# Evaluate with Seed-TTS testset import sys, os sys.path.append(os.getcwd()) import multiprocessing as mp import numpy as np from model.utils import ( get_seed_tts_test, run_asr_wer, run_sim, ) eval_task = "wer" # sim | wer lang = "zh" # zh | en metalst = f"data/seedtts_testset/{lang}/meta.lst" # seed-tts testset # gen_wav_dir = f"data/seedtts_testset/{lang}/wavs" # ground truth wavs gen_wav_dir = f"PATH_TO_GENERATED" # generated wavs # NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different # zh 1.254 seems a result of 4 workers wer_seed_tts gpus = [0,1,2,3,4,5,6,7] test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus) local = False if local: # use local custom checkpoint dir if lang == "zh": asr_ckpt_dir = "../checkpoints/funasr" # paraformer-zh dir under funasr elif lang == "en": asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3" else: asr_ckpt_dir = "" # auto download to cache dir wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth" # --------------------------- WER --------------------------- if eval_task == "wer": wers = [] with mp.Pool(processes=len(gpus)) as pool: args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set] results = pool.map(run_asr_wer, args) for wers_ in results: wers.extend(wers_) wer = round(np.mean(wers)*100, 3) print(f"\nTotal {len(wers)} samples") print(f"WER : {wer}%") # --------------------------- SIM --------------------------- if eval_task == "sim": sim_list = [] with mp.Pool(processes=len(gpus)) as pool: args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set] results = pool.map(run_sim, args) for sim_ in results: sim_list.extend(sim_) sim = round(sum(sim_list)/len(sim_list), 3) print(f"\nTotal {len(sim_list)} samples") print(f"SIM : {sim}")