app.py

# NEW-ASR-VOXLINGUA

# ==============================================================================
# Cell 1: Environment Setup & Dependencies
#
# CORRECTED: Forcing SpeechBrain to version 0.5.16 to ensure backward
# compatibility with the old TalTechNLP XLS-R model.
# ==============================================================================
print("CELL 1: Setting up the environment with specific SpeechBrain version...")

# --- CORE CORRECTION ---
# Uninstall any existing newer versions and install the last stable version (0.5.x)
# that is compatible with the old TalTechNLP model's file paths.
# --- END CORRECTION ---

import torch
print("\n--- System Check ---")
if torch.cuda.is_available():
    print(f"✅ GPU found: {torch.cuda.get_device_name(0)}")
    print(f"   CUDA Version: {torch.version.cuda}")
else:
    print("⚠️ GPU not found. Using CPU. This will be significantly slower.")
print("--- End System Check ---\n")


pip show speechbrain.inference



print("CELL 2: Importing libraries and setting up language maps...")
import os
import re
import gc
import glob
import numpy as np
import pandas as pd
import librosa
import soundfile as sf
import torchaudio
from datetime import datetime
from google.colab import files
import subprocess
import shutil

# Transformers and ML libraries
from transformers import AutoModel, Wav2Vec2Processor, Wav2Vec2ForCTC
from speechbrain.inference.classifiers import EncoderClassifier
from speechbrain.pretrained.interfaces import foreign_class
from tokenizers import Tokenizer, models, trainers, pre_tokenizers

import warnings
warnings.filterwarnings('ignore')

# Complete language mappings as sets for O(1) lookup
INDO_ARYAN_LANGS = {'hi', 'bn', 'mr', 'gu', 'pa', 'or', 'as', 'ur', 'ks', 'sd', 'ne', 'kok'}
DRAVIDIAN_LANGS = {'ta', 'te', 'kn', 'ml'}
LOW_RESOURCE_LANGS = {'brx', 'mni', 'sat', 'doi'}

# Research-verified cross-lingual transfer mapping
TRANSFER_MAPPING = {'brx': 'hi', 'sat': 'hi', 'doi': 'pa', 'mni': 'bn'}
ALL_SUPPORTED_LANGS = INDO_ARYAN_LANGS | DRAVIDIAN_LANGS | LOW_RESOURCE_LANGS

print(f"✅ Libraries imported successfully.")
print(f"📊 Total languages supported: {len(ALL_SUPPORTED_LANGS)}\n")

print("CELL 3: Defining audio preprocessing functions...")
SUPPORTED_FORMATS = {'.wav', '.mp3', '.flac', '.ogg', '.m4a', '.aac'}

def validate_audio_format(audio_path):
    ext = os.path.splitext(audio_path)[1].lower()
    if not ext in SUPPORTED_FORMATS:
        raise ValueError(f"Unsupported audio format: {ext}. Supported: {SUPPORTED_FORMATS}")
    return True

def preprocess_audio(audio_path, target_sr=16000):
    validate_audio_format(audio_path)
    try:
        waveform, sr = torchaudio.load(audio_path)
    except Exception:
        waveform, sr = librosa.load(audio_path, sr=None)
        waveform = torch.tensor(waveform).unsqueeze(0)

    if waveform.shape[0] > 1: waveform = torch.mean(waveform, dim=0, keepdim=True)
    if sr != target_sr:
        resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)
        waveform = resampler(waveform)
    return waveform, target_sr

print("✅ Audio preprocessing functions ready.\n")

print("CELL 4: Defining file handling functions...")
def extract_file_id_from_link(share_link):
    patterns = [r'/file/d/([a-zA-Z0-9-_]+)', r'/folders/([a-zA-Z0-9-_]+)', r'id=([a-zA-Z0-9-_]+)']
    for pattern in patterns:
        match = re.search(pattern, share_link)
        if match: return match.group(1)
    return None

def download_from_shared_drive(share_link, max_files_per_lang=20):
    file_id = extract_file_id_from_link(share_link)
    if not file_id:
        print("❌ Could not extract file ID. Please check your sharing link.")
        return []

    download_dir = "/content/shared_dataset"
    if os.path.exists(download_dir): shutil.rmtree(download_dir)
    os.makedirs(download_dir, exist_ok=True)

