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wav2vec2-xls-r-300m-sr-cv8

Automatic Speech Recognition Transformers PyTorch Serbian wav2vec2 mozilla-foundation/common_voice_8_0 generated_from_trainer robust-speech-event xlsr-fine-tuning-week hf-asr-leaderboard Eval Results Inference Endpoints

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wav2vec2-xls-r-300m-sr-cv8 / eval.py

comodoro

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	#!/usr/bin/env python3
	from datasets import load_dataset, load_metric, Audio, Dataset
	from transformers import pipeline, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2ForCTC
	import os
	import re
	import argparse
	import unicodedata
	from typing import Dict


	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 = {
	'ü': 'ue',
	'ö': 'oe',
	'ï': 'i',
	'ë': 'e',
	'ä': 'ae',
	'ã': 'a',
	'à': 'á',
	'ø': 'o',
	'è': 'é',
	'ê': 'é',
	'å': 'ó',
	'î': 'i',
	'ñ': 'ň',
	'ç': 's',
	'ľ': 'l',
	'ż': 'ž',
	'ł': 'w',
	'ć': 'č',
	'þ': 't',
	'ß': 'ss',
	'ę': 'en',
	'ą': 'an',
	'æ': 'ae',
	}

	def replace_chars(sentence):
	result = ''
	for ch in sentence:
	new = CHARS[ch] if ch in CHARS else ch
	result += new

	return result

	chars_to_ignore_regex = '[\,\?\.\!\-\;\:\/\"\“\„\%\”\�\–\'\`\«\»\—\’\…]'

	text = text.lower()
	# normalize non-standard (stylized) unicode characters
	text = unicodedata.normalize('NFKC', text)
	# remove punctuation
	text = re.sub(chars_to_ignore_regex, "", text)
	text = replace_chars(text)

	# Let's also make sure we split on all kinds of newlines, spaces, etc...
	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
	if args.limit:
	dataset = dataset.select(range(limit))


	asr = None
	feature_extractor = None

	if not args.model_id and not args.path:
	raise RuntimeError('No model given!')

	if not args.model_id:
	model = Wav2Vec2ForCTC.from_pretrained(args.path)
	tokenizer = AutoTokenizer.from_pretrained(args.path)
	feature_extractor = AutoFeatureExtractor.from_pretrained(args.path)

	# load eval pipeline
	asr = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)

	else:
	feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
	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

	# load processor
	sampling_rate = feature_extractor.sampling_rate

	# resample audio
	dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))

	# 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, help="Model identifier. Should be loadable with 🤗 Transformers", default=''
	)
	parser.add_argument(
	"--dataset", type=str, required=True, help="Dataset name to evaluate the model. 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."
	)
	parser.add_argument(
	"--path", type=str, help="If set and model_id is not set, use local model from this path.", default=''
	)
	parser.add_argument(
	"--limit", type=int, help="Not required. If greater than zero, select a subset of this size from the dataset.", default=0
	)
	args = parser.parse_args()

	main(args)