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8a3bc32 42742c6 8a3bc32 42742c6 8a3bc32 42742c6 8a3bc32 778443c 8a3bc32 3d1d87d 8a3bc32 8a34d65 42742c6 8a34d65 db0d138 778443c 8a34d65 778443c 42742c6 778443c 42742c6 8a3bc32 42742c6 | 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 | from pathlib import Path
import time
import csv
from funasr_onnx import SeacoParaformer, CT_Transformer, Fsmn_vad
from scripts.asr_utils import get_origin_text_dict, get_text_distance
def save_csv(file_path, rows):
with open(file_path, "w", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerows(rows)
print(f"write csv to {file_path}")
def load_model(quantize=True):
model_dir = Path("/Users/jeqin/work/code/Translator/python_server/moyoyo_asr_models")
asr_model_path = model_dir / 'speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
vad_model_path = model_dir / 'speech_fsmn_vad_zh-cn-16k-common-pytorch'
punc_model_path = model_dir / 'punc_ct-transformer_cn-en-common-vocab471067-large'
t0 = time.time()
quantize = True
vad_model = Fsmn_vad(vad_model_path, quantize=quantize)
asr_model = SeacoParaformer(asr_model_path, quantize=quantize)
punc_model = CT_Transformer(punc_model_path, quantize=quantize)
t1 = time.time()
print("load model time:", t1 - t0)
return vad_model, asr_model, punc_model
def inference(vad_model, asr_model, punc_model, audio:Path):
t1 = time.time()
# vad_res = vad_model(str(audio))
asr_res = asr_model(str(audio), hotwords="")
text = ""
if len(asr_res) > 0:
asr_text = asr_res[0]["preds"]
result = punc_model(asr_text)
text = result[0]
t4 = time.time()
t = t4-t1
return text, t
def run_once(audio):
quantize = True
vad_model, asr_model, punc_model = load_model(quantize)
text, t = inference(vad_model, asr_model, punc_model, audio)
print(text)
def run_recordings():
quantize = True
vad_model, asr_model, punc_model = load_model(quantize)
audios = Path("../test_data/recordings/")
rows = [["file_name", "time", "inference_result"]]
original = get_origin_text_dict()
for audio in sorted(audios.glob("*.wav"), key=lambda x: int(x.stem)):
text, t = inference(vad_model, asr_model, punc_model, audio)
d, nd, diff = get_text_distance(original[audio.stem], text)
rows.append([audio.name, round(t, 3), text, d, round(nd,3), diff]) # f"{audio.parent.name}/{audio.name}"
file_name = "csv/funasr_quant.csv" if quantize else "funasr_onnx.csv"
save_csv(file_name, rows)
def run_test_audios():
quantize = True
vad_model, asr_model, punc_model = load_model(quantize)
audios = Path("../test_data/audio_clips/")
rows = [["file_name", "time", "inference_result"]]
for audio in sorted(audios.glob("*s/zh*.wav")):
text, t = inference(vad_model, asr_model, punc_model, audio)
rows.append([f"{audio.parent.name}/{audio.name}", round(t, 3), text])
file_name = "csv/funasr_quant.csv" if quantize else "funasr_onnx.csv"
save_csv(file_name, rows)
def run_test_dataset():
from test_data.audios import read_dataset
quantize = True
vad_model, asr_model, punc_model = load_model(quantize)
test_data = Path("../test_data/AIShell/dataset/dataset.txt")
audio_parent = Path("../test_data/")
rows = [["file_name", "time", "inference_result"]]
result_list = []
count = 0
try:
for audio_path, sentence, duration in read_dataset(test_data):
count += 1
print(f"processing {count}: {audio_path}")
t1 = time.time()
text, t = inference(vad_model, asr_model, punc_model, audio_parent/audio_path)
t = time.time() - t1
print("inference time:", t)
print(text)
result_list.append({
"index": count,
"audio_path": audio_path,
"reference": sentence,
"duration": duration,
"inference_time": round(t, 3),
"inference_result": text
})
except Exception as e:
print(e)
except KeyboardInterrupt as e:
print(e)
import json
with open("csv/funasr_dataset_results.json", "w", encoding="utf-8") as f:
json.dump(result_list, f, ensure_ascii=False, indent=2)
def run_test_emilia():
from test_data.audios import read_emilia
quantize = True
vad_model, asr_model, punc_model = load_model(quantize)
parent = Path("../test_data/ZH-B000000")
result_list = []
count = 0
try:
for audio_path, sentence, duration in read_emilia(parent, count_limit=5000):
count += 1
print(f"processing {count}: {audio_path.name}")
text, t = inference(vad_model, asr_model, punc_model, audio_path)
print("inference time:", t)
print(text)
result_list.append({
"index": count,
"audio_path": audio_path.name,
"reference": sentence,
"duration": duration,
"inference_time": round(t, 3),
"inference_result": text
})
except Exception as e:
print(e)
except KeyboardInterrupt as e:
print(e)
import json
with open("csv/funasr_emilia_results.json", "w", encoding="utf-8") as f:
json.dump(result_list, f, ensure_ascii=False, indent=2)
def run_test_wenet():
from test_data.audios import read_wenet
quantize = True
vad_model, asr_model, punc_model = load_model(quantize)
result_list = []
count = 0
try:
for audio_path, sentence in read_wenet(count_limit=5000):
count += 1
print(f"processing {count}: {audio_path.name}")
text, t = inference(vad_model, asr_model, punc_model, audio_path)
print("inference time:", t)
print(text)
result_list.append({
"index": count,
"audio_path": audio_path.name,
"reference": sentence,
# "duration": duration,
"inference_time": round(t, 3),
"inference_result": text
})
# except Exception as e:
# print(e)
except KeyboardInterrupt as e:
print(e)
import json
with open("csv/funasr_wenet_results.json", "w", encoding="utf-8") as f:
json.dump(result_list, f, ensure_ascii=False, indent=2)
if __name__ == '__main__':
# run_recordings()
run_test_wenet()
# run_once(Path("/Users/jeqin/work/code/TestTranslator/test_data/audio_clips/zhengyaowei-part1.mp3"))
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