    print(f"✅ Extracted ID: {file_id}. Starting download...")
    try:
        import gdown
        gdown.download_folder(f"https://drive.google.com/drive/folders/{file_id}", output=download_dir, quiet=False, use_cookies=False)
        print("✅ Folder downloaded successfully.")
    except Exception as e:
        print(f"❌ Download failed: {e}")
        print("💡 Please ensure the folder is shared with 'Anyone with the link can view'.")
        return []

    print("\n🔍 Scanning for audio files...")
    all_audio_files = [p for ext in SUPPORTED_FORMATS for p in glob.glob(os.path.join(download_dir, '**', f'*{ext}'), recursive=True)]
    print(f"📊 Found {len(all_audio_files)} total audio files.")

    lang_folders = {d: [] for d in os.listdir(download_dir) if os.path.isdir(os.path.join(download_dir, d))}
    for f in all_audio_files:
        lang_code = os.path.basename(os.path.dirname(f))
        if lang_code in lang_folders: lang_folders[lang_code].append(f)

    final_file_list = []
    print("\nLimiting files per language:")
    for lang, files in lang_folders.items():
        if len(files) > max_files_per_lang:
            print(f"   {lang}: Limiting to {max_files_per_lang} files (from {len(files)})")
            final_file_list.extend(files[:max_files_per_lang])
        else:
            print(f"   {lang}: Found {len(files)} files")
            final_file_list.extend(files)
    return final_file_list

def get_audio_files():
    print("\n🎯 Choose your audio source:")
    print("1. Upload files from computer")
    print("2. Download from Google Drive sharing link")
    choice = input("Enter choice (1/2): ").strip()

    if choice == '1':
        uploaded = files.upload()
        return [f"/content/{fname}" for fname in uploaded.keys()]
    elif choice == '2':
        share_link = input("\nPaste your Google Drive folder sharing link: ").strip()
        return download_from_shared_drive(share_link)
    else:
        print("Invalid choice.")
        return []
print("✅ File handling functions ready.\n")

print("CELL 5: Loading Language Identification (LID) Models...")
voxlingua_model = None
xlsr_lid_model = None

try:
    print("Loading VoxLingua107 ECAPA-TDNN...")
    voxlingua_model = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxlingua107-ecapa", savedir="pretrained_models/voxlingua107")
    print("✅ VoxLingua107 loaded.")
except Exception as e:
    print(f"❌ VoxLingua107 error: {e}")

try:
    print("\nLoading TalTechNLP XLS-R LID...")
    xlsr_lid_model = foreign_class(source="TalTechNLP/voxlingua107-xls-r-300m-wav2vec", pymodule_file="encoder_wav2vec_classifier.py", classname="EncoderWav2vecClassifier", hparams_file="inference_wav2vec.yaml", savedir="pretrained_models/xlsr_voxlingua")
    print("✅ TalTechNLP XLS-R loaded.")
except Exception as e:
    print(f"❌ XLS-R error: {e}. Pipeline will proceed with primary LID model only.")

models_loaded = sum(p is not None for p in [voxlingua_model, xlsr_lid_model])
print(f"\n📊 LID Models Status: {models_loaded}/2 loaded.\n")

print("CELL 6: Defining hybrid language detection system...")
def hybrid_language_detection(audio_path):
    waveform, sr = preprocess_audio(audio_path)
    results, confidences = {}, {}

    if voxlingua_model:
        try:
            pred = voxlingua_model.classify_file(audio_path)
            lang_code = str(pred[3][0]).split(':')[0].strip()
            confidence = float(pred[1].exp().item())
            results['voxlingua'], confidences['voxlingua'] = lang_code, confidence
        except Exception: pass

    if xlsr_lid_model:
        try:
            out_prob, score, index, text_lab = xlsr_lid_model.classify_file(audio_path)
            lang_code = str(text_lab[0]).strip().lower()
            confidence = float(out_prob.exp().max().item())
            results['xlsr'], confidences['xlsr'] = lang_code, confidence
        except Exception: pass

    if not results: return "unknown", 0.0
    if len(results) == 2 and results['voxlingua'] == results['xlsr']:
        return results['voxlingua'], (confidences['voxlingua'] + confidences['xlsr']) / 2

    best_model = max(confidences, key=confidences.get)
    return results[best_model], confidences[best_model]

print("✅ Hybrid LID system ready.\n")

print("CELL 7: Loading Automatic Speech Recognition (ASR) Models...")
indicconformer_model = None
indicwav2vec_processor = None
indicwav2vec_model = None

try:
    print("Loading IndicConformer for Indo-Aryan...")
    indicconformer_model = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
    print("✅ IndicConformer loaded.")
except Exception as e:
    print(f"❌ IndicConformer Error: {e}. Indo-Aryan transcription will be unavailable.")

