E2-F5-TTS / src /f5_tts /eval /utils_eval.py
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import math
import os
import random
import string
import torch
import torch.nn.functional as F
import torchaudio
from tqdm import tqdm
from f5_tts.eval.ecapa_tdnn import ECAPA_TDNN_SMALL
from f5_tts.model.modules import MelSpec
from f5_tts.model.utils import convert_char_to_pinyin
# seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav
def get_seedtts_testset_metainfo(metalst):
f = open(metalst)
lines = f.readlines()
f.close()
metainfo = []
for line in lines:
if len(line.strip().split("|")) == 5:
utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
elif len(line.strip().split("|")) == 4:
utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav")
if not os.path.isabs(prompt_wav):
prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
metainfo.append((utt, prompt_text, prompt_wav, gt_text, gt_wav))
return metainfo
# librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav
def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path):
f = open(metalst)
lines = f.readlines()
f.close()
metainfo = []
for line in lines:
ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
# ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.' # if use librispeech test-clean (no-pc)
ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
# gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.' # if use librispeech test-clean (no-pc)
gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav))
return metainfo
# padded to max length mel batch
def padded_mel_batch(ref_mels):
max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax()
padded_ref_mels = []
for mel in ref_mels:
padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0)
padded_ref_mels.append(padded_ref_mel)
padded_ref_mels = torch.stack(padded_ref_mels)
padded_ref_mels = padded_ref_mels.permute(0, 2, 1)
return padded_ref_mels
# get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav
def get_inference_prompt(
metainfo,
speed=1.0,
tokenizer="pinyin",
polyphone=True,
target_sample_rate=24000,
n_fft=1024,
win_length=1024,
n_mel_channels=100,
hop_length=256,
mel_spec_type="vocos",
target_rms=0.1,
use_truth_duration=False,
infer_batch_size=1,
num_buckets=200,
min_secs=3,
max_secs=40,
):
prompts_all = []
min_tokens = min_secs * target_sample_rate // hop_length
max_tokens = max_secs * target_sample_rate // hop_length
batch_accum = [0] * num_buckets
utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = (
[[] for _ in range(num_buckets)] for _ in range(6)
)
mel_spectrogram = MelSpec(
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
n_mel_channels=n_mel_channels,
target_sample_rate=target_sample_rate,
mel_spec_type=mel_spec_type,
)
for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."):
# Audio
ref_audio, ref_sr = torchaudio.load(prompt_wav)
ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio)))
if ref_rms < target_rms:
ref_audio = ref_audio * target_rms / ref_rms
assert ref_audio.shape[-1] > 5000, f"Empty prompt wav: {prompt_wav}, or torchaudio backend issue."
if ref_sr != target_sample_rate:
resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
ref_audio = resampler(ref_audio)
# Text
if len(prompt_text[-1].encode("utf-8")) == 1:
prompt_text = prompt_text + " "
text = [prompt_text + gt_text]
if tokenizer == "pinyin":
text_list = convert_char_to_pinyin(text, polyphone=polyphone)
else:
text_list = text
# Duration, mel frame length
ref_mel_len = ref_audio.shape[-1] // hop_length
if use_truth_duration:
gt_audio, gt_sr = torchaudio.load(gt_wav)
if gt_sr != target_sample_rate:
resampler = torchaudio.transforms.Resample(gt_sr, target_sample_rate)
gt_audio = resampler(gt_audio)
total_mel_len = ref_mel_len + int(gt_audio.shape[-1] / hop_length / speed)
# # test vocoder resynthesis
# ref_audio = gt_audio
else:
ref_text_len = len(prompt_text.encode("utf-8"))
gen_text_len = len(gt_text.encode("utf-8"))
total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed)
# to mel spectrogram
ref_mel = mel_spectrogram(ref_audio)
ref_mel = ref_mel.squeeze(0)
# deal with batch
assert infer_batch_size > 0, "infer_batch_size should be greater than 0."
assert (
min_tokens <= total_mel_len <= max_tokens
), f"Audio {utt} has duration {total_mel_len*hop_length//target_sample_rate}s out of range [{min_secs}, {max_secs}]."
bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets)
utts[bucket_i].append(utt)
ref_rms_list[bucket_i].append(ref_rms)
ref_mels[bucket_i].append(ref_mel)
ref_mel_lens[bucket_i].append(ref_mel_len)
total_mel_lens[bucket_i].append(total_mel_len)
final_text_list[bucket_i].extend(text_list)
batch_accum[bucket_i] += total_mel_len
if batch_accum[bucket_i] >= infer_batch_size:
# print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
prompts_all.append(
(
utts[bucket_i],
ref_rms_list[bucket_i],
padded_mel_batch(ref_mels[bucket_i]),
ref_mel_lens[bucket_i],
total_mel_lens[bucket_i],
final_text_list[bucket_i],
)
)
batch_accum[bucket_i] = 0
(
utts[bucket_i],
ref_rms_list[bucket_i],
ref_mels[bucket_i],
ref_mel_lens[bucket_i],
total_mel_lens[bucket_i],
final_text_list[bucket_i],
) = [], [], [], [], [], []
# add residual
for bucket_i, bucket_frames in enumerate(batch_accum):
if bucket_frames > 0:
prompts_all.append(
(
utts[bucket_i],
ref_rms_list[bucket_i],
padded_mel_batch(ref_mels[bucket_i]),
ref_mel_lens[bucket_i],
total_mel_lens[bucket_i],
final_text_list[bucket_i],
)
)
# not only leave easy work for last workers
random.seed(666)
random.shuffle(prompts_all)
return prompts_all
# get wav_res_ref_text of seed-tts test metalst
# https://github.com/BytedanceSpeech/seed-tts-eval
def get_seed_tts_test(metalst, gen_wav_dir, gpus):
f = open(metalst)
lines = f.readlines()
f.close()
test_set_ = []
for line in tqdm(lines):
if len(line.strip().split("|")) == 5:
utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
elif len(line.strip().split("|")) == 4:
utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")):
continue
gen_wav = os.path.join(gen_wav_dir, utt + ".wav")
if not os.path.isabs(prompt_wav):
prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
test_set_.append((gen_wav, prompt_wav, gt_text))
num_jobs = len(gpus)
if num_jobs == 1:
return [(gpus[0], test_set_)]
wav_per_job = len(test_set_) // num_jobs + 1
test_set = []
for i in range(num_jobs):
test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
return test_set
# get librispeech test-clean cross sentence test
def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False):
f = open(metalst)
lines = f.readlines()
f.close()
test_set_ = []
for line in tqdm(lines):
ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
if eval_ground_truth:
gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
else:
if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")):
raise FileNotFoundError(f"Generated wav not found: {gen_utt}")
gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav")
ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
test_set_.append((gen_wav, ref_wav, gen_txt))
num_jobs = len(gpus)
if num_jobs == 1:
return [(gpus[0], test_set_)]
wav_per_job = len(test_set_) // num_jobs + 1
test_set = []
for i in range(num_jobs):
test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
return test_set
# load asr model
def load_asr_model(lang, ckpt_dir=""):
if lang == "zh":
from funasr import AutoModel
model = AutoModel(
model=os.path.join(ckpt_dir, "paraformer-zh"),
# vad_model = os.path.join(ckpt_dir, "fsmn-vad"),
# punc_model = os.path.join(ckpt_dir, "ct-punc"),
# spk_model = os.path.join(ckpt_dir, "cam++"),
disable_update=True,
) # following seed-tts setting
elif lang == "en":
from faster_whisper import WhisperModel
model_size = "large-v3" if ckpt_dir == "" else ckpt_dir
model = WhisperModel(model_size, device="cuda", compute_type="float16")
return model
# WER Evaluation, the way Seed-TTS does
def run_asr_wer(args):
rank, lang, test_set, ckpt_dir = args
if lang == "zh":
import zhconv
torch.cuda.set_device(rank)
elif lang == "en":
os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
else:
raise NotImplementedError(
"lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now."
)
asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir)
from zhon.hanzi import punctuation
punctuation_all = punctuation + string.punctuation
wers = []
from jiwer import compute_measures
for gen_wav, prompt_wav, truth in tqdm(test_set):
if lang == "zh":
res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True)
hypo = res[0]["text"]
hypo = zhconv.convert(hypo, "zh-cn")
elif lang == "en":
segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en")
hypo = ""
for segment in segments:
hypo = hypo + " " + segment.text
# raw_truth = truth
# raw_hypo = hypo
for x in punctuation_all:
truth = truth.replace(x, "")
hypo = hypo.replace(x, "")
truth = truth.replace(" ", " ")
hypo = hypo.replace(" ", " ")
if lang == "zh":
truth = " ".join([x for x in truth])
hypo = " ".join([x for x in hypo])
elif lang == "en":
truth = truth.lower()
hypo = hypo.lower()
measures = compute_measures(truth, hypo)
wer = measures["wer"]
# ref_list = truth.split(" ")
# subs = measures["substitutions"] / len(ref_list)
# dele = measures["deletions"] / len(ref_list)
# inse = measures["insertions"] / len(ref_list)
wers.append(wer)
return wers
# SIM Evaluation
def run_sim(args):
rank, test_set, ckpt_dir = args
device = f"cuda:{rank}"
model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None)
state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
model.load_state_dict(state_dict["model"], strict=False)
use_gpu = True if torch.cuda.is_available() else False
if use_gpu:
model = model.cuda(device)
model.eval()
sim_list = []
for wav1, wav2, truth in tqdm(test_set):
wav1, sr1 = torchaudio.load(wav1)
wav2, sr2 = torchaudio.load(wav2)
resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
wav1 = resample1(wav1)
wav2 = resample2(wav2)
if use_gpu:
wav1 = wav1.cuda(device)
wav2 = wav2.cuda(device)
with torch.no_grad():
emb1 = model(wav1)
emb2 = model(wav2)
sim = F.cosine_similarity(emb1, emb2)[0].item()
# print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
sim_list.append(sim)
return sim_list