chmanoj commited on
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6a99894
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1 Parent(s): dbca3b5

Add eval results with LM

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Files changed (2) hide show
  1. eval.py +155 -0
  2. openslr_SLR66_te_test_eval_results.txt +2 -0
eval.py ADDED
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+ #!/usr/bin/env python3
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+ import argparse
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+ import re
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+ from typing import Dict
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+
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+ from datasets import Audio, Dataset, load_dataset, load_metric, DatasetDict
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+
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+ from transformers import AutoFeatureExtractor, pipeline
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+
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+
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+ def log_results(result: Dataset, args: Dict[str, str]):
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+ """DO NOT CHANGE. This function computes and logs the result metrics."""
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+
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+ log_outputs = args.log_outputs
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+ dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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+
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+ # load metric
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+ wer = load_metric("wer")
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+ cer = load_metric("cer")
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+
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+ # compute metrics
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+ wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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+ cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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+
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+ # print & log results
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+ result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
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+ print(result_str)
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+
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+ with open(f"{dataset_id}_eval_results.txt", "w") as f:
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+ f.write(result_str)
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+
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+ # log all results in text file. Possibly interesting for analysis
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+ if log_outputs is not None:
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+ pred_file = f"log_{dataset_id}_predictions.txt"
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+ target_file = f"log_{dataset_id}_targets.txt"
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+
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+ with open(pred_file, "w") as p, open(target_file, "w") as t:
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+
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+ # mapping function to write output
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+ def write_to_file(batch, i):
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+ p.write(f"{i}" + "\n")
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+ p.write(batch["prediction"] + "\n")
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+ t.write(f"{i}" + "\n")
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+ t.write(batch["target"] + "\n")
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+
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+ result.map(write_to_file, with_indices=True)
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+
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+
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+ def normalize_text(text: str) -> str:
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+ """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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+
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+ chars_to_ignore_regex = '[,?.!\-\;\:"β€œ%β€˜β€οΏ½β€”β€™β€¦β€“]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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+
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+ text = re.sub(chars_to_ignore_regex, "", text.lower())
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+
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+ # In addition, we can normalize the target text, e.g. removing new lines characters etc...
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+ # note that order is important here!
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+ token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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+
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+ for t in token_sequences_to_ignore:
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+ text = " ".join(text.split(t))
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+
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+ return text
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+
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+
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+ def get_telugu_dataset(validation_split=False):
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+ dataset = load_dataset('openslr', 'SLR66')
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+
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+ seed=1242
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+
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+ if validation_split:
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+ train_testvalid = dataset['train'].train_test_split(test_size=0.2, seed=seed)
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+ # Split the 10% test + valid in half test, half valid
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+ test_valid = train_testvalid['test'].train_test_split(test_size=0.33, seed=seed)
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+ # gather everyone if you want to have a single DatasetDict
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+ out_dataset = DatasetDict({
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+ 'train': train_testvalid['train'],
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+ 'test': test_valid['test'],
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+ 'valid': test_valid['train']})
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+ else:
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+ train_testvalid = dataset['train'].train_test_split(test_size=0.25, seed=seed)
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+ out_dataset = DatasetDict({
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+ 'train': train_testvalid['train'],
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+ 'test': train_testvalid['test']})
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+ return out_dataset
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+
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+
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+ def main(args):
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+ # load dataset
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+ te_dataset = get_telugu_dataset(validation_split=False)
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+ def load_te_dataset(split):
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+ return te_dataset[split]
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+
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+ # dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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+ dataset = load_te_dataset(split=args.split)
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+
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+ # for testing: only process the first two examples as a test
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+ # dataset = dataset.select(range(10))
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+
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+ # load processor
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+ feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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+ sampling_rate = feature_extractor.sampling_rate
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+
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+ # resample audio
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+ dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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+
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+ # load eval pipeline
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+ asr = pipeline("automatic-speech-recognition", model=args.model_id, device=0)
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+
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+ # map function to decode audio
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+ def map_to_pred(batch):
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+ prediction = asr(
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+ batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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+ )
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+
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+ batch["prediction"] = prediction["text"]
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+ batch["target"] = normalize_text(batch["sentence"])
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+ return batch
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+
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+ # run inference on all examples
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+ result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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+
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+ # compute and log_results
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+ # do not change function below
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+ log_results(result, args)
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+
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+
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+ if __name__ == "__main__":
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+ parser = argparse.ArgumentParser()
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+
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+ parser.add_argument(
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+ "--model_id", type=str, required=True, help="Model identifier. Should be loadable with πŸ€— Transformers"
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+ )
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+ parser.add_argument(
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+ "--dataset",
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+ type=str,
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+ required=True,
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+ help="Dataset name to evaluate the `model_id`. Should be loadable with πŸ€— Datasets",
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+ )
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+ parser.add_argument(
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+ "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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+ )
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+ parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
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+ parser.add_argument(
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+ "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
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+ )
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+ parser.add_argument(
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+ "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
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+ )
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+ parser.add_argument(
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+ "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
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+ )
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+ args = parser.parse_args()
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+
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+ main(args)
openslr_SLR66_te_test_eval_results.txt ADDED
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+ WER: 0.24695121951219512
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+ CER: 0.04861934182322532