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Upload eval.py

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  1. eval.py +158 -0
eval.py ADDED
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+ from datasets import load_dataset, load_metric, Audio, Dataset
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+ from transformers import pipeline, AutoFeatureExtractor, AutoTokenizer, AutoConfig, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
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+ import re
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+ import torch
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+ import argparse
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+ from typing import Dict
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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 = (
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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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+
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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 = [",", "?", "¿", ".", "!", "¡", ";", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
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+ "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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+ "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
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+ "、", "﹂", "﹁", "‧", "~", "﹏", ",", "{", "}", "(", ")", "[", "]", "【", "】", "‥", "〽",
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+ "『", "』", "〝", "〟", "⟨", "⟩", "〜", ":", "!", "?", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
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+ chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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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 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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+
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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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+ if args.greedy:
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+ processor = Wav2Vec2Processor.from_pretrained(args.model_id)
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+ decoder = None
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+ else:
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+ processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
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+ decoder = processor.decoder
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+
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+ feature_extractor = processor.feature_extractor
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+ tokenizer = processor.tokenizer
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+
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+ # resample audio
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+ dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
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+
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+ # load eval pipeline
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+ if args.device is None:
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+ args.device = 0 if torch.cuda.is_available() else -1
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+
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+ config = AutoConfig.from_pretrained(args.model_id)
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+ model = AutoModelForCTC.from_pretrained(args.model_id)
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+
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+ #asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
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+ asr = pipeline("automatic-speech-recognition", config=config, model=model, tokenizer=tokenizer,
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+ feature_extractor=feature_extractor, decoder=decoder, device=args.device)
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+
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+
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+ # map function to decode audio
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+ def map_to_pred(batch, args=args, asr=asr):
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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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+
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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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+ # filtering out empty targets
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+ result = result.filter(lambda example: example["target"] != "")
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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 🤗 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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+ parser.add_argument(
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+ "--greedy", action='store_true', help="If defined, the LM will be ignored during inference."
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+ )
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+ parser.add_argument(
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+ "--device",
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+ type=int,
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+ default=None,
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+ help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
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+ )
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+ args = parser.parse_args()
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+
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+ main(args)