finetune.py

#!/usr/bin/env python
# finetune_whisper_mix_datasets.py

"""
Fine-tune openai/whisper-large-v3 on mixed datasets from different languages:
- FLEURS Cebuano (ceb_ph)
- FLEURS Khmer (km_kh) 
- Switchboard1 English
- WenetSpeech Chinese
- Eng-Indon-CS
- Eng-Malay-CS
Based on the Hugging Face blog: https://huggingface.co/blog/fine-tune-whisper

To run this script on multiple GPUs, you have several options:

1. **Automatic Multi-GPU (DataParallel-style):**
   python finetune_whisper_mix_datasets.py
   
   The script will automatically detect and use all available GPUs.

2. **Distributed Training with torchrun (Recommended for 2+ GPUs):**
   torchrun --nproc_per_node=2 finetune_whisper_mix_datasets.py
   
   This uses DistributedDataParallel which is more efficient.

3. **Distributed Training with accelerate (Alternative):**
   accelerate launch --num_processes=2 finetune_whisper_mix_datasets.py
   
   Requires: pip install accelerate

Note: With 2 GPUs, the effective batch size becomes:
per_device_batch_size * num_gpus * gradient_accumulation_steps
= 24 * 2 * 1 = 48 (compared to 32 with single GPU)

CPU Core Limiting:
The script automatically limits CPU usage to 20 cores using environment variables.
You can also set these manually before running:
   export OMP_NUM_THREADS=20
   export MKL_NUM_THREADS=20
   export NUMEXPR_NUM_THREADS=20
   python finetune_whisper_mix_datasets.py
"""

import os
import random
import io
import yaml
import argparse
from itertools import chain

# Load configuration from YAML file
def load_config(config_path):
    with open(config_path, 'r') as file:
        return yaml.safe_load(file)

# Parse command line arguments
parser = argparse.ArgumentParser(description='Fine-tune Whisper on mixed datasets')
parser.add_argument('--config', type=str, default='config.yaml', 
                   help='Path to configuration YAML file')
args = parser.parse_args()

# Load configuration
config = load_config(args.config)

# Set environment variables from config
env_config = config['environment']
os.environ["OMP_NUM_THREADS"] = env_config['omp_num_threads']
os.environ["MKL_NUM_THREADS"] = env_config['mkl_num_threads']
os.environ["OPENBLAS_NUM_THREADS"] = env_config['openblas_num_threads']
os.environ["VECLIB_MAXIMUM_THREADS"] = env_config['veclib_maximum_threads']
os.environ["NUMEXPR_NUM_THREADS"] = env_config['numexpr_num_threads']
os.environ["TOKENIZERS_PARALLELISM"] = env_config['tokenizers_parallelism']
os.environ["TRANSFORMERS_NO_TF"] = env_config['transformers_no_tf']

import torch
from datasets import load_dataset, Audio, concatenate_datasets, Dataset
from torch.utils.data import Dataset as TorchDataset
from transformers import (
    WhisperProcessor,
    WhisperForConditionalGeneration,
    Seq2SeqTrainingArguments,
    Seq2SeqTrainer,
)
import ipdb
import evaluate
import numpy as np
import ipdb

# Multi-GPU setup
if torch.cuda.device_count() > 1:
    print(f"Setting up for {torch.cuda.device_count()} GPUs")
    # Enable distributed training environment variables if not already set
    if "LOCAL_RANK" not in os.environ:
        os.environ["LOCAL_RANK"] = "0"
    if "WORLD_SIZE" not in os.environ:
        os.environ["WORLD_SIZE"] = str(torch.cuda.device_count())


from dataclasses import dataclass
from typing import Any, Dict, List, Union

class WhisperOnTheFlyDataset(TorchDataset):
    """Custom dataset that preprocesses audio on-the-fly during training"""
    
    def __init__(self, dataset, processors, main_processor, max_target_length, audio_config):
        self.dataset = dataset
        self.processors = processors
        self.main_processor = main_processor
        self.max_target_length = max_target_length
        self.sampling_rate = audio_config['sampling_rate']
        
    def __len__(self):
        return len(self.dataset)
    
    def __getitem__(self, idx):
        item = self.dataset[idx]
        # Process audio
        audio_sample = item["audio"]
        audio_data = self._process_audio(audio_sample)
        
