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import gradio as gr
import torch
import torchaudio
import numpy as np
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# Model loading function with caching
def load_model():
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = WhisperForConditionalGeneration.from_pretrained("tclin/whisper-large-v3-turbo-atcosim-finetune")
model = model.to(device=device, dtype=torch_dtype)
processor = WhisperProcessor.from_pretrained("tclin/whisper-large-v3-turbo-atcosim-finetune")
return model, processor, device, torch_dtype
# Load model and processor once at startup
model, processor, device, torch_dtype = load_model()
# Define the transcription function
def transcribe_audio(audio_file):
# Check if audio file exists
if audio_file is None:
return "Please upload an audio file"
try:
# Load and preprocess audio
waveform, sample_rate = torchaudio.load(audio_file)
# Resample to 16kHz (required for Whisper models)
if sample_rate != 16000:
resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
waveform = resampler(waveform)
# Convert stereo to mono if needed
if waveform.shape[0] > 1:
waveform = waveform.mean(dim=0, keepdim=True)
# Convert to numpy array
waveform_np = waveform.squeeze().cpu().numpy()
# Process with model
input_features = processor(waveform_np, sampling_rate=16000, return_tensors="pt").input_features
input_features = input_features.to(device=device, dtype=torch_dtype)
generated_ids = model.generate(input_features, max_new_tokens=128)
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return transcription
except Exception as e:
return f"Error processing audio: {str(e)}"
# Create Gradio interface
demo = gr.Interface(
fn=transcribe_audio,
inputs=gr.Audio(type="filepath"),
outputs="text",
title="ATC Speech Transcription",
description="Convert Air Traffic Control (ATC) radio communications to text. Upload your own ATC audio or try the examples below.",
examples=[
["atc-sample-1.wav"],
["atc-sample-2.wav"],
["atc-sample-3.wav"]
],
article="This model is fine-tuned on the ATCOSIM dataset with a 3.73% Word Error Rate on ATC communications. It is specifically optimized for aviation terminology, callsigns, and standard phraseology. Audio should be 16kHz sample rate for best results."
)
# Launch the interface
if __name__ == "__main__":
demo.launch() |