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# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
import sys, os
sys.path.append(os.getcwd())
import multiprocessing as mp
import numpy as np
from model.utils import (
get_librispeech_test,
run_asr_wer,
run_sim,
)
eval_task = "wer" # sim | wer
lang = "en"
metalst = "data/librispeech_pc_test_clean_cross_sentence.lst"
librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean" # test-clean path
gen_wav_dir = "PATH_TO_GENERATED" # generated wavs
gpus = [0,1,2,3,4,5,6,7]
test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
## leading to a low similarity for the ground truth in some cases.
# test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth = True) # eval ground truth
local = False
if local: # use local custom checkpoint dir
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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