File size: 7,331 Bytes
a16e4aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
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
import opencc
from ASRDataset import *
converter = opencc.OpenCC('s2tw.json')
normalizer = {
"en_us" : EnglishTextNormalizer(),
"other" : BasicTextNormalizer()
}
model_id = "/mnt/jeff/gemma_test"
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.",
}
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):
sample_results = []
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=covost_collate_fn)
evaluated_samples = {}
for batch_idx, batch in enumerate(tqdm(dataloader)):
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
)
input_lengths = batch['input_ids'].shape[1]
label_ids = generate_ids[:, input_lengths:]
batch_references = processor.batch_decode(
label_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
for i, (reference, prediction) in enumerate(zip(batch_references, batch_predictions)):
idx = batch_idx + 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": min(100,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": min(100,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__":
datasets = []
pickup_dataset = MultiturnAudioDataset(split='eval',processor=processor,json_path='/mnt/jeff/InCar/data/multiturn_data/pickup_processed.json')
datasets.append(pickup_dataset)
for dataset in datasets:
# ASR
asr_results = evaluate_task(dataset)
print(f"\n=== {asr_results.get('dataset', 'Dataset')}")
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')}") |