eval.py

from io import BytesIO
from urllib.request import urlopen
import soundfile
import torch
from datasets import load_dataset, Audio
import numpy as np
from transformers import AutoModel, AutoProcessor, BatchFeature
from tqdm import tqdm
import json
import os
import time
from datetime import datetime
from whisper_normalizer.english import EnglishTextNormalizer
from whisper_normalizer.basic import BasicTextNormalizer
import sacrebleu
from jiwer import cer, wer
from torch.utils.data import Dataset, DataLoader
import soundfile as sf
import re
from pathlib import Path

normalizer = {
    "en_us" : EnglishTextNormalizer(),
    "other" : BasicTextNormalizer()
}

model_id = "/home/jeff/codes/llm/InCar/gemma-3-4b-it-omni"
revision = "main" #"v1.0"

model = AutoModel.from_pretrained(
    model_id, device_map="cuda", revision = revision, trust_remote_code=True
).eval()

processor = AutoProcessor.from_pretrained(
    model_id, revision = revision, trust_remote_code=True
)

results_dir = f"evaluation_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
os.makedirs(results_dir, exist_ok=True)


INSTRUCTION = {
    "ast": "Translate the audio to {0}.",
    "asr": "Transcribe the audio clip into text.",
}

class BaseAudioDataset(Dataset):
    def __init__(self, processor, split, sampling_rate=16000, debug=False):
        self.processor = processor
        self.training = "train" in split
        self.debug = debug
        self.sampling_rate = sampling_rate
        self.name = ""

    def set_dataset_name(self, name):
        self.name = name

    @staticmethod
    def filter_corrupted_files(data, audio_field, text_fields, dataset_name, sampling_rate=16000, debug=True):
        original_size = len(data)
        
        data = data.cast_column(audio_field, Audio(decode=False))
        
        def identify_corrupted_files(example):
            try:
                sf.read(example[audio_field]["path"])
                
                for field in text_fields:
                    if example[field].replace('"', '') == "":
                        return False
                return True
            except Exception:
                return False
        
        data = data.filter(identify_corrupted_files, num_proc=16)
        validated_size = len(data)
        
        data = data.cast_column(audio_field, Audio(sampling_rate=sampling_rate, decode=True))
        
        
        return data

    @staticmethod
    def filter_by_audio_length(data, audio_field, min_sec=2, max_sec=20, debug=True):
        original_size = len(data)
        
        def filter_audio_by_length(example):
            try:
                audio = example[audio_field]['array']
                channel = 1
                if hasattr(audio, 'ndim') and audio.ndim > 1:
                    channel = audio.ndim
                    audio = audio.squeeze()
                audio_length = len(audio) / example[audio_field]['sampling_rate'] / channel
                return min_sec <= audio_length <= max_sec
            except Exception as e:
                return False
        
        data = data.filter(filter_audio_by_length, num_proc=16)
        filtered_size = len(data)

        return data

    def prepare_model_inputs(self, audio_array, instruction, answer_text):
        user_message = {
            'role': 'user',
            'content': '<start_of_audio>' + instruction,
        }
        prompt = self.processor.tokenizer.apply_chat_template(
            [user_message], tokenize=False, add_generation_prompt=True, add_bos=True
        )
        
        inputs = self.processor(
            text=prompt, 
            audio=[audio_array], 
            add_special_tokens=False, 
            return_tensors='pt'
        )
        
        input_ids = inputs.input_ids
        token_type_ids = inputs.token_type_ids
        
        return {
            'input_ids': input_ids,
            'token_type_ids': token_type_ids,
            'input_audio_embeds': inputs.input_audio_embeds,
            'audio_embed_sizes': inputs.audio_embed_sizes,
            'input_modes': inputs.input_modes,
            'answer': answer_text,
        }


# Libri Speech Dataset Class
class LibriSpeechDataset(BaseAudioDataset):
    def __init__(self, processor, subset, split, sampling_rate=16000, debug=False):
        super().__init__(processor, split, sampling_rate, debug)
        
        self.set_dataset_name(f"LibriSpeech_{subset}")
        # only ASR
        self.ast = False
        self.lang = "en"
        
