app.py

import os
import re
import time
import random
import numpy as np
import math
import shutil
# import base64 # Not directly needed for Gradio filepath output

# Torch and Audio
import torch
import torch.nn as nn
# import torch.optim as optim # Not needed for inference
# from torch.utils.data import Dataset, DataLoader # Not needed for inference
import torch.nn.functional as F
import torchaudio
import librosa
# import librosa.display # Not used in pipeline

# Text and Audio Processing
from unidecode import unidecode
# from inflect import engine # Not explicitly used in pipeline, consider removing
# import pydub # Not explicitly used in pipeline, consider removing
import soundfile as sf

# Transformers
from transformers import (
    WhisperProcessor, WhisperForConditionalGeneration,
    MarianTokenizer, MarianMTModel,
)
from huggingface_hub import hf_hub_download

# Gradio and Hugging Face Spaces
import gradio as gr
import spaces # <<< --- ADD THIS IMPORT --- <<<

# --- Global Configuration & Device Setup ---
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"--- Initializing on device: {DEVICE} ---") # This will run when the Space builds/starts

# --- Part 1: TTS Model Components (Your Custom TTS) ---
# ... (Keep all your Hyperparams, text_to_seq, audio processing for TTS, and Model class definitions:
# EncoderBlock, DecoderBlock, EncoderPreNet, PostNet, DecoderPreNet, TransformerTTS)
# ... (Ensure TransformerTTS and its sub-modules are correctly defined as in your previous code)
# --- (Start of your model definitions - make sure this is complete from your previous code) ---
class Hyperparams:
  seed = 42
  # We won't use these dataset paths, but keep them for hp object integrity
  csv_path = "path/to/metadata.csv"
  wav_path = "path/to/wavs"
  symbols = [
    'EOS', ' ', '!', ',', '-', '.', ';', '?', 'a', 'b', 'c', 'd', 'e', 'f', 
    'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 
    't', 'u', 'v', 'w', 'x', 'y', 'z', 'à', 'â', 'è', 'é', 'ê', 'ü', 
    '’', '“', '”' 
  ]
  sr = 22050
  n_fft = 2048
  n_stft = int((n_fft//2) + 1)
  hop_length = int(n_fft/8.0)
  win_length = int(n_fft/2.0)
  mel_freq = 128
  max_mel_time = 1024
  power = 2.0
  text_num_embeddings = 2*len(symbols)  
  embedding_size = 256
  encoder_embedding_size = 512 
  dim_feedforward = 1024
  postnet_embedding_size = 1024
  encoder_kernel_size = 3
  postnet_kernel_size = 5
  ampl_multiplier = 10.0
  ampl_amin = 1e-10
  db_multiplier = 1.0
  ampl_ref = 1.0
  ampl_power = 1.0
  max_db = 100
  scale_db = 10

hp = Hyperparams()

# Text to Sequence
symbol_to_id = {s: i for i, s in enumerate(hp.symbols)}
def text_to_seq(text):
  text = text.lower()
  seq = []
  for s in text:
    _id = symbol_to_id.get(s, None)
    if _id is not None:
      seq.append(_id)
  seq.append(symbol_to_id["EOS"])
  return torch.IntTensor(seq)

# Audio Processing
spec_transform = torchaudio.transforms.Spectrogram(n_fft=hp.n_fft, win_length=hp.win_length, hop_length=hp.hop_length, power=hp.power)
mel_scale_transform = torchaudio.transforms.MelScale(n_mels=hp.mel_freq, sample_rate=hp.sr, n_stft=hp.n_stft)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
mel_inverse_transform = torchaudio.transforms.InverseMelScale(n_mels=hp.mel_freq, sample_rate=hp.sr, n_stft=hp.n_stft).to(DEVICE)
griffnlim_transform = torchaudio.transforms.GriffinLim(n_fft=hp.n_fft, win_length=hp.win_length, hop_length=hp.hop_length).to(DEVICE)

