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import gradio as gr | |
import os | |
import whisper | |
# Load the Whisper model | |
model = whisper.load_model("base") | |
# Function to process the uploaded audio file and perform transcription | |
def process_audio(upload): | |
# Save the uploaded audio file | |
file_path = "uploaded_audio.mp3" | |
upload.save(file_path) | |
# Load the audio file and perform preprocessing | |
audio = whisper.load_audio(file_path) | |
audio = whisper.pad_or_trim(audio) | |
mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
# Detect the spoken language | |
_, probs = model.detect_language(mel) | |
detected_language = max(probs, key=probs.get) | |
# Perform transcription using Whisper ASR | |
options = whisper.DecodingOptions() | |
result = whisper.decode(model, mel, options) | |
transcription = result.text | |
return transcription | |
# Create an audio input component for uploading the audio file | |
audio_input = gr.inputs.Audio(label="Upload Audio", type="file") | |
# Create a text output component for displaying the transcription | |
text_output = gr.outputs.Textbox(label="Transcription") | |
# Create a Gradio interface | |
gr.Interface(fn=process_audio, inputs=audio_input, outputs=text_output, title="Audio Transcription").launch() | |