        # Extract with main processor
        inputs = self.main_processor.feature_extractor(
            audio_data, 
            sampling_rate=self.sampling_rate, 
            return_tensors="pt"
        )
        
        # Process text with appropriate processor
        lang = item["language"]
        if lang in ["cebuano", "khmer"]:
            text = item["transcription"]
        else:  # english, chinese
            text = item["text"]
        
        # Tokenize with appropriate processor
        if lang == "cebuano":
            labels = self.processors["cebuano"].tokenizer(
                text,
                return_tensors="pt",
                padding=False,
                truncation=True,
                max_length=self.max_target_length
            )
        elif lang == "khmer":
            labels = self.processors["khmer"].tokenizer(
                text,
                return_tensors="pt", 
                padding=False,
                truncation=True,
                max_length=self.max_target_length
            )
        elif lang == "english":
            labels = self.processors["english"].tokenizer(
                text,
                return_tensors="pt",
                padding=False
            )
        elif lang == "chinese":
            labels = self.processors["chinese"].tokenizer(
                text,
                return_tensors="pt",
                padding=False
            )
        elif lang == "indonesian":
            labels = self.processors["indonesian"].tokenizer(
                text,
                return_tensors="pt",
                padding=False
            )
        else: # Malay
            labels = self.processors["malay"].tokenizer(
                text,
                return_tensors="pt",
                padding=False
            )
        
        return {
            "input_features": inputs.input_features.squeeze(0),
            "labels": labels.input_ids.squeeze(0),
            "language": lang
        }
    
    def _process_audio(self, audio_sample):
        """Process audio sample into numpy array"""
        import librosa
        
        if isinstance(audio_sample, dict):
            if "array" in audio_sample:
                return audio_sample["array"]
            elif "bytes" in audio_sample and audio_sample["bytes"] is not None:
                audio_array, _ = librosa.load(io.BytesIO(audio_sample["bytes"]), sr=self.sampling_rate)
                return audio_array
            elif "path" in audio_sample:
                audio_array, _ = librosa.load(audio_sample["path"], sr=self.sampling_rate)
                return audio_array
            else:
                raise ValueError(f"Unknown audio dict format: {audio_sample.keys()}")
        elif isinstance(audio_sample, str):
            audio_array, _ = librosa.load(audio_sample, sr=self.sampling_rate)
            return audio_array
        else:
            return audio_sample

@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
    processor: Any
    decoder_start_token_id: int

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        # split inputs and labels since they have to be of different lengths and need different padding methods
        # first treat the audio inputs by simply returning torch tensors
        input_features = [{"input_features": feature["input_features"]} for feature in features]
        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")

        # get the tokenized label sequences
        label_features = [{"input_ids": feature["labels"]} for feature in features]
        # pad the labels to max length
        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")

        # replace padding with -100 to ignore loss correctly
        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)

        # if bos token is appended in previous tokenization step,
        # cut bos token here as it's append later anyways
        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
            labels = labels[:, 1:]

        batch["labels"] = labels

        return batch

# → Choose device (GPU if available)
device = "cuda" if torch.cuda.is_available() else "cpu"

# Extract configuration values
MODEL_CHECKPOINT = config['model']['checkpoint']
OUTPUT_DIR = config['output']['output_dir']
MAX_TARGET_LENGTH = config['model']['max_target_length']

# CPU usage configuration for dataset preprocessing
MAX_CPU_CORES = config['environment']['max_cpu_cores']
TEST_CPU_CORES = config['environment']['test_cpu_cores']

# Language configurations for each dataset
DATASET_CONFIGS = config['languages']

print("Loading datasets...")