def pow_to_db_mel_spec(mel_spec):
  mel_spec = torchaudio.functional.amplitude_to_DB(mel_spec, multiplier=hp.ampl_multiplier, amin=hp.ampl_amin, db_multiplier=hp.db_multiplier, top_db=hp.max_db)
  mel_spec = mel_spec/hp.scale_db
  return mel_spec

def db_to_power_mel_spec(mel_spec):
  mel_spec = mel_spec*hp.scale_db
  mel_spec = torchaudio.functional.DB_to_amplitude(mel_spec, ref=hp.ampl_ref, power=hp.ampl_power)  
  return mel_spec

def inverse_mel_spec_to_wav(mel_spec):
  power_mel_spec = db_to_power_mel_spec(mel_spec.to(DEVICE))
  spectrogram = mel_inverse_transform(power_mel_spec)
  pseudo_wav = griffnlim_transform(spectrogram)
  return pseudo_wav

def mask_from_seq_lengths(sequence_lengths: torch.Tensor, max_length: int) -> torch.BoolTensor:
    ones = sequence_lengths.new_ones(sequence_lengths.size(0), max_length)
    range_tensor = ones.cumsum(dim=1)
    return sequence_lengths.unsqueeze(1) >= range_tensor
    
# --- TransformerTTS Model Architecture (Copied from notebook)
class EncoderBlock(nn.Module):
    def __init__(self):
        super(EncoderBlock, self).__init__()
        self.norm_1 = nn.LayerNorm(normalized_shape=hp.embedding_size)
        self.attn = torch.nn.MultiheadAttention(embed_dim=hp.embedding_size, num_heads=4, dropout=0.1, batch_first=True)
        self.dropout_1 = torch.nn.Dropout(0.1)
        self.norm_2 = nn.LayerNorm(normalized_shape=hp.embedding_size)
        self.linear_1 = nn.Linear(hp.embedding_size, hp.dim_feedforward)
        self.dropout_2 = torch.nn.Dropout(0.1)
        self.linear_2 = nn.Linear(hp.dim_feedforward, hp.embedding_size)
        self.dropout_3 = torch.nn.Dropout(0.1)
    def forward(self, x, attn_mask=None, key_padding_mask=None):
        x_out = self.norm_1(x)
        x_out, _ = self.attn(query=x_out, key=x_out, value=x_out, attn_mask=attn_mask, key_padding_mask=key_padding_mask)
        x_out = self.dropout_1(x_out)
        x = x + x_out    
        x_out = self.norm_2(x) 
        x_out = self.linear_1(x_out)
        x_out = F.relu(x_out)
        x_out = self.dropout_2(x_out)
        x_out = self.linear_2(x_out)
        x_out = self.dropout_3(x_out)
        x = x + x_out
        return x

class DecoderBlock(nn.Module):
    def __init__(self):
        super(DecoderBlock, self).__init__()
        self.norm_1 = nn.LayerNorm(normalized_shape=hp.embedding_size)
        self.self_attn = torch.nn.MultiheadAttention(embed_dim=hp.embedding_size, num_heads=4, dropout=0.1, batch_first=True)
        self.dropout_1 = torch.nn.Dropout(0.1)
        self.norm_2 = nn.LayerNorm(normalized_shape=hp.embedding_size)
        self.attn = torch.nn.MultiheadAttention(embed_dim=hp.embedding_size, num_heads=4, dropout=0.1, batch_first=True)    
        self.dropout_2 = torch.nn.Dropout(0.1)
        self.norm_3 = nn.LayerNorm(normalized_shape=hp.embedding_size)
        self.linear_1 = nn.Linear(hp.embedding_size, hp.dim_feedforward)
        self.dropout_3 = torch.nn.Dropout(0.1)
        self.linear_2 = nn.Linear(hp.dim_feedforward, hp.embedding_size)
        self.dropout_4 = torch.nn.Dropout(0.1)
    def forward(self, x, memory, x_attn_mask=None, x_key_padding_mask=None, memory_attn_mask=None, memory_key_padding_mask=None):
        x_out, _ = self.self_attn(query=x, key=x, value=x, attn_mask=x_attn_mask, key_padding_mask=x_key_padding_mask)
        x_out = self.dropout_1(x_out)
        x = self.norm_1(x + x_out)
        x_out, _ = self.attn(query=x, key=memory, value=memory, attn_mask=memory_attn_mask, key_padding_mask=memory_key_padding_mask)
        x_out = self.dropout_2(x_out)
        x = self.norm_2(x + x_out)
        x_out = self.linear_1(x)
        x_out = F.relu(x_out)
        x_out = self.dropout_3(x_out)
        x_out = self.linear_2(x_out)
        x_out = self.dropout_4(x_out)
        x = self.norm_3(x + x_out)
        return x