        # load dataset
        self.data = load_dataset("openslr/librispeech_asr",
                            subset,
                            split=split,
                            trust_remote_code=True,
                            cache_dir=Path("/home/jeff/codes/llm/InCar/data")
                            )
        
        # (Optional) Audio length Filtering
        self.data = self.filter_by_audio_length(self.data, "audio")
            
        # Instruction Setting
        self.instruction = INSTRUCTION["asr"]
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        data = self.data[idx]
        
        # Libri Speech is only for ASR
        answer_text = data["text"].replace('"', '')
        
        return self.prepare_model_inputs(
            data["audio"]["array"],
            INSTRUCTION["asr"],
            answer_text
        )
    
# common_voice_16_1 dataset
class CommonVoiceDataset(BaseAudioDataset):
    def __init__(self, processor, split, source_lang, sampling_rate=16000, debug=False):
        super().__init__(processor, split, sampling_rate, debug)
        
        self.set_dataset_name(f"CommonVoice_{source_lang}")
        # only ASR
        self.ast = False
        self.lang=source_lang
        
        # load dataset
        self.data = load_dataset("mozilla-foundation/common_voice_16_1",
                            source_lang,
                            split=split,
                            trust_remote_code=True,
                            cache_dir=Path("/home/jeff/codes/llm/InCar/data")
                            )
        def prepare_dataset(batch):
            """Function to preprocess the dataset with the .map method"""
            transcription = batch["sentence"]
            
            if transcription.startswith('"') and transcription.endswith('"'):
                # we can remove trailing quotation marks as they do not affect the transcription
                transcription = transcription[1:-1]
            
            if transcription[-1] not in [".", "?", "!"]:
                # append a full-stop to sentences that do not end in punctuation
                transcription = transcription + "."
            
            batch["sentence"] = transcription
            
            return batch
        self.data.map(prepare_dataset, desc="preprocess dataset")
        
        # (Optional) Audio length Filtering
        self.data = self.filter_by_audio_length(self.data, "audio")
            
        # Instruction Setting
        self.instruction = INSTRUCTION["asr"]
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        data = self.data[idx]
        
        answer_text = data["sentence"]
        return self.prepare_model_inputs(
            data["audio"]["array"],
            INSTRUCTION["asr"],
            answer_text
        )
    

# Fleurs Dataset Class
class FleursDataset(BaseAudioDataset):
    def __init__(self, processor, split, source_lang, target_lang=None, 
                 mode="asr", sampling_rate=16000, debug=False):
        super().__init__(processor, split, sampling_rate, debug)
        
        self.set_dataset_name("Fleurs")
        # Mode Setting (ASR or AST)
        if mode not in ["asr", "ast"]:
            raise ValueError("mode must be 'asr' or 'ast'.")
        
        self.mode = mode
        self.ast = (mode == "ast")
        self.source_lang = source_lang
        
        # Language name mapping (expand if needed)
        self.lang_names = {
            'en_us': 'English', 'cmn_hans': 'Mandarin Chinese'
        }
        
        # load dataset - source language dataset
        self.data = load_dataset("google/fleurs",
                            source_lang,
                            split=split,
                            trust_remote_code=True,
                            cache_dir=Path("/home/jeff/codes/llm/InCar/data")
                            )
        def prepare_dataset(batch):
            import opencc
            converter = opencc.OpenCC('s2tw.json')
            if self.ast:
                translation = converter.convert(batch["translation"])
                batch["translation"] = translation
            else:
                transcription = converter.convert(batch["transcription"])
                batch["transcription"] = transcription
            
            return batch
        if (source_lang=="cmn_hans_cn" and not self.ast) or (self.ast and target_lang=="cmn_hans_cn"):
            self.data.map(prepare_dataset, desc="preprocess dataset")