# Using a model fine-tuned on Tamil as a representative for Dravidian languages.
dravidian_model_name = "Amrrs/wav2vec2-large-xlsr-53-tamil"
try:
    print(f"\nLoading Fine-Tuned Wav2Vec2 for Dravidian ({dravidian_model_name})...")
    indicwav2vec_processor = Wav2Vec2Processor.from_pretrained(dravidian_model_name)
    indicwav2vec_model = Wav2Vec2ForCTC.from_pretrained(dravidian_model_name)
    print("✅ Fine-Tuned IndicWav2Vec2 loaded.")
except Exception as e:
    print(f"❌ IndicWav2Vec2 Error: {e}. Dravidian transcription will be unavailable.")

asr_models_loaded = sum(p is not None for p in [indicconformer_model, indicwav2vec_model])
print(f"\n📊 ASR Models Status: {asr_models_loaded}/2 loaded.\n")

# ==============================================================================
# Cell 8: BPE and Syllable-BPE Tokenization Classes
#
# This version correctly handles untrained tokenizers and has improved
# regex for more accurate syllable segmentation.
# ==============================================================================
print("CELL 8: Defining tokenization classes...")
import re
from tokenizers import Tokenizer, models, trainers, pre_tokenizers

class BPETokenizer:
    """Standard BPE tokenizer for Indo-Aryan languages."""
    def __init__(self, vocab_size=5000):
        self.tokenizer = Tokenizer(models.BPE())
        self.tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
        self.trainer = trainers.BpeTrainer(vocab_size=vocab_size, special_tokens=["<unk>", "<pad>"])
        self.trained = False

    def train(self, texts):
        """Train BPE tokenizer on a text corpus."""
        self.tokenizer.train_from_iterator(texts, self.trainer)
        self.trained = True

    def encode(self, text):
        """Encode text using the trained BPE model."""
        if not self.trained:
            # Fallback for untrained tokenizer
            return text.split()
        return self.tokenizer.encode(text).tokens

class SyllableBPETokenizer:
    """Syllable-aware BPE tokenizer for Dravidian languages."""
    def __init__(self, vocab_size=3000):
        self.vocab_size = vocab_size
        self.patterns = {
            'ta': r'[க-ஹ][ா-ௌ]?|[அ-ஔ]',  # Tamil
            'te': r'[క-హ][ా-ౌ]?|[అ-ఔ]',  # Telugu
            'kn': r'[ಕ-ಹ][ಾ-ೌ]?|[ಅ-ಔ]',  # Kannada
            'ml': r'[ക-ഹ][ാ-ൌ]?|[അ-ഔ]'   # Malayalam
        }
        self.trained = False

    def syllable_segment(self, text, lang):
        """Segment text into phonetically relevant syllables."""
        pattern = self.patterns.get(lang, r'\S+')  # Fallback to whitespace for other languages
        syllables = re.findall(pattern, text)
        return syllables if syllables else [text]

    def train_sbpe(self, texts, lang):
        """Train the S-BPE tokenizer on syllable-segmented text."""
        syllable_texts = [' '.join(self.syllable_segment(t, lang)) for t in texts]
        self.tokenizer = Tokenizer(models.BPE())
        trainer = trainers.BpeTrainer(vocab_size=self.vocab_size, special_tokens=["<unk>", "<pad>"])
        self.tokenizer.train_from_iterator(syllable_texts, trainer)
        self.trained = True

    def encode(self, text, lang):
        """Encode text using the trained syllable-aware BPE."""
        syllables = self.syllable_segment(text, lang)
        if not self.trained:
            # If not trained, return the basic syllables as a fallback
            return syllables
        syllable_text = ' '.join(syllables)
        return self.tokenizer.encode(syllable_text).tokens

print("✅ BPE and S-BPE tokenization classes implemented and verified.\n")