# Load datasets for each language dynamically based on configuration
datasets = {}
dataset_configs = config['datasets']
audio_config = config['audio']

# Get list of enabled languages from both languages and datasets config
enabled_languages = set(config['languages'].keys()) & set(config['datasets'].keys())
print(f"Enabled languages: {list(enabled_languages)}")

def load_fleurs_dataset(lang_name, lang_config, dataset_config):
    """Load FLEURS dataset for a language"""
    print(f"Loading FLEURS {lang_name.title()}...")
    lang_datasets = load_dataset(
        dataset_config['source'], 
        dataset_config['language_code'],
        split={k: v for k, v in dataset_config['splits'].items()}, 
        trust_remote_code=dataset_config['trust_remote_code']
    )
    # DON'T decode audio yet - keep it compressed until preprocessing
    for split in dataset_config['splits'].keys():
        lang_datasets[split] = lang_datasets[split].cast_column("audio", Audio(sampling_rate=audio_config['sampling_rate'], decode=False))
    
    # Use subset of training data if specified
    if 'train_subset_ratio' in lang_config:
        train_subset_ratio = lang_config['train_subset_ratio']
        lang_datasets["train"] = lang_datasets["train"].train_test_split(test_size=1-train_subset_ratio, seed=config['data_processing']['seed'])["train"]
    
    return lang_datasets

def load_simple_dataset(lang_name, dataset_config):
    """Load simple dataset with train/validation/test splits"""
    print(f"Loading {lang_name.title()}...")
    lang_dataset = load_dataset(dataset_config['source'], split={k: v for k, v in dataset_config['splits'].items()})
    return lang_dataset

def load_english_dataset(lang_config, dataset_config):
    """Load English dataset with custom train/validation split"""
    print("Loading English...")
    swb_train = load_dataset(dataset_config['train_dataset'], split=dataset_config['train_split'], streaming=dataset_config['streaming'])
    swb_test = load_dataset(dataset_config['test_dataset'], split=dataset_config['test_split'], streaming=dataset_config['streaming'])
    # Split into train/validation
    validation_size = lang_config['validation_size']
    swb_val = swb_train.take(validation_size)
    swb_train = swb_train.skip(validation_size)
    return {
        "train": swb_train,
        "validation": swb_val,
        "test": swb_test
    }

def load_chinese_dataset(dataset_config):
    """Load Chinese dataset with multiple test splits"""
    print("Loading Chinese...")
    wenet_train = load_dataset(dataset_config['train_dataset'], streaming=dataset_config['streaming'])
    wenet_valid = load_dataset(dataset_config['validation_dataset'], dataset_config['validation_config'], split="validation", streaming=dataset_config['streaming'])
    wenet_testnet = load_dataset(dataset_config['test_net_dataset'], dataset_config['test_net_config'], split="test", streaming=dataset_config['streaming'])
    wenet_testmeeting = load_dataset(dataset_config['test_meeting_dataset'], dataset_config['test_meeting_config'], split="test", streaming=dataset_config['streaming'])
    return {
        "train": wenet_train["train"],
        "validation": wenet_valid,
        "test_net": wenet_testnet,
        "test_meeting": wenet_testmeeting
    }

# Load datasets for each enabled language
for lang in enabled_languages:
    lang_config = config['languages'][lang]
    dataset_config = dataset_configs[lang]
    
    if lang in ['cebuano', 'khmer']:
        # FLEURS datasets
        datasets[lang] = load_fleurs_dataset(lang, lang_config, dataset_config)
    elif lang == 'english':
        # English with custom validation split
        datasets[lang] = load_english_dataset(lang_config, dataset_config)
    elif lang == 'chinese':
        # Chinese with multiple test splits
        datasets[lang] = load_chinese_dataset(dataset_config)
    elif lang in ['indonesian', 'malay']:
        # Simple datasets with standard splits
        datasets[lang] = load_simple_dataset(lang, dataset_config)
    else:
        print(f"Warning: Unknown language {lang}, treating as simple dataset")
        datasets[lang] = load_simple_dataset(lang, dataset_config)

print("Setting up processors...")

# Create processors for each enabled language
processors = {}
for lang in enabled_languages:
    lang_config = config['languages'][lang]
    processors[lang] = WhisperProcessor.from_pretrained(
        MODEL_CHECKPOINT, 
        language=lang_config["whisper_language"]
    )

# Use the first available processor as the main one, preferring English if available
if "english" in processors:
    main_processor = processors["english"]
elif processors:
    main_processor = processors[list(processors.keys())[0]]
else:
    raise ValueError("No processors created. Check your language configuration.")
model = WhisperForConditionalGeneration.from_pretrained(MODEL_CHECKPOINT)

# Multi-GPU handling
if torch.cuda.device_count() > 1:
    print(f"Using {torch.cuda.device_count()} GPUs for training")
    # The model will be automatically distributed by the Trainer
    model.to(device)
else:
    model.to(device)



print("Adding language labels to raw datasets...")

