162 lines
4.9 KiB
Python
Executable File
162 lines
4.9 KiB
Python
Executable File
#!/usr/bin/env python3
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from datasets import load_dataset, load_metric, Audio, Dataset
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from transformers import pipeline, AutoFeatureExtractor
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import re
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import argparse
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import unicodedata
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from typing import Dict
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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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log_outputs = args.log_outputs
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dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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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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# 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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# print & log results
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result_str = (
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f"WER: {wer_result}\n"
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f"CER: {cer_result}"
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)
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print(result_str)
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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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# 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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with open(pred_file, "w") as p, open(target_file, "w") as t:
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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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result.map(write_to_file, with_indices=True)
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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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CHARS = {
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'ü': 'ue',
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'ö': 'oe',
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'ï': 'i',
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'ë': 'e',
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'ã': 'a',
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'à': 'á',
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'ø': 'o',
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'è': 'é',
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'ê': 'é',
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'å': 'ó',
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'î': 'i',
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'ñ': 'ň',
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'ç': 's',
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'ż': 'ž',
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'ł': 'w',
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'ć': 'č',
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'þ': 't',
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'ß': 'ss',
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'ę': 'en',
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'ą': 'an',
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'æ': 'ae',
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}
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def replace_chars(sentence):
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result = ''
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for ch in sentence:
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new = CHARS[ch] if ch in CHARS else ch
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result += new
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return result
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\/\"\“\„\%\”\<EFBFBD>\–\'\`\«\»\—\’\…\³]'
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text = text.lower()
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# normalize non-standard (stylized) unicode characters
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text = unicodedata.normalize('NFKC', text)
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# remove punctuation
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text = re.sub(chars_to_ignore_regex, "", text)
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text = replace_chars(text)
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# Let's also make sure we split on all kinds of newlines, spaces, etc...
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text = " ".join(text.split())
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return text
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def main(args):
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# load dataset
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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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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# 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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# resample audio
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dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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# load eval pipeline
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asr = pipeline("automatic-speech-recognition", model=args.model_id)
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# map function to decode audio
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def map_to_pred(batch):
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prediction = asr(batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s)
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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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# run inference on all examples
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result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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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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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
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)
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parser.add_argument(
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"--dataset", type=str, required=True, help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 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(
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"--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
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)
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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 None. For long audio files a good value would be 5.0 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 None. For long audio files a good value would be 1.0 seconds."
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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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main(args)
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