app.py

import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, AutoTokenizer, BartForConditionalGeneration
import torch
import torchaudio  # Replace librosa for faster audio processing

# Load BART tokenizer and model for summarization
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
summarizer = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")

# Load Wav2Vec2 processor and model for transcription
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

# Check if CUDA is available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
summarizer.to(device)

model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
summarizer = torch.quantization.quantize_dynamic(summarizer, {torch.nn.Linear}, dtype=torch.qint8)


def transcribe_and_summarize(audioFile):
    # Load audio using torchaudio
    audio, sampling_rate = torchaudio.load(audioFile)
    
    # Resample audio to 16kHz if necessary
    if sampling_rate != 16000:
        resample_transform = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
        audio = resample_transform(audio)
    audio = audio.squeeze()

    # Process audio in chunks for large files
    chunk_size = int(16000 * 30)  # 10-second chunks
    transcription = ""

    for i in range(0, len(audio), chunk_size):
        chunk = audio[i:i+chunk_size].numpy()
        inputs = processor(chunk, sampling_rate=16000, return_tensors="pt").input_values.to(device)

        # Transcription
        with torch.no_grad():
            logits = model(inputs).logits
            predicted_ids = torch.argmax(logits, dim=-1)
            transcription += processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] + " "

    # Summarization
    inputs = tokenizer(transcription, return_tensors="pt", truncation=True, max_length=1024).to(device)
    
    result = summarizer.generate(
        inputs["input_ids"],
        min_length=10,
        max_length=1024,
        no_repeat_ngram_size=2,
        encoder_no_repeat_ngram_size=2,
        repetition_penalty=2.0,
        num_beams=2,  # Reduced beams for faster inference
        early_stopping=True,
    )
    summary = tokenizer.decode(result[0], skip_special_tokens=True)

    return transcription.strip(), summary.strip()

# Gradio interface
iface = gr.Interface(
    fn=transcribe_and_summarize,
    inputs=gr.Audio(type="filepath", label="Upload Audio"),
    outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summary")], 
    title="Audio Transcription and Summarization",
    description="Transcribe and summarize audio using Wav2Vec2 and BART.",
)

iface.launch()