        # (Optional) Audio length Filtering
        self.data = self.filter_by_audio_length(self.data, "audio")
        self.target_lang_name = ""
        # When AST mode, load target language dataset.
        if self.ast:
            if target_lang is None:
                raise ValueError("AST mode requires target_lang.")
                
            self.target_lang = target_lang
            self.lang = f"{source_lang}_{target_lang}"
            
            # load dataset - target language dataset (for translation)
            target_data = load_dataset("google/fleurs",
                                target_lang,
                                split=split,
                                trust_remote_code=True,
                                cache_dir=Path("/home/jeff/codes/llm/InCar/data")
                                )
            
            source_dict = {item['id']: item for item in self.data}
            target_dict = {item['id']: item for item in target_data}
            
            # only Common ID, add translation fields
            common_ids = set(source_dict.keys()) & set(target_dict.keys())
            print(f"FLEURS AST Common data filtering: {len(self.data)} -> {len(common_ids)}")
            self.data = [
                {**source_dict[id], 'translation': target_dict[id]['transcription']}
                for id in common_ids
            ]

            # Instruction Setting - use target language name
            self.target_lang_name = self.lang_names.get(target_lang, target_lang.capitalize())
            self.instruction = INSTRUCTION["ast"]
        else:
            # ASR mode
            self.lang = source_lang
            self.instruction = INSTRUCTION["asr"]

        if self.debug:
            print(f"FLEURS dataset loaded: {self.mode.upper()} mode")
            print(f"source lang: {source_lang} ({self.lang_names.get(source_lang, source_lang)})")
            if self.ast:
                print(f"target lang: {target_lang} ({self.lang_names.get(target_lang, target_lang)})")
            print(f"dataset size: {len(self.data)}")
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        data = self.data[idx]
        audio_array = data["audio"]["array"]

        if self.ast:
            answer_text = data["translation"]
        else:
            answer_text = data["transcription"]
        
        return self.prepare_model_inputs(
            audio_array,
            self.instruction.format(self.target_lang_name),
            answer_text
        )

def pad_sequence(sequences, padding_side='left', padding_value=0):
    """
    Pad a list of sequences to the same length.
    sequences: list of tensors in [seq_len, *] shape
    """
    assert padding_side in ['right', 'left']
    max_size = sequences[0].size()
    trailing_dims = max_size[1:]
    max_len = max(len(seq) for seq in sequences)
    batch_size = len(sequences)
    output = sequences[0].new_full((batch_size, max_len) + trailing_dims, padding_value)
    for i, seq in enumerate(sequences):
        length = seq.size(0)
        if padding_side == 'right':
            output.data[i, :length] = seq
        else:
            output.data[i, -length:] = seq
    return output

def cat_with_pad(tensors, dim, padding_value=0):
    """
    cat along dim, while pad to max for all other dims
    """
    ndim = tensors[0].dim()
    assert all(
        t.dim() == ndim for t in tensors[1:]
    ), 'All tensors must have the same number of dimensions'

    out_size = [max(t.shape[i] for t in tensors) for i in range(ndim)]
    out_size[dim] = sum(t.shape[dim] for t in tensors)
    output = tensors[0].new_full(out_size, padding_value)

    index = 0
    for t in tensors:
        # Create a slice list where every dimension except dim is full slice
        slices = [slice(0, t.shape[d]) for d in range(ndim)]
        # Update only the concat dimension slice
        slices[dim] = slice(index, index + t.shape[dim])

        output[slices] = t
        index += t.shape[dim]

    return output

def covost_collate_fn(batch):
    input_ids_list = []
    input_audio_embeds_list = []
    audio_embed_sizes_list = []
    audio_attention_mask_list = []
    input_modes_list = []
    answer_list = []
    for inputs in batch:
        input_ids_list.append(inputs['input_ids'][0])
        input_audio_embeds_list.append(inputs['input_audio_embeds'])
        audio_embed_sizes_list.append(inputs['audio_embed_sizes'])
        audio_attention_mask_list.append(
            inputs['input_audio_embeds'].new_full((inputs['input_audio_embeds'].size(1),), True, dtype=torch.bool)
        )
        input_modes_list.append(inputs['input_modes'])
        answer_list.append(inputs['answer'])

