audio_processing.py

import torch
import whisper
import numpy as np
import torchaudio as ta
import gradio as gr
from model_utils import get_processor, get_model, get_whisper_model_small, get_device
from config import SAMPLING_RATE, CHUNK_LENGTH_S
import subprocess

def resample_with_ffmpeg(input_file, output_file, target_sr=16000):
    command = [
        'ffmpeg', '-i', input_file, '-ar', str(target_sr), output_file
    ]
    subprocess.run(command, check=True)

def detect_language(audio):
    whisper_model = get_whisper_model_small()

    # Save the input audio to a temporary file
    ta.save("input_audio.wav", torch.tensor(audio[1]).unsqueeze(0), audio[0])

    # Resample if necessary using ffmpeg
    if audio[0] != SAMPLING_RATE:
        resample_with_ffmpeg("input_audio.wav", "resampled_audio.wav", target_sr=SAMPLING_RATE)
        audio_tensor, _ = ta.load("resampled_audio.wav")
    else:
        audio_tensor = torch.tensor(audio[1]).float()

    # Ensure the audio is in the correct shape (mono)
    if audio_tensor.dim() == 2:
        audio_tensor = audio_tensor.mean(dim=0)

    # Use Whisper's preprocessing
    audio_tensor = whisper.pad_or_trim(audio_tensor)
    print(f"Audio length after pad/trim: {audio_tensor.shape[-1] / SAMPLING_RATE} seconds")
    mel = whisper.log_mel_spectrogram(audio_tensor).to(whisper_model.device)

    # Detect language
    _, probs = whisper_model.detect_language(mel)
    detected_lang = max(probs, key=probs.get)

    print(f"Audio shape: {audio_tensor.shape}")
    print(f"Mel spectrogram shape: {mel.shape}")
    print(f"Detected language: {detected_lang}")
    print("Language probabilities:", probs)

    return detected_lang

def process_long_audio(audio, task="transcribe", language=None):
    # ... (rest of the function remains the same)

def process_audio(audio):
    if audio is None:
        return "No file uploaded", "", ""

    detected_lang = detect_language(audio)
    transcription = process_long_audio(audio, task="transcribe")
    translation = process_long_audio(audio, task="translate", language=detected_lang)

    return detected_lang, transcription, translation

# Gradio interface
iface = gr.Interface(
    fn=process_audio,
    inputs=gr.Audio(),
    outputs=[
        gr.Textbox(label="Detected Language"),
        gr.Textbox(label="Transcription", lines=5),
        gr.Textbox(label="Translation", lines=5)
    ],
    title="Audio Transcription and Translation",
    description="Upload an audio file to detect its language, transcribe, and translate it.",
    allow_flagging="never",
    css=".output-textbox { font-family: 'Noto Sans Devanagari', sans-serif; font-size: 18px; }"
)