+
+
+def get_text(text, hps, is_symbol):
+    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm
+
+
+def create_tts_fn(model, hps, speaker_ids):
+    def tts_fn(text, speaker, speed, is_symbol):
+        if limitation:
+            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
+            max_len = 150
+            if is_symbol:
+                max_len *= 3
+            if text_len > max_len:
+                return "Error: Text is too long", None
+
+        speaker_id = speaker_ids[speaker]
+        stn_tst = get_text(text, hps, is_symbol)
+        with no_grad():
+            x_tst = stn_tst.unsqueeze(0).to(device)
+            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+            sid = LongTensor([speaker_id]).to(device)
+            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
+                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
+        del stn_tst, x_tst, x_tst_lengths, sid
+        return "Success", (hps.data.sampling_rate, audio)
+
+    return tts_fn
+
+
+def create_to_symbol_fn(hps):
+    def to_symbol_fn(is_symbol_input, input_text, temp_text):
+        return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
+            else (temp_text, temp_text)
+
+    return to_symbol_fn
+
+
+download_audio_js = """
+() =>{{
+    let root = document.querySelector("body > gradio-app");
+    if (root.shadowRoot != null)
+        root = root.shadowRoot;
+    let audio = root.querySelector("#{audio_id}").querySelector("audio");
+    if (audio == undefined)
+        return;
+    audio = audio.src;
+    let oA = document.createElement("a");
+    oA.download = Math.floor(Math.random()*100000000)+'.wav';
+    oA.href = audio;
+    document.body.appendChild(oA);
+    oA.click();
+    oA.remove();
+}}
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu')
+    parser.add_argument("--share", action="store_true", default=True, help="share gradio app")
+    args = parser.parse_args()
+
+    device = torch.device(args.device)
+    models_tts = []
+    with open("saved_model/info.json", "r", encoding="utf-8") as f:
+        models_info = json.load(f)
+    for i, info in models_info.items():
+        name = info["title"]
+        author = info["author"]
+        lang = info["lang"]
+        example = info["example"]
+        config_path = f"saved_model/{i}/config.json"
+        model_path = f"saved_model/{i}/model.pth"
+        cover = info["cover"]
+        cover_path = f"saved_model/{i}/{cover}" if cover else None
+        hps = utils.get_hparams_from_file(config_path)
+        model = SynthesizerTrn(
+            len(hps.symbols),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model)
+        utils.load_checkpoint(model_path, model, None)
+        model.eval().to(device)
+        speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
+        speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
+
+        t = info["type"]
+        if t == "vits":
+            models_tts.append((name, author, cover_path, speakers, lang, example,
+                               hps.symbols, create_tts_fn(model, hps, speaker_ids),
+                               create_to_symbol_fn(hps)))
+
+    app = gr.Blocks()
+
+    with app:
+        for i, (name, author, cover_path, speakers, lang, example, symbols, tts_fn,
+                to_symbol_fn) in enumerate(models_tts):
+            with gr.TabItem(f"model{i}"):
+                with gr.Column():
+                    tts_input1 = gr.TextArea(label="Text (150 words limitation)", value=example,
+                                             elem_id=f"tts-input{i}")
+                    tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
+                                             type="index", value=speakers[0])
+                    tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1)
+                    with gr.Accordion(label="Advanced Options", open=False):
+                        temp_text_var = gr.Variable()
+                        symbol_input = gr.Checkbox(value=False, label="Symbol input")
+                        symbol_list = gr.Dataset(label="Symbol list", components=[tts_input1],
+                                                 samples=[[x] for x in symbols],
+                                                 elem_id=f"symbol-list{i}")
+                        symbol_list_json = gr.Json(value=symbols, visible=False)
+                    tts_submit = gr.Button("Generate", variant="primary")
+                    tts_output1 = gr.Textbox(label="Output Message")
+                    tts_output2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio{i}")
+                    download = gr.Button("Download Audio")
+                    download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{i}"))
 
-gr.Interface.load("models/ulysses115/pmvoice").launch()
+                    tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, symbol_input],
+                                     [tts_output1, tts_output2])
+                    symbol_input.change(to_symbol_fn,
+                                        [symbol_input, tts_input1, temp_text_var],
+                                        [tts_input1, temp_text_var])
+                    symbol_list.click(None, [symbol_list, symbol_list_json], [],
+                                      _js=f"""
+                    (i,symbols) => {{
+                        let root = document.querySelector("body > gradio-app");
+                        if (root.shadowRoot != null)
+                            root = root.shadowRoot;
+                        let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
+                        let startPos = text_input.selectionStart;
+                        let endPos = text_input.selectionEnd;
+                        let oldTxt = text_input.value;
+                        let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
+                        text_input.value = result;
+                        let x = window.scrollX, y = window.scrollY;
+                        text_input.focus();
+                        text_input.selectionStart = startPos + symbols[i].length;
+                        text_input.selectionEnd = startPos + symbols[i].length;
+                        text_input.blur();
+                        window.scrollTo(x, y);
+                        return [];
+                    }}""")
+    app.queue(concurrency_count=1).launch(show_api=True, share=args.share)