xls-r-uzbek-cv8 / eval_lm.py

add code for using kenlm

007afbd about 2 years ago

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7.63 kB

	#!/usr/bin/env python3
	import argparse
	import functools
	import re
	import string
	import unidecode
	from typing import Dict

	from datasets import Audio, Dataset, DatasetDict, load_dataset, load_metric

	from transformers import AutoFeatureExtractor, AutoTokenizer, 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 = f'[{re.escape(string.punctuation)}]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	text = re.sub(
	chars_to_ignore_regex,
	"",
	re.sub("['`´]", "’", # elsewhere probably meant as glottal stop
	re.sub("([og])['`´]", "\g<1>‘", # after o/g indicate modified char
	unidecode.unidecode(text).lower()
	)
	)
	) + " "

	# 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 create_vocabulary_from_data(
	datasets: DatasetDict,
	word_delimiter_token = None,
	unk_token = None,
	pad_token = None,
	):
	# Given training and test labels create vocabulary
	def extract_all_chars(batch):
	all_text = " ".join(batch["target"])
	vocab = list(set(all_text))
	return {"vocab": [vocab], "all_text": [all_text]}

	vocabs = datasets.map(
	extract_all_chars,
	batched=True,
	batch_size=-1,
	keep_in_memory=True,
	remove_columns=datasets["test"].column_names,
	)


	vocab_dict = {v: k for k, v in enumerate(sorted(vocabs["test"]["vocab"][0]))}

	# replace white space with delimiter token
	if word_delimiter_token is not None:
	vocab_dict[word_delimiter_token] = vocab_dict[" "]
	del vocab_dict[" "]

	# add unk and pad token
	if unk_token is not None:
	vocab_dict[unk_token] = len(vocab_dict)

	if pad_token is not None:
	vocab_dict[pad_token] = len(vocab_dict)

	return vocab_dict


	def asr_pipeline_lm(model_id):
	from transformers.models.auto.modeling_auto import AutoModelForCTC
	from transformers.models.auto.processing_auto import AutoProcessor
	import torch

	model = AutoModelForCTC.from_pretrained(model_id)
	processor = AutoProcessor.from_pretrained(model_id)

	def asr(audio_array, sampling_rate):
	input_values = processor(
	audio_array,
	return_tensors="pt",
	sampling_rate=sampling_rate
	).input_values
	with torch.no_grad():
	logits = model(input_values).logits
	return processor.batch_decode(logits.numpy()).text

	return asr


	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))

	if args.use_lm:
	asr = asr_pipeline_lm(args.model_id)

	def map_to_pred(batch):
	prediction = asr(batch["audio"]["array"])
	batch["prediction"] = prediction[0]
	batch["target"] = normalize_text(batch["sentence"])
	return batch

	else:
	# load eval pipeline
	asr = pipeline("automatic-speech-recognition", model=args.model_id)

	# 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 args.check_vocab:
	tokenizer = AutoTokenizer.from_pretrained(args.model_id)
	unk_token = "[UNK]"
	pad_token = "[PAD]"
	word_delimiter_token = "\|"
	raw_datasets = DatasetDict({"test": result})
	vocab_dict = create_vocabulary_from_data(
	raw_datasets,
	word_delimiter_token=word_delimiter_token,
	unk_token=unk_token,
	pad_token=pad_token,
	)
	print(vocab_dict)
	print("OOV chars:", set(vocab_dict) - set(tokenizer.get_vocab()))


	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(
	"--check_vocab", action="store_true", help="Verify that normalized target text is within character set"
	)
	parser.add_argument(
	"--use-lm", action="store_true", help="Use kenlm decoder"
	)
	args = parser.parse_args()

	main(args)