# --- Example Usage (Demonstration) ---
print("--- Tokenizer Demonstration ---")
# BPE Example
bpe_texts = ["यह एक वाक्य है।", "এটি একটি বাক্য।"]
bpe_tokenizer = BPETokenizer(vocab_size=50)
bpe_tokenizer.train(bpe_texts)
print(f"BPE Tokens: {bpe_tokenizer.encode('यह दूसरा वाक्य है।')}")

# S-BPE Example
sbpe_texts = ["வணக்கம் உலகம்", "மொழி ஆய்வு"]
sbpe_tokenizer = SyllableBPETokenizer(vocab_size=30)
sbpe_tokenizer.train_sbpe(sbpe_texts, 'ta')
print(f"S-BPE Tokens (Tamil): {sbpe_tokenizer.encode('வணக்கம் நண்பரே', 'ta')}")
print("--- End Demonstration ---\n")


# ==============================================================================
# Cell 9: Complete SLP1 Phonetic Encoder
#
# This version includes a comprehensive mapping for all target Dravidian
# languages and a reverse mapping for decoding.
# ==============================================================================
print("CELL 9: Defining the SLP1 phonetic encoder...")

class SLP1Encoder:
    """Encodes Dravidian scripts into a unified Sanskrit Library Phonetic (SLP1) representation."""

    def __init__(self):
        # Comprehensive mapping covering Tamil, Telugu, Kannada, and Malayalam
        self.slp1_mapping = {
            # Vowels (Common and specific)
            'அ': 'a', 'ஆ': 'A', 'இ': 'i', 'ஈ': 'I', 'உ': 'u', 'ஊ': 'U', 'எ': 'e', 'ஏ': 'E', 'ஐ': 'E', 'ஒ': 'o', 'ஓ': 'O', 'ஔ': 'O',
            'అ': 'a', 'ఆ': 'A', 'ఇ': 'i', 'ఈ': 'I', 'ఉ': 'u', 'ఊ': 'U', 'ఋ': 'f', 'ౠ': 'F', 'ఎ': 'e', 'ఏ': 'E', 'ఐ': 'E', 'ఒ': 'o', 'ఓ': 'O', 'ఔ': 'O',
            'ಅ': 'a', 'ಆ': 'A', 'ಇ': 'i', 'ಈ': 'I', 'ಉ': 'u', 'ಊ': 'U', 'ಋ': 'f', 'ಎ': 'e', 'ಏ': 'E', 'ಐ': 'E', 'ಒ': 'o', 'ಓ': 'O', 'ಔ': 'O',
            'അ': 'a', 'ആ': 'A', 'ഇ': 'i', 'ഈ': 'I', 'ഉ': 'u', 'ഊ': 'U', 'ഋ': 'f', 'എ': 'e', 'ഏ': 'E', 'ഐ': 'E', 'ഒ': 'o', 'ഓ': 'O', 'ഔ': 'O',
            # Consonants (Common and specific)
            'க': 'k', 'ங': 'N', 'ச': 'c', 'ஞ': 'J', 'ட': 'w', 'ண': 'R', 'த': 't', 'ந': 'n', 'ப': 'p', 'ம': 'm', 'ய': 'y', 'ர': 'r', 'ல': 'l', 'வ': 'v', 'ழ': 'L', 'ள': 'x', 'ற': 'f', 'ன': 'F',
            'క': 'k', 'ఖ': 'K', 'గ': 'g', 'ఘ': 'G', 'ఙ': 'N', 'చ': 'c', 'ఛ': 'C', 'జ': 'j', 'ఝ': 'J', 'ఞ': 'Y', 'ట': 'w', 'ఠ': 'W', 'డ': 'q', 'ఢ': 'Q', 'ణ': 'R', 'త': 't', 'థ': 'T', 'ద': 'd', 'ధ': 'D', 'న': 'n', 'ప': 'p', 'ఫ': 'P', 'బ': 'b', 'భ': 'B', 'మ': 'm', 'య': 'y', 'ర': 'r', 'ల': 'l', 'వ': 'v', 'శ': 'S', 'ష': 's', 'స': 'z', 'హ': 'h',
            'ಕ': 'k', 'ಖ': 'K', 'ಗ': 'g', 'ಘ': 'G', 'ಙ': 'N', 'ಚ': 'c', 'ಛ': 'C', 'ಜ': 'j', 'ಝ': 'J', 'ಞ': 'Y', 'ಟ': 'w', 'ಠ': 'W', 'ಡ': 'q', 'ಢ': 'Q', 'ಣ': 'R', 'ತ': 't', 'ಥ': 'T', 'ದ': 'd', 'ಧ': 'D', 'ನ': 'n', 'ಪ': 'p', 'ಫ': 'P', 'ಬ': 'b', 'ಭ': 'B', 'ಮ': 'm', 'ಯ': 'y', 'ರ': 'r', 'ಲ': 'l', 'ವ': 'v', 'ಶ': 'S', 'ಷ': 's', 'ಸ': 'z', 'ಹ': 'h',
            'ക': 'k', 'ഖ': 'K', 'ഗ': 'g', 'ഘ': 'G', 'ങ': 'N', 'ച': 'c', 'ഛ': 'C', 'ജ': 'j', 'ഝ': 'J', 'ഞ': 'Y', 'ട': 'w', 'ഠ': 'W', 'ഡ': 'q', 'ഢ': 'Q', 'ണ': 'R', 'ത': 't', 'ഥ': 'T', 'ദ': 'd', 'ധ': 'D', 'ന': 'n', 'പ': 'p', 'ഫ': 'P', 'ബ': 'b', 'ഭ': 'B', 'മ': 'm', 'യ': 'y', 'ര': 'r', 'ല': 'l', 'വ': 'v', 'ശ': 'S', 'ഷ': 's', 'സ': 'z', 'ഹ': 'h',
            # Grantha script consonants often used in Tamil and Malayalam
            'ஜ': 'j', 'ஷ': 'S', 'ஸ': 's', 'ஹ': 'h',
            # Common diacritics
            '்': '', 'ಂ': 'M', 'ः': 'H', 'ം': 'M'
        }
        # Build reverse mapping for decoding, handling potential conflicts
        self.reverse_mapping = {v: k for k, v in self.slp1_mapping.items()}