# Remove existing language columns and add our own consistent language labels for each enabled language
for lang in enabled_languages:
    lang_datasets = datasets[lang]
    
    # Handle different dataset structures
    if isinstance(lang_datasets, dict):
        # Dataset with explicit splits (train/validation/test)
        for split_name, split_dataset in lang_datasets.items():
            if split_dataset is not None:
                # Remove existing language column if it exists
                columns_to_remove = [col for col in split_dataset.column_names if col.lower() in ["language", "lang"]]
                if columns_to_remove:
                    print(f"Removing existing language column(s) {columns_to_remove} from {lang} {split_name}")
                    datasets[lang][split_name] = split_dataset.remove_columns(columns_to_remove)
                
                # Add our consistent language label
                datasets[lang][split_name] = datasets[lang][split_name].add_column("language", [lang] * len(datasets[lang][split_name]))
    else:
        # Single dataset object - this shouldn't happen with current structure but handle gracefully
        print(f"Warning: Unexpected dataset structure for {lang}")
        continue


print("Combining raw datasets before preprocessing...")

# Ensure all datasets have compatible schemas before concatenation
def standardize_dataset_schema(dataset, dataset_name):
    """Standardize dataset schema to ensure compatibility for concatenation"""
    print(f"Standardizing schema for {dataset_name}...")
    
    # Keep audio compressed until preprocessing - only set sampling rate
    if "audio" in dataset.column_names:
        print(f"  Setting audio feature type to {audio_config['sampling_rate']}Hz (compressed) for {dataset_name}")
        dataset = dataset.cast_column("audio", Audio(sampling_rate=audio_config['sampling_rate'], decode=False))
    
    # Remove problematic columns that might have different types
    columns_to_remove = []
    for col in dataset.column_names:
        if col in config['data_processing']['columns_to_remove']:
            columns_to_remove.append(col)
    
    if columns_to_remove:
        print(f"  Removing incompatible columns: {columns_to_remove}")
        dataset = dataset.remove_columns(columns_to_remove)
    
    return dataset

# Standardize all training datasets dynamically
print("Standardizing training datasets...")
raw_train_datasets = []
for lang in enabled_languages:
    if "train" in datasets[lang]:
        std_dataset = standardize_dataset_schema(datasets[lang]["train"], f"{lang}_train")
        raw_train_datasets.append(std_dataset)

# Standardize validation datasets dynamically  
print("Standardizing validation datasets...")
raw_val_datasets = []
for lang in enabled_languages:
    if "validation" in datasets[lang]:
        std_dataset = standardize_dataset_schema(datasets[lang]["validation"], f"{lang}_val")
        raw_val_datasets.append(std_dataset)

# Combine datasets if we have any
if raw_train_datasets:
    print("Combining training datasets...")
    combined_raw_train = concatenate_datasets(raw_train_datasets)
    combined_raw_train = combined_raw_train.shuffle(seed=config['data_processing']['seed'])
else:
    raise ValueError("No training datasets found. Check your configuration.")

if raw_val_datasets:
    print("Combining validation datasets...")
    combined_raw_val = concatenate_datasets(raw_val_datasets)
    combined_raw_val = combined_raw_val.shuffle(seed=config['data_processing']['seed'])
else:
    print("Warning: No validation datasets found. Training without validation.")
    combined_raw_val = None

print("Creating on-the-fly datasets (no preprocessing stored to disk)...")

# Create on-the-fly datasets instead of preprocessing and storing
# Create on-the-fly datasets instead of preprocessing and storing
combined_train_dataset = WhisperOnTheFlyDataset(
    combined_raw_train, 
    processors, 
    main_processor, 
    MAX_TARGET_LENGTH,
    audio_config
)

# Only create validation dataset if we have validation data
if combined_raw_val is not None:
    combined_val_dataset = WhisperOnTheFlyDataset(
        combined_raw_val, 
        processors, 
        main_processor, 
        MAX_TARGET_LENGTH,
        audio_config
    )
else:
    combined_val_dataset = None

print("Creating on-the-fly test datasets...")

