Update README.md

import gradio as gr
from transformers import pipeline

# Load the Whisper model
whisper = pipeline("automatic-speech-recognition", model="openai/whisper-base")

# Define the function for speech-to-text
def transcribe_audio(audio):
if audio is None:
return ""
transcription = whisper(audio)["text"]
return transcription

# Create a Gradio interface with microphone and file upload options
iface = gr.Interface(
fn=transcribe_audio,
inputs=[
gr.Audio(sources=["microphone", "upload"], type="filepath", label="Record or Upload Audio")
],
outputs=gr.Textbox(label="Transcription", placeholder="Transcribed text will appear here..."),
title="Speech-to-Text Transcription",
description="Record audio using your microphone or upload an audio file to transcribe it.",
live=True, # Automatically transcribe when audio is provided
)

# Launch the app
if __name__ == "__main__":
iface.launch()

README.md

@@ -1,17 +1,11 @@
 ---
-title: OpenAI's Whisper Real-time Demo
+title: vepp-stt
 emoji: 🎙️
 colorFrom: indigo
 colorTo: red
 sdk: gradio
-sdk_version: 3.3.1
+sdk_version: 5.12.0
 app_file: app.py
 pinned: false
 license: mit
----
-
-OpenAI's Whisper Real-time Demo
-
-A simple demo of OpenAI's [**Whisper**](https://github.com/openai/whisper) speech recognition model.
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---