class EncoderPreNet(nn.Module):
    def __init__(self):
        super(EncoderPreNet, self).__init__()
        self.embedding = nn.Embedding(num_embeddings=hp.text_num_embeddings, embedding_dim=hp.encoder_embedding_size)
        self.linear_1 = nn.Linear(hp.encoder_embedding_size, hp.encoder_embedding_size)
        self.linear_2 = nn.Linear(hp.encoder_embedding_size, hp.embedding_size)
        self.conv_1 = nn.Conv1d(hp.encoder_embedding_size, hp.encoder_embedding_size, kernel_size=hp.encoder_kernel_size, stride=1, padding=int((hp.encoder_kernel_size - 1) / 2), dilation=1)
        self.bn_1 = nn.BatchNorm1d(hp.encoder_embedding_size)
        self.dropout_1 = torch.nn.Dropout(0.5)
        self.conv_2 = nn.Conv1d(hp.encoder_embedding_size, hp.encoder_embedding_size, kernel_size=hp.encoder_kernel_size, stride=1, padding=int((hp.encoder_kernel_size - 1) / 2), dilation=1)
        self.bn_2 = nn.BatchNorm1d(hp.encoder_embedding_size)
        self.dropout_2 = torch.nn.Dropout(0.5)
        self.conv_3 = nn.Conv1d(hp.encoder_embedding_size, hp.encoder_embedding_size, kernel_size=hp.encoder_kernel_size, stride=1, padding=int((hp.encoder_kernel_size - 1) / 2), dilation=1)
        self.bn_3 = nn.BatchNorm1d(hp.encoder_embedding_size)
        self.dropout_3 = torch.nn.Dropout(0.5)    
    def forward(self, text):
        x = self.embedding(text)
        x = self.linear_1(x)
        x = x.transpose(2, 1)
        x = self.conv_1(x)
        x = self.bn_1(x)
        x = F.relu(x)
        x = self.dropout_1(x)
        x = self.conv_2(x)
        x = self.bn_2(x)
        x = F.relu(x)
        x = self.dropout_2(x)
        x = self.conv_3(x)
        x = self.bn_3(x)    
        x = F.relu(x)
        x = self.dropout_3(x)
        x = x.transpose(1, 2)
        x = self.linear_2(x)
        return x