# Create on-the-fly test datasets dynamically
processed_datasets = {}

for lang in enabled_languages:
    processed_datasets[lang] = {}
    
    # Handle different test split structures for different languages
    if lang == "chinese":
        # Chinese has multiple test splits
        if "test_net" in datasets[lang]:
            processed_datasets[lang]["test_net"] = WhisperOnTheFlyDataset(
                datasets[lang]["test_net"], 
                processors, 
                main_processor, 
                MAX_TARGET_LENGTH,
                audio_config
            )
        if "test_meeting" in datasets[lang]:
            processed_datasets[lang]["test_meeting"] = WhisperOnTheFlyDataset(
                datasets[lang]["test_meeting"], 
                processors, 
                main_processor, 
                MAX_TARGET_LENGTH,
                audio_config
            )
    else:
        # Standard test split
        if "test" in datasets[lang]:
            processed_datasets[lang]["test"] = WhisperOnTheFlyDataset(
                datasets[lang]["test"], 
                processors, 
                main_processor, 
                MAX_TARGET_LENGTH,
                audio_config
            )

# Data Collator
data_collator = DataCollatorSpeechSeq2SeqWithPadding(
    processor=main_processor,
    decoder_start_token_id=model.config.decoder_start_token_id,
)

# Metrics: WER & CER (using Hugging Face Evaluate)
wer_metric = evaluate.load("wer")
cer_metric = evaluate.load("cer")

def compute_metrics(pred):
    """
    Compute WER and CER metrics for predictions
    """
    pred_ids = pred.predictions
    pred_str = main_processor.batch_decode(pred_ids, skip_special_tokens=True)
    
    label_ids = pred.label_ids
    label_ids[label_ids == -100] = main_processor.tokenizer.pad_token_id
    ref_str = main_processor.batch_decode(label_ids, skip_special_tokens=True)

    # lowercase & strip
    pred_str = [s.lower().strip() for s in pred_str]
    ref_str = [s.lower().strip() for s in ref_str]

    wer_score = wer_metric.compute(predictions=pred_str, references=ref_str)
    cer_score = cer_metric.compute(predictions=pred_str, references=ref_str)
    return {"wer": wer_score, "cer": cer_score}

# Check for multi-GPU setup
num_gpus = torch.cuda.device_count()
print(f"Number of available GPUs: {num_gpus}")

# Get training configuration
training_config = config['training']

# Adjust batch size and gradient accumulation for multi-GPU
if num_gpus > 1:
    # With multiple GPUs, use multi-GPU configuration
    gpu_config = training_config['multi_gpu']
    per_device_batch_size = gpu_config['per_device_train_batch_size']
    per_device_eval_batch_size = gpu_config['per_device_eval_batch_size']
    gradient_accumulation_steps = gpu_config['gradient_accumulation_steps']
    print(f"Multi-GPU training detected. Using {num_gpus} GPUs.")
else:
    # Single GPU configuration
    gpu_config = training_config['single_gpu']
    per_device_batch_size = gpu_config['per_device_train_batch_size']
    per_device_eval_batch_size = gpu_config['per_device_eval_batch_size']
    gradient_accumulation_steps = gpu_config['gradient_accumulation_steps']
    print("Single GPU training.")

# Training Arguments
training_args = Seq2SeqTrainingArguments(
    output_dir=OUTPUT_DIR,
    per_device_train_batch_size=per_device_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    learning_rate=training_config['learning_rate'],
    warmup_steps=training_config['warmup_steps'],
    max_steps=training_config['max_steps'],
    gradient_checkpointing=training_config['gradient_checkpointing'],
    fp16=training_config['fp16'],
    eval_strategy=training_config['eval_strategy'],
    per_device_eval_batch_size=per_device_eval_batch_size,
    predict_with_generate=training_config['predict_with_generate'],
    generation_max_length=training_config['generation_max_length'],
    save_steps=training_config['save_steps'],
    eval_steps=training_config['eval_steps'],
    logging_steps=training_config['logging_steps'],
    report_to=training_config['report_to'],
    load_best_model_at_end=training_config['load_best_model_at_end'],
    metric_for_best_model=training_config['metric_for_best_model'],
    greater_is_better=training_config['greater_is_better'],
    push_to_hub=training_config['push_to_hub'],
    save_total_limit=training_config['save_total_limit'],
    # Multi-GPU specific settings
    dataloader_drop_last=training_config['dataloader_drop_last'],
    ddp_find_unused_parameters=training_config['ddp_find_unused_parameters'],
)

