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import gradio as gr
import whisper
from transformers import pipeline
# Load Whisper model
whisper_model = whisper.load_model("small", device="cpu")
# Load the text correction model
correction_pipeline = pipeline("text2text-generation", model="tiiuae/falcon3-1b-instruct", device=-1)
# Function to preprocess audio and transcribe it using Whisper
def transcribe_audio(audio_file):
transcription = whisper_model.transcribe(audio_file)
return transcription["text"]
# Function to correct grammar in text
def chunk_text(text, max_tokens=2000):
"""
Splits the text into smaller chunks to ensure it doesn't exceed the token limit.
"""
words = text.split()
chunks = []
chunk = []
current_tokens = 0
for word in words:
word_tokens = len(word.split()) # Approximate token count
if current_tokens + word_tokens > max_tokens:
chunks.append(" ".join(chunk))
chunk = [word]
current_tokens = word_tokens
else:
chunk.append(word)
current_tokens += word_tokens
if chunk:
chunks.append(" ".join(chunk))
return chunks
# Function to process the pipeline
def process_pipeline(audio_file):
raw_transcription = transcribe_audio(audio_file)
corrected_transcription = correct_text(raw_transcription)
return raw_transcription, corrected_transcription
# Gradio Interface
interface = gr.Interface(
fn=process_pipeline,
inputs=gr.Audio(type="filepath", label="Upload Audio"),
outputs=[
gr.Textbox(label="Raw Transcription"),
gr.Textbox(label="Corrected Transcription"),
],
title="Speech Correction Demo",
description="Upload an audio file to see raw transcription and grammar-corrected output.",
)
# Launch the app
interface.launch(share=True)
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