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import gradio as gr
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# Load Whisper model and processor from Hugging Face
processor = WhisperProcessor.from_pretrained("openai/whisper-base")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").to("cuda" if torch.cuda.is_available() else "cpu")
def transcribe(audio):
try:
# Load audio
audio_input = processor(audio, sampling_rate=16000, return_tensors="pt")
# Move to appropriate device
audio_input = audio_input.input_features.to(model.device)
# Generate transcription
predicted_ids = model.generate(audio_input)
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
return transcription
except Exception as e:
return f"Error: {str(e)}"
def convert_to_wav(audio_file_path):
try:
wav_file_path = os.path.splitext(audio_file_path)[0] + '.wav'
audio = AudioSegment.from_file(audio_file_path)
audio.export(wav_file_path, format='wav')
logging.info(f'Converted {audio_file_path} to {wav_file_path}')
return wav_file_path
except Exception as e:
logging.error(f'Error converting file to WAV: {e}')
raise
# Create a Gradio interface
iface = gr.Interface(
fn=transcribe,
inputs=gr.Audio(type="filepath"),
outputs="text",
title="Whisper Transcription",
description="Upload an audio file and get the transcription using Whisper model."
)
if __name__ == "__main__":
iface.launch()
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