Update app.py

app.py

@@ -1,49 +1,41 @@
 import io
 import os
 import tempfile
-from typing import List, Optional
+from typing import List
 
 import TTS.api
 import torch
 from pydub import AudioSegment
-
-from fastapi import FastAPI, File, Form, UploadFile, HTTPException
-from fastapi.responses import StreamingResponse, Response
+import gradio as gr  # Gradio库
 
 import config
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 models = {}
-
 for id, model in config.models.items():
     models[id] = TTS.api.TTS(model).to(device)
 
-class SynthesizeResponse(Response):
-    media_type = 'audio/wav'
 
-app = FastAPI()
-
-@app.post('/tts', response_class=SynthesizeResponse)
-async def synthesize(
-    text: str = Form('Hello,World!'), 
-    speaker_wavs: List[UploadFile] = File(None), 
-    speaker_idx: str = Form('Ana Florence'), 
-    language: str = Form('ja'), 
-    temperature: float = Form(0.65), 
-    length_penalty: float = Form(1.0), 
-    repetition_penalty: float = Form(2.0), 
-    top_k: int = Form(50), 
-    top_p: float = Form(0.8), 
-    speed: float = Form(1.0), 
-    enable_text_splitting: bool = Form(True)
-) -> StreamingResponse:
+def synthesize_tts(
+    text: str = 'Hello, World!',
+    speaker_wavs: List[gr.File] = None,
+    speaker_idx: str = 'Ana Florence',
+    language: str = 'ja',
+    temperature: float = 0.65,
+    length_penalty: float = 1.0,
+    repetition_penalty: float = 2.0,
+    top_k: int = 50,
+    top_p: float = 0.8,
+    speed: float = 1.0,
+    enable_text_splitting: bool = True,
+):
     temp_files = []
     try:
         if speaker_wavs:
             # Process each uploaded file
             for speaker_wav in speaker_wavs:
-                speaker_wav_bytes = await speaker_wav.read()
+                speaker_wav_bytes = speaker_wav.read()
                 # Convert the uploaded audio file to a WAV format using pydub
                 try:
                     audio = AudioSegment.from_file(io.BytesIO(speaker_wav_bytes))
@@ -51,7 +43,7 @@ async def synthesize(
                     audio.export(wav_buffer, format="wav")
                     wav_buffer.seek(0)  # Reset buffer position to the beginning
                 except Exception as e:
-                    raise HTTPException(status_code=400, detail=f"Error processing audio file: {e}")
+                    return f"Error processing audio file: {e}"
 
                 temp_wav_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
                 temp_wav_file.write(wav_buffer.read())
@@ -87,9 +79,36 @@ async def synthesize(
                 speed=speed,
                 enable_text_splitting=enable_text_splitting
             )
+        
         output_buffer.seek(0)
-        return StreamingResponse(output_buffer, media_type="audio/wav")
+        return output_buffer.read()
+
     finally:
         for temp_file in temp_files:
             if isinstance(temp_file, str) and os.path.exists(temp_file):
-                os.remove(temp_file)
+                os.remove(temp_file)
+
+
+# 创建Gradio界面
+inputs = [
+    gr.Textbox(value="Hello, World!", label="Text to Synthesize"),
+    gr.File(file_types=["audio"], label="Speaker WAV files (optional)", optional=True, multiple=True),
+    gr.Textbox(value="Ana Florence", label="Speaker Index"),
+    gr.Textbox(value="ja", label="Language"),
+    gr.Slider(0, 1, value=0.65, step=0.01, label="Temperature"),
+    gr.Slider(0.5, 2, value=1.0, step=0.1, label="Length Penalty"),
+    gr.Slider(1, 10, value=2.0, step=0.1, label="Repetition Penalty"),
+    gr.Slider(1, 100, value=50, step=1, label="Top-K"),
+    gr.Slider(0, 1, value=0.8, step=0.01, label="Top-P"),
+    gr.Slider(0.5, 2, value=1.0, step=0.01, label="Speed"),
+    gr.Checkbox(value=True, label="Enable Text Splitting")
+]
+
+outputs = gr.Audio(label="Generated Speech")
+
+gr.Interface(
+    fn=synthesize_tts, 
+    inputs=inputs, 
+    outputs=outputs, 
+    title="Text-to-Speech Synthesis with Gradio"
+).launch()