File size: 5,348 Bytes
18c7dbf 1b7a748 18c7dbf |
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 |
#!/usr/bin/env python3
!pip install mecab-python3 unidic-lite pykakasi
from datasets import load_dataset, load_metric, Audio, Dataset
from transformers import pipeline, AutoFeatureExtractor
import re
import argparse
import unicodedata
from typing import Dict
import MeCab
import pykakasi
import torch
def log_results(result: Dataset, args: Dict[str, str]):
""" DO NOT CHANGE. This function computes and logs the result metrics. """
log_outputs = args.log_outputs
dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
# load metric
wer = load_metric("wer")
cer = load_metric("cer")
# compute metrics
wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
# print & log results
result_str = (
f"WER: {wer_result}\n"
f"CER: {cer_result}"
)
print(result_str)
with open(f"{dataset_id}_eval_results.txt", "w") as f:
f.write(result_str)
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
pred_file = f"log_{dataset_id}_predictions.txt"
target_file = f"log_{dataset_id}_targets.txt"
with open(pred_file, "w") as p, open(target_file, "w") as t:
# mapping function to write output
def write_to_file(batch, i):
p.write(f"{i}" + "\n")
p.write(batch["prediction"] + "\n")
t.write(f"{i}" + "\n")
t.write(batch["target"] + "\n")
result.map(write_to_file, with_indices=True)
def normalize_text(text: str) -> str:
""" DO ADAPT FOR YOUR USE CASE. this function normalizes the target text. """
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\�\‘\、\。\．\！\，\・\―\─\~\｢\｣\『\』\〆\｡\※\[\]\{\}\「\」\〇\？\…\=\+\〜\'\－\･\(\)\/\—\`\’\–]'
FULLWIDTH_TO_HALFWIDTH = str.maketrans(
' ０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ！゛＃＄％＆（）＊＋、ー。／：；〈＝〉？＠［］＾＿‘｛｜｝～',
' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&()*+,-./:;<=>?@[]^_`{|}~',
)
wakati = MeCab.Tagger("-Owakati")
kakasi = pykakasi.kakasi()
kakasi.setMode("J","H") # kanji to hiragana
kakasi.setMode("K","H") # katakana to hiragana
conv = kakasi.getConverter()
def fullwidth_to_halfwidth(s):
return s.translate(FULLWIDTH_TO_HALFWIDTH)
text = fullwidth_to_halfwidth(text)
text = re.sub(chars_to_ignore_regex, " ", text).lower()
text = wakati.parse(text)
text = conv.do(text)
text = " ".join(text.split()) + " "
return text
def main(args):
# load dataset
dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
sampling_rate = feature_extractor.sampling_rate
# resample audio
dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
# load eval pipeline
device = torch.cuda.current_device() if torch.cuda.is_available() else -1
asr = pipeline("automatic-speech-recognition", model=args.model_id, device = device)
# map function to decode audio
def map_to_pred(batch):
prediction = asr(batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s)
batch["prediction"] = prediction["text"]
batch["target"] = normalize_text(batch["sentence"])
return batch
# run inference on all examples
result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
# compute and log_results
# do not change function below
log_results(result, args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset", type=str, required=True, help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets"
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument(
"--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
)
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to None. For long audio files a good value would be 5.0 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to None. For long audio files a good value would be 1.0 seconds."
)
parser.add_argument(
"--log_outputs", action='store_true', help="If defined, write outputs to log file for analysis."
)
args = parser.parse_args()
main(args)
|