add run_evaluation.py

133f88a over 2 years ago

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	#!/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, Wav2Vec2ForCTC, Wav2Vec2Processor, set_seed
	from pyctcdecode import build_ctcdecoder
	from multiprocessing import Pool

	class KenLM:
	def __init__(self, tokenizer, model_name, unigrams=None, num_workers=8, beam_width=128):
	self.num_workers = num_workers
	self.beam_width = beam_width
	vocab_dict = tokenizer.get_vocab()
	self.vocabulary = [x[0] for x in sorted(vocab_dict.items(), key=lambda x: x[1], reverse=False)]
	self.vocabulary = self.vocabulary[:-1]
	self.decoder = build_ctcdecoder(self.vocabulary, model_name, unigrams=unigrams)

	@staticmethod
	def lm_postprocess(text):
	return ' '.join([x if len(x) > 1 else "" for x in text.split()]).strip()

	def decode(self, logits):
	probs = logits.cpu().numpy()
	# probs = logits.numpy()
	with Pool(self.num_workers) as pool:
	text = self.decoder.decode_batch(pool, probs)
	text = [KenLM.lm_postprocess(x) for x in text]
	return text

	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 = '[,?.!-;:""%\'"\'\'`…’»«‘“”�éû]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	text = re.sub(chars_to_ignore_regex, "", 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 main(args):
	# load dataset
	dataset = load_dataset(args.dataset, args.config, data_dir=args.data_dir, 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
	if args.device is None:
	args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

	set_seed(42) # set the random seed to have reproducible result.
	processor = Wav2Vec2Processor.from_pretrained(args.model_id)
	model = Wav2Vec2ForCTC.from_pretrained(args.model_id)
	model.to(args.device)
	kenlm = KenLM(processor.tokenizer, "language_model/5gram.bin", unigrams="language_model/unigrams.txt")

	# map function to decode audio
	def map_to_pred(batch):
	inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt", padding=True)
	with torch.no_grad():
	logits = model(inputs.input_values.to(args.device), attention_mask=inputs.attention_mask.to(args.device)).logits
	prediction = kenlm.decode(logits)

	batch["prediction"] = prediction
	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("--data_dir", type=str, required=False, default=None,
	help="The directory contains the dataset")
	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)