Spaces:
Running
Running
File size: 8,443 Bytes
9d9ac6c |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 |
import argparse
import os
import string
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
import librosa
import numpy as np
import torch
from evaluate import load
from pymcd.mcd import Calculate_MCD
from tqdm import tqdm
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, Wav2Vec2FeatureExtractor, WavLMForXVector, pipeline
def convert_numbers_to_words(text):
"""Convert single digits in text to words with spaces"""
number_word_map = {
"0": "zero",
"1": "one",
"2": "two",
"3": "three",
"4": "four",
"5": "five",
"6": "six",
"7": "seven",
"8": "eight",
"9": "nine",
}
words = text.split()
converted_words = []
for word in words:
# Check if the word contains both letters and numbers (like 'j4')
if any(c.isdigit() for c in word) and any(c.isalpha() for c in word):
# Split the word into parts and convert digits
new_word = ""
for c in word:
if c.isdigit():
new_word += " " + number_word_map[c]
else:
new_word += c
converted_words.append(new_word)
# Check if the word is a single digit
elif word.isdigit() and len(word) == 1:
converted_words.append(number_word_map[word])
else:
converted_words.append(word)
return " ".join(converted_words)
def clean_text(text):
text = convert_numbers_to_words(text)
text = text.translate(str.maketrans("", "", string.punctuation))
text = text.lower()
return text
def wer_pipe(gen_dir: str, target_dir: str, model_id="openai/whisper-large-v3-turbo"):
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
print(f"Using Model: {model_id} for WER Evaluation")
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
).to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
gen_list = list(Path(gen_dir).glob("*.wav"))
for line in tqdm(gen_list, desc="Processing audio files"):
wav = line
if not wav.exists():
continue
text = pipe(librosa.load(wav, sr=16000)[0], generate_kwargs={"language": "english"})["text"]
with open(wav.with_suffix(".asrtxt"), "w") as fw:
fw.write(text)
wer_metric = load("wer")
val_list = list(Path(target_dir).glob("*.txt"))
wer = []
for txt in tqdm(val_list, desc="Calculating WER"):
try:
# Since the original text is automatically transcribed and has not been manually verified, all texts will be cleaned here.
target_text = " ".join(set(txt.read_text().splitlines()))
target_text = clean_text(target_text)
gen_text = " ".join(Path(os.path.join(gen_dir, txt.with_suffix(".asrtxt").name)).read_text().splitlines())
gen_text = clean_text(gen_text)
if target_text == "" or gen_text == "":
continue
wer_ = wer_metric.compute(references=[target_text], predictions=[gen_text])
except Exception as e:
print("Error in wer calculation: ", e)
continue
wer.append(wer_)
return np.mean(wer)
def spk_sim_pipe(gen_dir, target_dir):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/wavlm-base-sv")
model = WavLMForXVector.from_pretrained("microsoft/wavlm-base-sv").cuda()
cosine_sim = torch.nn.CosineSimilarity(dim=-1)
val_list = list(Path(target_dir).glob("*.wav"))
scos = []
for target_wav in tqdm(val_list, desc="Calculating speaker similarity"):
target = librosa.load(target_wav, sr=16000)[0]
gen = librosa.load(os.path.join(gen_dir, target_wav.name), sr=16000)[0]
try:
input1 = feature_extractor(gen, return_tensors="pt", sampling_rate=16000).to("cuda")
embeddings1 = model(**input1).embeddings
input2 = feature_extractor(target, return_tensors="pt", sampling_rate=16000).to("cuda")
