up

eval.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python3
-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")
-
-    # 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 = text.lower()
-    # normalize non-standard (stylized) unicode characters
-    text = unicodedata.normalize('NFKC', text)
-    # remove punctuation
-    text = re.sub(chars_to_ignore_regex, "", 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
-    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)