Create app4

app4

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+import gradio as gr
+import base64
+import numpy as np
+from scipy.io import wavfile
+from voice_processing import tts, get_model_names, voice_mapping
+from io import BytesIO
+import asyncio
+from pydub import AudioSegment
+
+async def convert_tts(model_name, tts_text, selected_voice, slang_rate, use_uploaded_voice, voice_upload):
+    edge_tts_voice = voice_mapping.get(selected_voice)
+    if not edge_tts_voice:
+        return {"error": f"Invalid voice '{selected_voice}'."}, None
+
+    voice_upload_file = None
+    if use_uploaded_voice and voice_upload is not None:
+        with open(voice_upload.name, 'rb') as f:
+            voice_upload_file = f.read()
+
+    # Process the text input or uploaded voice
+    info, edge_tts_output_path, tts_output_data, edge_output_file = await tts(
+        model_name, tts_text, edge_tts_voice, slang_rate, use_uploaded_voice, voice_upload_file
+    )
+
+    _, audio_output = tts_output_data
+
+    # Return audio data as bytes
+    audio_bytes = None
+    if isinstance(audio_output, np.ndarray):
+        byte_io = BytesIO()
+        wavfile.write(byte_io, 40000, audio_output)
+        byte_io.seek(0)
+        audio_segment = AudioSegment.from_wav(byte_io)
+        mp3_bytes = audio_segment.export(format="mp3").read()
+        audio_bytes = mp3_bytes
+    else:
+        audio_segment = AudioSegment.from_file(BytesIO(audio_output), format="wav")
+        mp3_bytes = audio_segment.export(format="mp3").read()
+        audio_bytes = mp3_bytes
+
+    return audio_bytes
+
+def get_models():
+    return get_model_names()
+
+def get_voices():
+    return list(voice_mapping.keys())
+
+iface = gr.Interface(
+    fn=convert_tts,
+    inputs=[
+        gr.Dropdown(choices=get_models(), label="Model", interactive=True),
+        gr.Textbox(label="Text", placeholder="Enter text here"),
+        gr.Dropdown(choices=get_voices(), label="Voice", interactive=True),
+        gr.Slider(minimum=0, maximum=1, step=0.01, label="Slang Rate"),
+        gr.Checkbox(label="Use Uploaded Voice"),
+        gr.File(label="Voice File")
+    ],
+    outputs=[
+        gr.Audio(label="Result Audio")
+
+        
+    ],
+    title="Text-to-Speech Conversion"
+)
+
+iface.launch(concurrency_limit=2)