    try:
        input_ids = pad_sequence(input_ids_list, padding_side='left', padding_value=0)
        audio_attention_mask = (
            pad_sequence(audio_attention_mask_list, padding_side='right', padding_value=False)
            if len(audio_attention_mask_list) > 1
            else None
        )
    except Exception as e:
        print(e)
        print(input_ids_list)
        print(audio_attention_mask)
        raise
    attention_mask = (input_ids != 0).long()
    input_audio_embeds = cat_with_pad(input_audio_embeds_list, dim=0)
    audio_embed_sizes = torch.cat(audio_embed_sizes_list)
    input_modes = torch.cat(input_modes_list)

    return BatchFeature(
        {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
            'input_audio_embeds': input_audio_embeds,
            'audio_embed_sizes': audio_embed_sizes,
            'audio_attention_mask': audio_attention_mask,
            'input_modes': input_modes,
            'answer': answer_list,
        }
    )

def save_results(results, dataset_name, task, source_lang, target_lang=None, sample_idx=None):
    filename = f"{task}_{dataset_name}_{source_lang}"
    if target_lang:
        filename += f"_to_{target_lang}"
    if sample_idx is not None:
        filename += f"_sample_{sample_idx}"
    
    filepath = os.path.join(results_dir, f"{filename}.json")
    
    results["timestamp"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    
    with open(filepath, 'w', encoding='utf-8') as f:
        json.dump(results, f, ensure_ascii=False, indent=2)
    
    return filepath

def evaluate_task(dataset, source_lang, target_lang, num_samples=-1, batch_size = 4, is_asr=True):
    import opencc
    converter = opencc.OpenCC('s2tw.json')
    task_type = "asr" if is_asr else "translation"
    eval_lang = source_lang if is_asr else target_lang
    if eval_lang in normalizer:
        eval_normalizer = normalizer[eval_lang]
    else:
        eval_normalizer = normalizer['other']
    sample_results = []
    
    if num_samples > 0 and num_samples < len(dataset):
        indices = np.random.choice(len(dataset), num_samples, replace=False)
        dataset = dataset.select(indices)
    
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, collate_fn=covost_collate_fn)
        
    evaluated_samples = {} 
    
    for batch_idx, batch in enumerate(tqdm(dataloader)):
        batch_references = batch.pop("answer")

        if torch.cuda.is_available():
            try:
                batch = {k: v.to("cuda") for k, v in batch.items()}
            except:
                print('error')
                break
        
        with torch.inference_mode():
            generate_ids = model.generate(**batch, 
            max_new_tokens=256,
            #temperature = 1.0, top_p = 0.95, top_k = 64, do_sample=True
            )
            
            input_lengths = batch['input_ids'].shape[1]
            generate_ids = generate_ids[:, input_lengths:]
        
            batch_predictions = processor.batch_decode(
                generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
            )
        
        for i, (reference, prediction) in enumerate(zip(batch_references, batch_predictions)):
            idx = batch_idx * batch_size + i
            sample_result = {
                "id": idx,
                "reference": reference,
                "prediction": converter.convert(prediction)
            }
            sample_results.append(sample_result)
        
        if (batch_idx + 1) % 10 == 0:
            temp_results = []
            
            for item in sample_results:
                sample_id = item["id"]
                
                if sample_id in evaluated_samples:
                    temp_item = item.copy()
                    temp_item.update(evaluated_samples[sample_id])
                    temp_results.append(temp_item)
                else:
                    temp_item = item.copy()
                    try:
                        
                        ref = eval_normalizer(item["reference"])
                        pred = eval_normalizer(item["prediction"])

                        # BLEU, WER/CER
                        utt_bleu = sacrebleu.sentence_bleu(pred, [ref]).score
                        utt_cer = round(cer(re.sub(r"\s+", "", ref), re.sub(r"\s+", "", pred)) * 100, 2)
                        utt_wer = round(wer(ref, pred) * 100, 2)