class PostNet(nn.Module):
    def __init__(self):
        super(PostNet, self).__init__()
        self.conv_1 = nn.Conv1d(hp.mel_freq, hp.postnet_embedding_size, kernel_size=hp.postnet_kernel_size, stride=1, padding=int((hp.postnet_kernel_size - 1) / 2), dilation=1)
        self.bn_1 = nn.BatchNorm1d(hp.postnet_embedding_size)
        self.dropout_1 = torch.nn.Dropout(0.5)
        self.conv_2 = nn.Conv1d(hp.postnet_embedding_size, hp.postnet_embedding_size, kernel_size=hp.postnet_kernel_size, stride=1, padding=int((hp.postnet_kernel_size - 1) / 2), dilation=1)
        self.bn_2 = nn.BatchNorm1d(hp.postnet_embedding_size)
        self.dropout_2 = torch.nn.Dropout(0.5)
        self.conv_3 = nn.Conv1d(hp.postnet_embedding_size, hp.postnet_embedding_size, kernel_size=hp.postnet_kernel_size, stride=1, padding=int((hp.postnet_kernel_size - 1) / 2), dilation=1)
        self.bn_3 = nn.BatchNorm1d(hp.postnet_embedding_size)
        self.dropout_3 = torch.nn.Dropout(0.5)
        self.conv_4 = nn.Conv1d(hp.postnet_embedding_size, hp.postnet_embedding_size, kernel_size=hp.postnet_kernel_size, stride=1, padding=int((hp.postnet_kernel_size - 1) / 2), dilation=1)
        self.bn_4 = nn.BatchNorm1d(hp.postnet_embedding_size)
        self.dropout_4 = torch.nn.Dropout(0.5)
        self.conv_5 = nn.Conv1d(hp.postnet_embedding_size, hp.postnet_embedding_size, kernel_size=hp.postnet_kernel_size, stride=1, padding=int((hp.postnet_kernel_size - 1) / 2), dilation=1)
        self.bn_5 = nn.BatchNorm1d(hp.postnet_embedding_size)
        self.dropout_5 = torch.nn.Dropout(0.5)
        self.conv_6 = nn.Conv1d(hp.postnet_embedding_size, hp.mel_freq, kernel_size=hp.postnet_kernel_size, stride=1, padding=int((hp.postnet_kernel_size - 1) / 2), dilation=1)
        self.bn_6 = nn.BatchNorm1d(hp.mel_freq)
        self.dropout_6 = torch.nn.Dropout(0.5)
    def forward(self, x):
        x = x.transpose(2, 1)
        x = self.conv_1(x)
        x = self.bn_1(x); x = torch.tanh(x); x = self.dropout_1(x)
        x = self.conv_2(x)
        x = self.bn_2(x); x = torch.tanh(x); x = self.dropout_2(x)
        x = self.conv_3(x)
        x = self.bn_3(x); x = torch.tanh(x); x = self.dropout_3(x)
        x = self.conv_4(x)
        x = self.bn_4(x); x = torch.tanh(x); x = self.dropout_4(x)
        x = self.conv_5(x)
        x = self.bn_5(x); x = torch.tanh(x); x = self.dropout_5(x)
        x = self.conv_6(x)
        x = self.bn_6(x); x = self.dropout_6(x)
        x = x.transpose(1, 2)
        return x

class DecoderPreNet(nn.Module):
    def __init__(self):
        super(DecoderPreNet, self).__init__()
        self.linear_1 = nn.Linear(hp.mel_freq, hp.embedding_size)
        self.linear_2 = nn.Linear(hp.embedding_size, hp.embedding_size)
    def forward(self, x):
        x = self.linear_1(x)
        x = F.relu(x)
        x = F.dropout(x, p=0.5, training=True)
        x = self.linear_2(x)
        x = F.relu(x)    
        x = F.dropout(x, p=0.5, training=True)
        return x    

