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()) # 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, 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 = 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["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[args.text_column_name]) 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( "--text_column_name", type=str, default="text", help="The name of the dataset column containing the text data. Defaults to 'text'", ) 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)