                        metrics = {
                            "bleu": utt_bleu,
                            "cer": utt_cer,
                            "wer": utt_wer
                        }
                        
                        evaluated_samples[sample_id] = metrics
                        temp_item.update(metrics)
                    except Exception as e:
                        print(f"Error evaluating sample {sample_id}: {e}")
                        metrics = {
                            "bleu": 0,
                            "cer": 100,
                            "wer": 100,
                            "error": str(e)
                        }
                        evaluated_samples[sample_id] = metrics
                        temp_item.update(metrics)
                
                    temp_results.append(temp_item)

            partial_results = {
                "task": task_type,
                "source_lang": source_lang,
                "target_lang": target_lang,
                "num_samples": len(temp_results),
                "sample_results": temp_results
            }
            save_results(partial_results, dataset.name, task_type, source_lang, target_lang)

    for item in sample_results:
        ref = eval_normalizer(item["reference"])
        pred = eval_normalizer(item["prediction"])

        utt_bleu = sacrebleu.sentence_bleu(pred, [ref]).score
        utt_cer = round(cer(re.sub(r"\s+", "", ref), re.sub(r"\s+", "", pred)) * 100, 2)
        utt_wer = round(wer(ref, pred) * 100, 2)

        item.update({
            "bleu": utt_bleu,
            "cer": utt_cer,
            "wer": utt_wer
        })

    avg_bleu = sum(item["bleu"] for item in sample_results) / len(sample_results)
    avg_cer = sum(item["cer"] for item in sample_results) / len(sample_results)
    avg_wer = sum(item["wer"] for item in sample_results) / len(sample_results)
    
    results = {
        "dataset": dataset.name,
        "task": task_type,
        "source_lang": source_lang,
        "target_lang": target_lang,
        "num_samples": len(sample_results),
        "metrics": {
            "bleu": avg_bleu,
            "cer": avg_cer,
            "wer": avg_wer
        },
        "sample_results": sample_results
    }
    
    save_results(results, dataset.name, task_type, source_lang, target_lang)
    return results


if __name__ == "__main__":

    source_languages = [
        ("en_us", "English"),
    ]
    
    target_languages = [
        ("zh-TW", "zh-TW"),
    ]

    num_samples = -1
    batch_size = 2

    for source_lang, target_lang in zip(source_languages, target_languages):
        print(f"\n===== {source_lang[0]} ASR =====")
        
        split = "test"

        datasets = []

        commonvoice_speech_tw = CommonVoiceDataset(
            processor=processor,
            source_lang="zh-TW",
            split=split
        )
        datasets.append(commonvoice_speech_tw)

        # Libri Speech Clean ASR mode (English -> English text)
        # libri_speech_clean = LibriSpeechDataset(
        #     processor=processor,
        #     subset="clean",
        #     split=split
        # )
        # datasets.append(libri_speech_clean)

        # # Libri Speech Other ASR mode (English -> English text)
        # libri_speech_other = LibriSpeechDataset(
        #     processor=processor,
        #     subset="other",
        #     split=split
        # )
        # datasets.append(libri_speech_other)

        # Fleurs ASR mode (English -> English text)
        fleurs = FleursDataset(
            processor=processor,
            split=split,
            source_lang="en_us",  # English
            mode="asr"
        )
        datasets.append(fleurs)

        for dataset in datasets:
            # ASR
            asr_results = evaluate_task(dataset, source_lang[0], target_lang[0], num_samples, batch_size=batch_size, is_asr = True)

            print(f"\n=== {asr_results.get('dataset', 'Dataset')} | {source_lang[0]} ASR===")
            print(f"BLEU: {asr_results.get('metrics', {}).get('bleu', 'N/A')}")
            print(f"WER: {asr_results.get('metrics', {}).get('wer', 'N/A')}")
            print(f"CER: {asr_results.get('metrics', {}).get('cer', 'N/A')}")