class TransformerTTS(nn.Module):
    def __init__(self, device=DEVICE):
        super(TransformerTTS, self).__init__()
        self.encoder_prenet = EncoderPreNet()
        self.decoder_prenet = DecoderPreNet()
        self.postnet = PostNet()
        self.pos_encoding = nn.Embedding(num_embeddings=hp.max_mel_time, embedding_dim=hp.embedding_size)
        self.encoder_block_1 = EncoderBlock()
        self.encoder_block_2 = EncoderBlock()
        self.encoder_block_3 = EncoderBlock()
        self.decoder_block_1 = DecoderBlock()
        self.decoder_block_2 = DecoderBlock()
        self.decoder_block_3 = DecoderBlock()
        self.linear_1 = nn.Linear(hp.embedding_size, hp.mel_freq) 
        self.linear_2 = nn.Linear(hp.embedding_size, 1)
        self.norm_memory = nn.LayerNorm(normalized_shape=hp.embedding_size)
    def forward(self, text, text_len, mel, mel_len):  
        N = text.shape[0]; S = text.shape[1]; TIME = mel.shape[1]
        self.src_key_padding_mask = torch.zeros((N, S), device=text.device).masked_fill(~mask_from_seq_lengths(text_len, max_length=S), float("-inf"))
        self.src_mask = torch.zeros((S, S), device=text.device).masked_fill(torch.triu(torch.full((S, S), True, dtype=torch.bool), diagonal=1).to(text.device), float("-inf"))
        self.tgt_key_padding_mask = torch.zeros((N, TIME), device=mel.device).masked_fill(~mask_from_seq_lengths(mel_len, max_length=TIME), float("-inf"))
        self.tgt_mask = torch.zeros((TIME, TIME), device=mel.device).masked_fill(torch.triu(torch.full((TIME, TIME), True, device=mel.device, dtype=torch.bool), diagonal=1), float("-inf"))
        self.memory_mask = torch.zeros((TIME, S), device=mel.device).masked_fill(torch.triu(torch.full((TIME, S), True, device=mel.device, dtype=torch.bool), diagonal=1), float("-inf"))    
        text_x = self.encoder_prenet(text) 
        pos_codes = self.pos_encoding(torch.arange(hp.max_mel_time).to(mel.device))
        S = text_x.shape[1]; text_x = text_x + pos_codes[:S]
        text_x = self.encoder_block_1(text_x, attn_mask = self.src_mask, key_padding_mask = self.src_key_padding_mask)
        text_x = self.encoder_block_2(text_x, attn_mask = self.src_mask, key_padding_mask = self.src_key_padding_mask)    
        text_x = self.encoder_block_3(text_x, attn_mask = self.src_mask, key_padding_mask = self.src_key_padding_mask)
        text_x = self.norm_memory(text_x)
        mel_x = self.decoder_prenet(mel); mel_x = mel_x + pos_codes[:TIME]
        mel_x = self.decoder_block_1(x=mel_x, memory=text_x, x_attn_mask=self.tgt_mask, x_key_padding_mask=self.tgt_key_padding_mask, memory_attn_mask=self.memory_mask, memory_key_padding_mask=self.src_key_padding_mask)
        mel_x = self.decoder_block_2(x=mel_x, memory=text_x, x_attn_mask=self.tgt_mask, x_key_padding_mask=self.tgt_key_padding_mask, memory_attn_mask=self.memory_mask, memory_key_padding_mask=self.src_key_padding_mask)
        mel_x = self.decoder_block_3(x=mel_x, memory=text_x, x_attn_mask=self.tgt_mask, x_key_padding_mask=self.tgt_key_padding_mask, memory_attn_mask=self.memory_mask, memory_key_padding_mask=self.src_key_padding_mask)
        mel_linear = self.linear_1(mel_x)
        mel_postnet = self.postnet(mel_linear)
        mel_postnet = mel_linear + mel_postnet
        stop_token = self.linear_2(mel_x)
        bool_mel_mask = self.tgt_key_padding_mask.ne(0).unsqueeze(-1).repeat(1, 1, hp.mel_freq)
        mel_linear = mel_linear.masked_fill(bool_mel_mask, 0)
        mel_postnet = mel_postnet.masked_fill(bool_mel_mask, 0)
        stop_token = stop_token.masked_fill(bool_mel_mask[:, :, 0].unsqueeze(-1), 1e3).squeeze(2)
        return mel_postnet, mel_linear, stop_token 

