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# Evaluate with Seed-TTS testset
import sys
import 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 = "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}")
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