Upload app.py

app.py

@@ -0,0 +1,139 @@
+# coding=utf-8
+import time
+import os
+import gradio as gr
+import utils
+import argparse
+import commons
+from models import SynthesizerTrn
+from text import text_to_sequence
+import torch
+from torch import no_grad, LongTensor
+import webbrowser
+import logging
+logging.getLogger('numba').setLevel(logging.WARNING)
+limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
+
+def get_text(text, hps):
+    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm, clean_text
+
+def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
+    start = time.perf_counter()
+    if not len(text):
+        return "输入文本不能为空！", None, None
+    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
+    if len(text) > 100 and limitation:
+        return f"输入文字过长！{len(text)}>100", None, None
+    if language == 0:
+        text = f"[ZH]{text}[ZH]"
+    elif language == 1:
+        text = f"[JA]{text}[JA]"
+    else:
+        text = f"{text}"
+    stn_tst, clean_text = get_text(text, hps_ms)
+    with no_grad():
+        x_tst = stn_tst.unsqueeze(0).to(device)
+        x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+        speaker_id = LongTensor([speaker_id]).to(device)
+        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
+                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
+
+    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
+
+def search_speaker(search_value):
+    for s in speakers:
+        if search_value == s:
+            return s
+    for s in speakers:
+        if search_value in s:
+            return s
+
+def change_lang(language):
+    if language == 0:
+        return 0.6, 0.668, 1.2
+    else:
+        return 0.6, 0.668, 1.1
+
+download_audio_js = """
+() =>{{
+    let root = document.querySelector("body > gradio-app");
+    if (root.shadowRoot != null)
+        root = root.shadowRoot;
+    let audio = root.querySelector("#tts-audio").querySelector("audio");
+    let text = root.querySelector("#input-text").querySelector("textarea");
+    if (audio == undefined)
+        return;
+    text = text.value;
+    if (text == undefined)
+        text = Math.floor(Math.random()*100000000);
+    audio = audio.src;
+    let oA = document.createElement("a");
+    oA.download = text.substr(0, 20)+'.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('--api', action="store_true", default=False)
+    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
+    parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
+    args = parser.parse_args()
+    device = torch.device(args.device)
+    
+    hps_ms = utils.get_hparams_from_file(r'./model/config.json')
+    net_g_ms = SynthesizerTrn(
+        len(hps_ms.symbols),
+        hps_ms.data.filter_length // 2 + 1,
+        hps_ms.train.segment_size // hps_ms.data.hop_length,
+        n_speakers=hps_ms.data.n_speakers,
+        **hps_ms.model)
+    _ = net_g_ms.eval().to(device)
+    speakers = hps_ms.speakers
+    model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
+    
+    with gr.Blocks() as app:
+        gr.Markdown(
+            "<div align='center'>JSX</div>"
+            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
+            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
+        )
+
+        with gr.Tabs():
+            with gr.TabItem("vits"):
+                with gr.Row():
+                    with gr.Column():
+                        input_text = gr.Textbox(label="Text (100 words limitation) " if limitation else "Text", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
+                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
+                                    type="index", value="中文")
+                        btn = gr.Button(value="Submit")
+                        with gr.Row():
+                            search = gr.Textbox(label="Search Speaker", lines=1)
+                            btn2 = gr.Button(value="Search")
+                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
+                        with gr.Row():
+                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
+                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
+                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
+                    with gr.Column():
+                        o1 = gr.Textbox(label="Output Message")
+                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
+                        o3 = gr.Textbox(label="Extra Info")
+                        download = gr.Button("Download Audio")
+                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3])
+                    download.click(None, [], [], _js=download_audio_js.format())
+                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
+                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
+            with gr.TabItem("可用人物一览"):
+                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
+    if args.colab:
+        webbrowser.open("http://127.0.0.1:7860")
+    app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)