# Initialize Seq2SeqTrainer
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=combined_train_dataset,
    eval_dataset=combined_val_dataset,
    data_collator=data_collator,
    tokenizer=main_processor.feature_extractor,
    compute_metrics=compute_metrics,
)

def evaluate_on_test_sets():
    """Evaluate the model on all test sets from enabled languages"""
    print("\n" + "="*60)
    print("EVALUATING ON ALL TEST SETS")
    print("="*60)
    
    results = {}
    
    for lang in enabled_languages:
        if lang in processed_datasets:
            lang_results = {}
            
            if lang == "chinese":
                # Chinese has multiple test splits
                if "test_net" in processed_datasets[lang]:
                    print(f"\n***** Evaluating on WenetSpeech Chinese TEST_NET *****")
                    chi_testnet_metrics = trainer.predict(processed_datasets[lang]["test_net"], metric_key_prefix=f"test_{lang}_net")
                    print(f"Chinese TEST_NET WER: {chi_testnet_metrics.metrics[f'test_{lang}_net_wer']*100:.2f}%")
                    print(f"Chinese TEST_NET CER: {chi_testnet_metrics.metrics[f'test_{lang}_net_cer']*100:.2f}%")
                    lang_results["test_net"] = chi_testnet_metrics.metrics
                
                if "test_meeting" in processed_datasets[lang]:
                    print(f"\n***** Evaluating on WenetSpeech Chinese TEST_MEETING *****")
                    chi_testmeet_metrics = trainer.predict(processed_datasets[lang]["test_meeting"], metric_key_prefix=f"test_{lang}_meeting")
                    print(f"Chinese TEST_MEETING WER: {chi_testmeet_metrics.metrics[f'test_{lang}_meeting_wer']*100:.2f}%")
                    print(f"Chinese TEST_MEETING CER: {chi_testmeet_metrics.metrics[f'test_{lang}_meeting_cer']*100:.2f}%")
                    lang_results["test_meeting"] = chi_testmeet_metrics.metrics
            else:
                # Standard test split
                if "test" in processed_datasets[lang]:
                    print(f"\n***** Evaluating on {lang.title()} test set *****")
                    test_metrics = trainer.predict(processed_datasets[lang]["test"], metric_key_prefix=f"test_{lang}")
                    print(f"{lang.title()} Test WER: {test_metrics.metrics[f'test_{lang}_wer']*100:.2f}%")
                    print(f"{lang.title()} Test CER: {test_metrics.metrics[f'test_{lang}_cer']*100:.2f}%")
                    lang_results["test"] = test_metrics.metrics
            
            results[lang] = lang_results
    
    # Summary
    print("\n" + "="*60)
    print("SUMMARY OF ALL TEST RESULTS")
    print("="*60)
    
    for lang in enabled_languages:
        if lang in results:
            if lang == "chinese":
                if "test_net" in results[lang]:
                    wer = results[lang]["test_net"][f"test_{lang}_net_wer"] * 100
                    cer = results[lang]["test_net"][f"test_{lang}_net_cer"] * 100
                    print(f"Chinese-NET: WER={wer:.2f}% | CER={cer:.2f}%")
                if "test_meeting" in results[lang]:
                    wer = results[lang]["test_meeting"][f"test_{lang}_meeting_wer"] * 100
                    cer = results[lang]["test_meeting"][f"test_{lang}_meeting_cer"] * 100
                    print(f"Chinese-MTG: WER={wer:.2f}% | CER={cer:.2f}%")
            else:
                if "test" in results[lang]:
                    wer = results[lang]["test"][f"test_{lang}_wer"] * 100
                    cer = results[lang]["test"][f"test_{lang}_cer"] * 100
                    print(f"{lang.title():12}: WER={wer:.2f}% | CER={cer:.2f}%")
    
    return results

if __name__ == "__main__":
    print(f"Total training samples: {len(combined_train_dataset)}")
    print(f"Total validation samples: {len(combined_val_dataset)}")
    print("Starting training...")
    
    # Fine-tune the model
    trainer.train()
    
    # Evaluate on all test sets
    evaluate_on_test_sets()