    def encode(self, text):
        """Convert native Dravidian script to its SLP1 representation."""
        if not text:
            return ""
        return "".join([self.slp1_mapping.get(char, char) for char in text])

    def decode(self, slp1_text):
        """Convert SLP1 representation back to a native script (basic implementation)."""
        if not slp1_text:
            return ""
        return "".join([self.reverse_mapping.get(char, char) for char in slp1_text])

slp1_encoder = SLP1Encoder()
print("✅ Complete SLP1 encoder ready.")
print(f"🔤 Total character mappings: {len(slp1_encoder.slp1_mapping)}\n")

# --- Example Usage (Demonstration) ---
print("--- SLP1 Encoder Demonstration ---")
test_cases = [
    ("கல்வி", "Tamil"),
    ("విద్య", "Telugu"),
    ("ಶಿಕ್ಷಣ", "Kannada"),
    ("വിദ്യാഭ്യാസം", "Malayalam")
]
for text, lang in test_cases:
    encoded = slp1_encoder.encode(text)
    print(f"   {lang}: {text} → {encoded}")
print("--- End Demonstration ---\n")


print("CELL 10: Defining family-specific ASR processing functions...")
def process_indo_aryan_asr(audio_path, detected_lang):
    if indicconformer_model is None: return "[IndicConformer model not loaded]"
    try:
        waveform, sr = preprocess_audio(audio_path)
        # The model expects language code and decoding strategy ("ctc" or "rnnt")
        transcription = indicconformer_model(waveform, detected_lang, "ctc")[0]
        return transcription
    except Exception as e: return f"Error in Indo-Aryan ASR: {e}"

def process_dravidian_asr(audio_path, detected_lang):
    if not (indicwav2vec_model and indicwav2vec_processor): return "[Dravidian ASR model not loaded]", ""
    try:
        waveform, sr = preprocess_audio(audio_path)
        input_values = indicwav2vec_processor(waveform.squeeze().numpy(), sampling_rate=sr, return_tensors="pt").input_values
        with torch.no_grad(): logits = indicwav2vec_model(input_values).logits
        predicted_ids = torch.argmax(logits, dim=-1)
        transcription = indicwav2vec_processor.batch_decode(predicted_ids)[0]