    @torch.no_grad()
    def inference(self, text, max_length=800, stop_token_threshold=0.5, with_tqdm=True):
        self.eval(); self.train(False)
        text_lengths = torch.tensor(text.shape[1]).unsqueeze(0).to(DEVICE)
        N = 1
        SOS = torch.zeros((N, 1, hp.mel_freq), device=DEVICE)
        mel_padded = SOS
        mel_lengths = torch.tensor(1).unsqueeze(0).to(DEVICE)
        stop_token_outputs = torch.FloatTensor([]).to(text.device)
        iters = range(max_length)
        for _ in iters:
            mel_postnet, mel_linear, stop_token = self(text, text_lengths, mel_padded, mel_lengths)
            mel_padded = torch.cat([mel_padded, mel_postnet[:, -1:, :]], dim=1)
            if torch.sigmoid(stop_token[:, -1]) > stop_token_threshold:      
                break
            else:
                stop_token_outputs = torch.cat([stop_token_outputs, stop_token[:, -1:]], dim=1)
                mel_lengths = torch.tensor(mel_padded.shape[1]).unsqueeze(0).to(DEVICE)
        return mel_postnet, stop_token_outputs
# --- (End of your model definitions) ---

# --- Part 2: Model Loading ---
# (Same as before - ensure TTS_MODEL = TransformerTTS(device=DEVICE).to(DEVICE) is used)
TTS_MODEL_HUB_ID = "MoHamdyy/transformer-tts-ljspeech"
ASR_HUB_ID       = "MoHamdyy/whisper-stt-model"
MARIAN_HUB_ID    = "MoHamdyy/marian-ar-en-translation"




# Wrap model loading in a function to clearly see when it happens or to potentially delay it.
# For Spaces, global loading is fine and preferred as it happens once.
print("--- Starting Model Loading ---")
try:
    print("Loading TTS model...")
    # Download the .pt file from its repo
    tts_model_path = hf_hub_download(repo_id=TTS_MODEL_HUB_ID, filename="train_SimpleTransfromerTTS.pt")
    state = torch.load(tts_model_path, map_location=DEVICE)
    TTS_MODEL = TransformerTTS().to(DEVICE)
    # Check for the correct key in the state dictionary
    if "model" in state:
        TTS_MODEL.load_state_dict(state["model"])
    elif "state_dict" in state:
        TTS_MODEL.load_state_dict(state["state_dict"])
    else:
        TTS_MODEL.load_state_dict(state) # Assume the whole file is the state_dict
    TTS_MODEL.eval()
    print("TTS model loaded successfully.")
except Exception as e:
    print(f"Error loading TTS model: {e}")
    TTS_MODEL = None

# Load STT (Whisper) Model from Hub
try:
    print("Loading STT (Whisper) model...")
    stt_processor = WhisperProcessor.from_pretrained(ASR_HUB_ID)
    stt_model = WhisperForConditionalGeneration.from_pretrained(ASR_HUB_ID).to(DEVICE).eval()
    print("STT model loaded successfully.")
except Exception as e:
    print(f"Error loading STT model: {e}")
    stt_processor = None
    stt_model = None

# Load TTT (MarianMT) Model from Hub
try:
    print("Loading TTT (MarianMT) model...")
    mt_tokenizer = MarianTokenizer.from_pretrained(MARIAN_HUB_ID)
    mt_model = MarianMTModel.from_pretrained(MARIAN_HUB_ID).to(DEVICE).eval()
    print("TTT model loaded successfully.")
except Exception as e:
    print(f"Error loading TTT model: {e}")
    mt_tokenizer = None
    mt_model = None
print("--- Model Loading Complete ---")


# --- Part 3: Full Pipeline Function for Gradio ---
@spaces.GPU # For ZeroGPU execution context
def full_speech_translation_pipeline(audio_input_path: str):
    print(f"--- PIPELINE START: Processing {audio_input_path} ---")
    if audio_input_path is None or not os.path.exists(audio_input_path):
        msg = "Error: Audio file not provided or not found."
        print(msg)
        # Return empty/default values
        return "Error: No file", "", (hp.sr, np.array([]).astype(np.float32))

