Upload 4 files

apppy.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import edge_tts
+import asyncio
+import tempfile
+import os
+
+async def get_voices():
+    voices = await edge_tts.list_voices()
+    return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}
+
+async def text_to_speech(text, voice, rate, volume, pitch):
+    if not text.strip():
+        return None, "Please enter text to convert."
+    if not voice:
+        return None, "Please select a voice."
+    
+    voice_short_name = voice.split(" - ")[0]
+    rate_str = f"{rate:+d}%"
+    volume_str = f"{volume:+d}%"
+    pitch_str = f"{pitch:+d}Hz"
+    communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, volume=volume_str, pitch=pitch_str)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    return tmp_path, None
+
+async def tts_interface(text, voice, rate, volume, pitch):
+    audio, warning = await text_to_speech(text, voice, rate, volume, pitch)
+    if warning:
+        return audio, gr.Warning(warning)
+    return audio, None
+
+async def create_demo():
+    voices = await get_voices()
+    
+    description = """
+    Convert text to speech using Microsoft Edge TTS. Adjust speech rate and pitch: 0 is default, positive values increase, negative values decrease.
+    
+    🎥 **Exciting News: Introducing our Text-to-Video Converter!** 🎥
+    
+    Take your content creation to the next level with our cutting-edge Text-to-Video Converter! 
+    Transform your words into stunning, professional-quality videos in just a few clicks. 
+    
+    ✨ Features:
+    • Convert text to engaging videos with customizable visuals
+    • Choose from 40+ languages and 300+ voices
+    • Perfect for creating audiobooks, storytelling, and language learning materials
+    • Ideal for educators, content creators, and language enthusiasts
+    """
+    
+    demo = gr.Interface(
+        fn=tts_interface,
+        inputs=[
+            gr.Textbox(label="Input Text", lines=5),
+            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Voice", value=""),
+            gr.Slider(minimum=-50, maximum=50, value=0, label="Speech Rate Adjustment (%)", step=1),
+            gr.Slider(minimum=-50, maximum=50, value=0, label="Speech Volume Adjustment (%)", step=1),
+            gr.Slider(minimum=-20, maximum=20, value=0, label="Pitch Adjustment (Hz)", step=1)
+        ],
+        outputs=[
+            gr.Audio(label="Generated Audio", type="filepath"),
+            gr.Markdown(label="Warning", visible=False)
+        ],
+        title="Edge TTS Text-to-Speech",
+        description=description,
+        article="Experience the power of Edge TTS for text-to-speech conversion, and explore our advanced Text-to-Video Converter for even more creative possibilities!",
+        analytics_enabled=False,
+        allow_flagging="manual",
+        api_name=None
+    )
+    return demo
+
+async def main():
+    demo = await create_demo()
+    demo.queue(default_concurrency_limit=5)
+    demo.launch(show_api=False)
+
+if __name__ == "__main__":
+    asyncio.run(main())

apptts1.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Load a modern TTS pipeline (Microsoft SpeechT5 for balance of speed/quality)
+# In 2025, ensure you have the latest 'transformers' and 'torch' installed.
+synthesizer = pipeline("text-to-speech", model="microsoft/speecht5_tts")
+
+# For SpeechT5, we also need speaker embeddings
+from datasets import load_dataset
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+def generate_speech(text):
+    if not text:
+        return None
+    speech = synthesizer(text, forward_params={"speaker_embeddings": speaker_embedding})
+    # Returns a tuple of (sample_rate, audio_numpy_array)
+    return (speech["sampling_rate"], speech["audio"])
+
+# Build the Interface
+demo = gr.Interface(
+    fn=generate_speech,
+    inputs=gr.Textbox(label="Enter Text", placeholder="Type something to hear it..."),
+    outputs=gr.Audio(label="Generated Audio"),
+    title="2025 Gradio TTS Demo",
+    description="A simple text-to-speech demo using Microsoft SpeechT5."
+)
+
+if __name__ == "__main__":
+    demo.launch()

apptts2.py

@@ -0,0 +1,17 @@
+import gradio as gr
+# Note: Requires F5-TTS package (pip install f5-tts)
+from f5_tts.infer.utils_infer import infer_process
+
+def tts_f5(text):
+    # This is a simplified representation; F5-TTS usually requires a reference audio for cloning
+    # Official Gradio apps for F5-TTS often use 'f5-tts_infer-gradio' command directly
+    audio_path = "output.wav" 
+    # Logic to process text -> audio file
+    return audio_path
+
+demo = gr.Interface(
+    fn=tts_f5,
+    inputs="text",
+    outputs="audio",
+    title="F5-TTS Advanced Demo"
+)

requirements.txt

@@ -0,0 +1,5 @@
+edge-tts
+torch
+transformers
+datasets
+gradio-client