返回 F5-TTS
eval_seedtts_testset.py
根目录 / src / f5_tts / eval / eval_seedtts_testset.py
1 # Evaluate with Seed-TTS testset
2
3 import argparse
4 import ast
5 import json
6 import os
7 import sys
8
9
10 sys.path.append(os.getcwd())
11
12 import multiprocessing as mp
13 from importlib.resources import files
14
15 import numpy as np
16
17 from f5_tts.eval.utils_eval import get_seed_tts_test, run_asr_wer, run_sim
18
19
20 rel_path = str(files("f5_tts").joinpath("../../"))
21
22
23 def get_args():
24 parser = argparse.ArgumentParser()
25 parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
26 parser.add_argument("-l", "--lang", type=str, default="en", choices=["zh", "en"])
27 parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
28 parser.add_argument(
29 "-n", "--gpu_nums", type=str, default="8", help="Number of GPUs to use (e.g., 8) or GPU list (e.g., [0,1,2,3])"
30 )
31 parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
32 return parser.parse_args()
33
34
35 def parse_gpu_nums(gpu_nums_str):
36 try:
37 if gpu_nums_str.startswith("[") and gpu_nums_str.endswith("]"):
38 gpu_list = ast.literal_eval(gpu_nums_str)
39 if isinstance(gpu_list, list):
40 return gpu_list
41 return list(range(int(gpu_nums_str)))
42 except (ValueError, SyntaxError):
43 raise argparse.ArgumentTypeError(
44 f"Invalid GPU specification: {gpu_nums_str}. Use a number (e.g., 8) or a list (e.g., [0,1,2,3])"
45 )
46
47
48 def main():
49 args = get_args()
50 eval_task = args.eval_task
51 lang = args.lang
52 gen_wav_dir = args.gen_wav_dir
53 metalst = rel_path + f"/data/seedtts_testset/{lang}/meta.lst" # seed-tts testset
54
55 # NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different
56 # zh 1.254 seems a result of 4 workers wer_seed_tts
57 gpus = parse_gpu_nums(args.gpu_nums)
58 test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus)
59
60 local = args.local
61 if local: # use local custom checkpoint dir
62 if lang == "zh":
63 asr_ckpt_dir = "../checkpoints/funasr" # paraformer-zh dir under funasr
64 elif lang == "en":
65 asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
66 else:
67 asr_ckpt_dir = "" # auto download to cache dir
68 wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
69
70 # --------------------------------------------------------------------------
71
72 full_results = []
73 metrics = []
74
75 if eval_task == "wer":
76 with mp.Pool(processes=len(gpus)) as pool:
77 args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
78 results = pool.map(run_asr_wer, args)
79 for r in results:
80 full_results.extend(r)
81 elif eval_task == "sim":
82 with mp.Pool(processes=len(gpus)) as pool:
83 args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
84 results = pool.map(run_sim, args)
85 for r in results:
86 full_results.extend(r)
87 else:
88 raise ValueError(f"Unknown metric type: {eval_task}")
89
90 result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
91 with open(result_path, "w") as f:
92 for line in full_results:
93 metrics.append(line[eval_task])
94 f.write(json.dumps(line, ensure_ascii=False) + "\n")
95 metric = round(np.mean(metrics), 5)
96 f.write(f"\n{eval_task.upper()}: {metric}\n")
97
98 print(f"\nTotal {len(metrics)} samples")
99 print(f"{eval_task.upper()}: {metric}")
100 print(f"{eval_task.upper()} results saved to {result_path}")
101
102
103 if __name__ == "__main__":
104 main()
105
105 lines PYTHON