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- #!/usr/bin/env python3
- 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())
- text = re.sub("[،]", "", text)
- text = re.sub("[؟]", "", text)
- text = re.sub("['َ]", "", text)
- text = re.sub("['ُ]", "", text)
- text = re.sub("['ِ]", "", text)
- text = re.sub("['ّ]", "", text)
- text = re.sub("['ٔ]", "", text)
- text = re.sub("['ٰ]", "", text)
- text = re.sub("[ۂ]", "ہ", text)
- text = re.sub("[ي]", "ی", text)
- text = re.sub("[ؤ]", "و", text)
- # batch["sentence"] = re.sub("[ئ]", 'ى', batch["sentence"])
- text = re.sub("[ى]", "ی", text)
- text = re.sub("[۔]", "", text)
- # 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 path_adjust(batch):
- batch["path"] = "Data/ur/clips/" + str(batch["path"])
- return batch
- def main(args):
- # load dataset
- dataset = load_dataset(args.dataset, args.config, delimiter="\t", split=args.split)
- # 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.map(path_adjust)
- dataset = dataset.cast_column("path", 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["path"]["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 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)
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