Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,298 Bytes
4dab15f |
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 |
# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
import sys
import os
sys.path.append(os.getcwd())
import multiprocessing as mp
from importlib.resources import files
import numpy as np
from f5_tts.eval.utils_eval import (
get_librispeech_test,
run_asr_wer,
run_sim,
)
rel_path = str(files("f5_tts").joinpath("../../"))
eval_task = "wer" # sim | wer
lang = "en"
metalst = rel_path + "/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}")
|