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import gradio as gr
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from datasets import load_dataset
import torch
# Load Whisper model and processor
model_name = "openai/whisper-large-v3-turbo"
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
# Load dataset (bigcode/the-stack)
dataset = load_dataset("bigcode/the-stack", data_dir="data/html")
def transcribe(audio):
# Process audio for transcription
audio_input = processor(audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(audio_input).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
# Return the transcription
return transcription[0]
# Gradio interface
iface = gr.Interface(
fn=transcribe,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs="text",
title="Whisper Transcription for Developers",
description="Transcribe developer-related terms using Whisper and bigcode dataset for contextual support."
)
# Launch the Gradio app
iface.launch()
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