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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; }"
) |