Update app.py

app.py

@@ -1,6 +1,3 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import IPython.display as ipd
 import os
 import json
 import math
@@ -8,39 +5,68 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
+from scipy.io.wavfile import write
+import numpy as np
+
+import gradio as gr
+import IPython.display as ipd
+
 import commons
 import utils
 from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
 from models import SynthesizerTrn
 from text.symbols import symbols
 from text import text_to_sequence
-from scipy.io.wavfile import write
-import numpy as np
 
-# 加载情感字典
-emotion_dict = json.load(open("configs/leo.json", "r"))
 
-# 加载预训练模型
+def get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+
+def get_text_byroma(text, hps):
+    text_norm = []
+    for i in text:
+        text_norm.append(symbols.index(i))
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+
 hps = utils.get_hparams_from_file("./configs/leo.json")
-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)
+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
+)
 _ = net_g.eval()
+
 _ = utils.load_checkpoint("logs/leo/G_4000.pth", net_g, None)
 
-# 定义文本转语音函数
+# 随机抽取情感参考音频的根目录
+random_emotion_root = "wavs"
+emotion_dict = json.load(open("configs/leo.json", "r"))
+
+
 def tts(txt, emotion, roma=False, length_scale=1):
+    """emotion为参考情感音频路径 或random_sample（随机抽取）"""
     if roma:
         stn_tst = get_text_byroma(txt, hps)
     else:
         stn_tst = get_text(txt, hps)
-    
     with torch.no_grad():
         x_tst = stn_tst.unsqueeze(0)
         x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
         sid = torch.LongTensor([0])
-        
-        if emotion == "random_sample":
-            # 随机选择一个情感参考音频
-            random_emotion_root = "wavs"
+        if os.path.exists(f"{emotion}.emo.npy"):
+            emo = torch.FloatTensor(np.load(f"{emotion}.emo.npy")).unsqueeze(0)
+        elif emotion == "random_sample":
             while True:
                 rand_wav = random.sample(os.listdir(random_emotion_root), 1)[0]
                 if rand_wav.endswith('wav') and os.path.exists(f"{random_emotion_root}/{rand_wav}.emo.npy"):
@@ -48,27 +74,29 @@ def tts(txt, emotion, roma=False, length_scale=1):
             emo = torch.FloatTensor(np.load(f"{random_emotion_root}/{rand_wav}.emo.npy")).unsqueeze(0)
             print(f"{random_emotion_root}/{rand_wav}")
         elif emotion.endswith("wav"):
-            # 从提供的音频中提取情感特征
             import emotion_extract
             emo = torch.FloatTensor(emotion_extract.extract_wav(emotion))
         else:
             print("emotion参数不正确")
-    
-        audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1.2, emo=emo)[0][0, 0].data.float().numpy()
-    
+
+        audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1.2, emo=emo)[0][0,0].data.float().numpy()
     ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))
 
-# 创建GUI界面
-def run_tts(text, emotion, roma=False):
+
+def run_tts(text, emotion, roma):
     tts(text, emotion, roma)
 
-inputs = [
-    gr.inputs.Textbox(label="请输入文本"),
-    gr.inputs.Textbox(label="请输入参考音频路径或选择'random_sample'随机选择"),
-    gr.inputs.Checkbox(label="是否使用音素合成")
-]
 
-outputs = gr.outputs.Audio(type="numpy",label="合成音频")
+iface = gr.Interface(
+    fn=run_tts,
+    inputs=["text", "text", "checkbox"],
+    outputs="audio",
+    layout="vertical",
+    title="TTS Demo",
+    description="Generative TTS Demo with Emotional Control",
+    allow_flagging=False,
+    theme="huggingface",
+    flagging_dir="flagged",
+)
 
-interface = gr.Interface(fn=run_tts, inputs=inputs, outputs=outputs, title="中文文本转语音")
-interface.launch()
+iface.launch(inline=True)