Urdu-ASR-SOTA / eval.py
Abid
eval and markdown a61ebcb
#!/usr/bin/env python3
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
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 = """[\!\؛\،\٫\؟\۔\٪\"\'\:\-\‘\’]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
text = re.sub(chars_to_ignore_regex, "", text.lower())
text = re.sub("[،]", "", text)
text = re.sub("[؟]", "", text)
text = re.sub("['َ]", "", text)
text = re.sub("['ُ]", "", text)
text = re.sub("['ِ]", "", text)
text = re.sub("['ّ]", "", text)
text = re.sub("['ٔ]", "", text)
text = re.sub("['ٰ]", "", text)
text = re.sub("[ۂ]", "ہ", text)
text = re.sub("[ي]", "ی", text)
text = re.sub("[ؤ]", "و", text)
# batch["sentence"] = re.sub("[ئ]", 'ى', batch["sentence"])
text = re.sub("[ى]", "ی", text)
text = re.sub("[۔]", "", text)
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
for t in token_sequences_to_ignore:
text = " ".join(text.split(t))
return text
def path_adjust(batch):
batch["path"] = "Data/ur/clips/" + str(batch["path"])
return batch
def main(args):
# load dataset
dataset = load_dataset(args.dataset, args.config, delimiter="\t", split=args.split)
# 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.map(path_adjust)
dataset = dataset.cast_column("path", Audio(sampling_rate=sampling_rate))
# load eval pipeline
if args.device is None:
args.device = 0 if torch.cuda.is_available() else -1
asr = pipeline(
"automatic-speech-recognition", model=args.model_id, device=args.device
)
# map function to decode audio
def map_to_pred(batch):
prediction = asr(
batch["path"]["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 5 seconds.",
)
parser.add_argument(
"--stride_length_s",
type=float,
default=None,
help="Stride of the audio chunks. Defaults to 1 second.",
)
parser.add_argument(
"--log_outputs",
action="store_true",
help="If defined, write outputs to log file for analysis.",
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
args = parser.parse_args()
main(args)