from datasets import load_dataset, load_metric, Audio, Dataset from transformers import pipeline, AutoFeatureExtractor 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") pred_string = [element.lower() for element in result["prediction"]] actual = [element.lower() for element in result["target"]] # compute metrics wer_result = wer.compute(references=actual, predictions=pred_string) cer_result = cer.compute(references=actual, predictions=pred_string) # 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 = '[\,\.\!\-\;\:\"\“\%\”\�\'\...\…\–\é]' text = re.sub(chars_to_ignore_regex, "", text).lower().strip() text = re.sub(r"([b-df-hj-np-tv-z])' ([aeiou])", r"\1'\2", text) text = re.sub(r"(-| '|' | +)", " ", text) 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 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 __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)