embeddings2 = model(**input2).embeddings
similarity = cosine_sim(embeddings1[0], embeddings2[0])
except Exception as e:
print(f"Error in {target_wav}, {e}")
continue
scos.append(similarity.detach().cpu().numpy())
return np.mean(scos)
def calculate_mcd_for_wav(target_wav, gen_dir, mcd_toolbox_dtw, mcd_toolbox_dtw_sl):
_mcd_dtw = mcd_toolbox_dtw.calculate_mcd(target_wav, os.path.join(gen_dir, target_wav.name))
_mcd_dtw_sl = mcd_toolbox_dtw_sl.calculate_mcd(target_wav, os.path.join(gen_dir, target_wav.name))
return _mcd_dtw, _mcd_dtw_sl
def mcd_pipe(gen_dir, target_dir, num_processes=16):
mcd_toolbox_dtw = Calculate_MCD(MCD_mode="dtw")
mcd_toolbox_dtw_sl = Calculate_MCD(MCD_mode="dtw_sl")
val_list = list(Path(target_dir).glob("*.wav"))
mcd_dtw = []
mcd_dtw_sl = []
with ProcessPoolExecutor(max_workers=num_processes) as executor:
futures = [
executor.submit(calculate_mcd_for_wav, target_wav, gen_dir, mcd_toolbox_dtw, mcd_toolbox_dtw_sl)
for target_wav in val_list
]
for future in tqdm(futures, desc="Calculating MCD"):
_mcd_dtw, _mcd_dtw_sl = future.result()
mcd_dtw.append(_mcd_dtw)
mcd_dtw_sl.append(_mcd_dtw_sl)
return np.mean(mcd_dtw), np.mean(mcd_dtw_sl)
def run_all_metrics(gen_dir, target_dir, whisper_model="openai/whisper-large-v3-turbo"):
"""Run all evaluation metrics and return results"""
results = {}
print("Running WER evaluation...")
results["wer"] = wer_pipe(gen_dir, target_dir, model_id=whisper_model)
print("Running speaker similarity evaluation...")
results["speaker_similarity"] = spk_sim_pipe(gen_dir, target_dir)
print("Running MCD evaluation...")
mcd_dtw, mcd_dtw_sl = mcd_pipe(gen_dir, target_dir)
results["mcd_dtw"] = mcd_dtw
results["mcd_dtw_sl"] = mcd_dtw_sl
return results
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Audio evaluation metrics")
parser.add_argument("--gen_dir", type=str, required=True, help="Directory containing generated audio files")
parser.add_argument("--target_dir", type=str, required=True, help="Directory containing target audio files")
parser.add_argument(
"--metric",
type=str,
default="all",
choices=["wer", "spk_sim", "mcd", "all"],
help="Evaluation metric to use",
)
parser.add_argument(
"--whisper_model",
type=str,
default="openai/whisper-large-v3-turbo",
help="Whisper model to use for WER evaluation",
)
# python eval.py --gen_dir path/to/generated --target_dir path/to/target
# keep the name of gen_wav and target_wav the same
args = parser.parse_args()
gen_dir = args.gen_dir
target_dir = args.target_dir
if not os.path.exists(gen_dir):
raise ValueError(f"Generated audio directory does not exist: {gen_dir}")
if not os.path.exists(target_dir):
raise ValueError(f"Target audio directory does not exist: {target_dir}")
if args.metric == "all":
results = run_all_metrics(gen_dir, target_dir, args.whisper_model)
print("\nEvaluation Results:")
print(f"WER: {results['wer']:.4f}")
print(f"Speaker Similarity: {results['speaker_similarity']:.4f}")
print(f"MCD (DTW): {results['mcd_dtw']:.4f}")
print(f"MCD (DTW-SL): {results['mcd_dtw_sl']:.4f}")
elif args.metric == "wer":
wer = wer_pipe(gen_dir, target_dir, model_id=args.whisper_model)
print(f"WER: {wer:.4f}")
elif args.metric == "spk_sim":
spk_sim = spk_sim_pipe(gen_dir, target_dir)
print(f"Speaker Similarity: {spk_sim:.4f}")
elif args.metric == "mcd":
mcd_dtw, mcd_dtw_sl = mcd_pipe(gen_dir, target_dir)
print(f"MCD (DTW): {mcd_dtw:.4f}")
print(f"MCD (DTW-SL): {mcd_dtw_sl:.4f}")
|