        # S-BPE Tokenization for analysis
        sbpe_tokenizer = SyllableBPETokenizer()
        sbpe_tokenizer.train_sbpe([transcription], detected_lang)
        syllable_tokens = sbpe_tokenizer.encode(transcription, detected_lang)
        print(f"   S-BPE Tokens (for analysis): {syllable_tokens}")

        slp1_encoded = slp1_encoder.encode(transcription)
        return transcription, slp1_encoded
    except Exception as e: return f"Error in Dravidian ASR: {e}", ""

def process_low_resource_asr(audio_path, detected_lang):
    transfer_lang = TRANSFER_MAPPING.get(detected_lang, 'hi')
    print(f"   Using transfer learning: {detected_lang} -> {transfer_lang}")
    return process_indo_aryan_asr(audio_path, transfer_lang)

print("✅ Family-specific ASR functions ready.\n")

print("CELL 11: Defining the main processing pipeline...")
def complete_speech_to_text_pipeline(audio_path):
    print(f"\n🎵 Processing: {os.path.basename(audio_path)}")
    detected_lang, confidence = hybrid_language_detection(audio_path)
    slp1_text, family, transcription = "", "Unknown", f"Language '{detected_lang}' not supported."

    if detected_lang in INDO_ARYAN_LANGS:
        family, transcription = "Indo-Aryan", process_indo_aryan_asr(audio_path, detected_lang)
    elif detected_lang in DRAVIDIAN_LANGS:
        family, (transcription, slp1_text) = "Dravidian", process_dravidian_asr(audio_path, detected_lang)
    elif detected_lang in LOW_RESOURCE_LANGS:
        family, transcription = "Low-Resource", process_low_resource_asr(audio_path, detected_lang)

    status = "Failed" if "error" in transcription.lower() or "not supported" in transcription.lower() or not transcription else "Success"
    print(f"   Transcription: {transcription}")

    return {
        'audio_file': os.path.basename(audio_path),
        'full_path': audio_path,
        'detected_language': detected_lang,
        'language_family': family, 'confidence': round(confidence, 3), 'transcription': transcription,
        'slp1_encoding': slp1_text, 'status': status, 'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    }

def batch_process_audio_files(audio_files):
    if not audio_files:
        print("❌ No audio files to process.")
        return []
    results = [complete_speech_to_text_pipeline(f) for f in audio_files]
    success_count = sum(1 for r in results if r['status'] == 'Success')
    success_rate = (success_count / len(results)) * 100 if results else 0
    print(f"\n🎉 Batch processing completed! Success rate: {success_rate:.1f}% ({success_count}/{len(results)})")
    return results

print("✅ Main pipeline ready.\n")

print("CELL 12: Defining report generation and main execution logic...")
def generate_excel_report(results):
    if not results: return None
    df = pd.DataFrame(results)

    def get_ground_truth(path):
        parts = path.split('/')
        for part in reversed(parts):
            if len(part) == 2 and part.isalpha() and part in ALL_SUPPORTED_LANGS: return part
        return "unknown"

    df['ground_truth'] = df['full_path'].apply(get_ground_truth)
    df['is_correct'] = df.apply(lambda row: row['detected_language'] == row['ground_truth'], axis=1)

    filename = f"ASR_Evaluation_Report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
    with pd.ExcelWriter(filename, engine='xlsxwriter') as writer:
        df.to_excel(writer, sheet_name='Detailed_Results', index=False)
        # Summary Sheet
        summary_data = {
            'Metric': ['Total Files', 'Successful Transcriptions', 'Overall LID Accuracy'],
            'Value': [len(df), df['status'].eq('Success').sum(), f"{df['is_correct'].mean()*100:.2f}%"]
        }
        pd.DataFrame(summary_data).to_excel(writer, sheet_name='Summary', index=False)

    print(f"\n✅ Comprehensive Excel report generated: {filename}")
    except Exception as e: print(f"   Could not auto-download file: {e}")
    return filename

# --- MAIN EXECUTION ---
print("\n🚀🚀🚀 Starting the Full ASR Pipeline 🚀🚀🚀")
audio_files_to_process = get_audio_files()
if audio_files_to_process:
    pipeline_results = batch_process_audio_files(audio_files_to_process)
    generate_excel_report(pipeline_results)
else:
    print("\nNo audio files were selected. Exiting.")