    # STT Stage
    arabic_transcript = "STT Error: Processing failed."
    try:
        print("STT: Loading and resampling audio...")
        wav, sr = torchaudio.load(audio_input_path)
        if wav.size(0) > 1: wav = wav.mean(dim=0, keepdim=True)
        target_sr_stt = stt_processor.feature_extractor.sampling_rate
        if sr != target_sr_stt: wav = torchaudio.transforms.Resample(sr, target_sr_stt)(wav)
        audio_array_stt = wav.squeeze().cpu().numpy()
        
        print("STT: Extracting features and transcribing...")
        inputs = stt_processor(audio_array_stt, sampling_rate=target_sr_stt, return_tensors="pt").input_features.to(DEVICE)
        forced_ids = stt_processor.get_decoder_prompt_ids(language="arabic", task="transcribe")
        with torch.no_grad():
            generated_ids = stt_model.generate(inputs, forced_decoder_ids=forced_ids, max_length=448)
        arabic_transcript = stt_processor.decode(generated_ids[0], skip_special_tokens=True).strip()
        print(f"STT Output: {arabic_transcript}")
    except Exception as e:
        print(f"STT Error: {e}")

    # TTT Stage
    english_translation = "TTT Error: Processing failed."
    if arabic_transcript and not arabic_transcript.startswith("STT Error"):
        try:
            print("TTT: Translating to English...")
            batch = mt_tokenizer(arabic_transcript, return_tensors="pt", padding=True).to(DEVICE)
            with torch.no_grad():
                translated_ids = mt_model.generate(**batch, max_length=512)
            english_translation = mt_tokenizer.batch_decode(translated_ids, skip_special_tokens=True)[0].strip()
            print(f"TTT Output: {english_translation}")
        except Exception as e:
            print(f"TTT Error: {e}")
    else:
        english_translation = "(Skipped TTT due to STT failure)"
        print(english_translation)

    # TTS Stage
    synthesized_audio_np = np.array([]).astype(np.float32)
    if english_translation and not english_translation.startswith("TTT Error"):
        try:
            print("TTS: Synthesizing English speech...")
            sequence = text_to_seq(english_translation).unsqueeze(0).to(DEVICE)
            generated_mel, _ = TTS_MODEL.inference(sequence, max_length=hp.max_mel_time-20, stop_token_threshold=0.5, with_tqdm=False)
            
            print(f"TTS: Generated mel shape: {generated_mel.shape if generated_mel is not None else 'None'}")
            if generated_mel is not None and generated_mel.numel() > 0:
                mel_for_vocoder = generated_mel.detach().squeeze(0).transpose(0, 1)
                audio_tensor = inverse_mel_spec_to_wav(mel_for_vocoder)
                synthesized_audio_np = audio_tensor.cpu().numpy()
                print(f"TTS: Synthesized audio shape: {synthesized_audio_np.shape}")
        except Exception as e:
            print(f"TTS Error: {e}")
    
    print(f"--- PIPELINE END ---")
    return arabic_transcript, english_translation, (hp.sr, synthesized_audio_np)


# --- Part 4: Gradio Interface Definition ---
# (Same as before)
iface = gr.Interface(
    fn=full_speech_translation_pipeline,
    inputs=[
        gr.Audio(type="filepath", label="Upload Arabic Speech")
    ],
    outputs=[
        gr.Textbox(label="Arabic Transcript (STT)"),
        gr.Textbox(label="English Translation (TTT)"),
        gr.Audio(label="Synthesized English Speech (TTS)", type="filepath")
    ],
    title="Arabic to English Speech Translation (ZeroGPU)",
    description="Upload an Arabic audio file. Transcribed to Arabic (Whisper), translated to English (MarianMT), synthesized to English speech (Custom TransformerTTS).",
    allow_flagging="never",
    # examples=[["sample.wav"]] # If you add a sample.wav to your repo
)

# --- Part 5: Launch for Spaces (and local testing) ---
if __name__ == '__main__':
    # Clean up temp audio files from previous local runs
    for f_name in os.listdir("."):
        if f_name.startswith("output_audio_") and f_name.endswith(".wav"):
            try:
                os.remove(f_name)
            except OSError:
                pass # Ignore if file is already gone or locked
    print("Starting Gradio interface locally with debug mode...")
    iface.launch(debug=True, share=False) # share=False for local, Spaces handles public URL