eval.py

#!/usr/bin/env python3
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from num2words import num2words as n2w

from transformers import AutoFeatureExtractor, AutoModelForCTC, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
# from pyctcdecode import BeamSearchDecoderCTC


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
    lm = "withLM" if args.use_lm else "noLM"
    model_id = args.model_id.replace("/", "_").replace(".", "")
    dataset_id = "_".join([model_id] + args.dataset.split("/") + [args.config, args.split, lm])

    # 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"{dataset_id}\nWER: {wer_result}\nCER: {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, dataset: 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()) + " "

    if dataset.lower().endswith("nst"):
        text = text.lower()
        text = text.replace("(...vær stille under dette opptaket...)", "")
        text = re.sub('[áàâ]', 'a', text)
        text = re.sub('[ä]', 'æ', text)
        text = re.sub('[éèëê]', 'e', text)
        text = re.sub('[íìïî]', 'i', text)
        text = re.sub('[óòöô]', 'o', text)
        text = re.sub('[ö]', 'ø', text)
        text = re.sub('[ç]', 'c', text)
        text = re.sub('[úùüû]', 'u', text)
        # text = re.sub('\\(?=(Punktum|Komma|Utropstegn|Spørsmålstegn))', ' ', text)
        text = re.sub('\s+', ' ', text)
    elif dataset.lower().endswith("npsc"):
        text = re.sub('[áàâ]', 'a', text)
        text = re.sub('[ä]', 'æ', text)
        text = re.sub('[éèëê]', 'e', text)
        text = re.sub('[íìïî]', 'i', text)
        text = re.sub('[óòöô]', 'o', text)
        text = re.sub('[ö]', 'ø', text)
        text = re.sub('[ç]', 'c', text)
        text = re.sub('[úùüû]', 'u', text)
        text = re.sub('\s+', ' ', text)
    elif dataset.lower().endswith("fleurs"):
        text = re.sub('[áàâ]', 'a', text)
        text = re.sub('[ä]', 'æ', text)
        text = re.sub('[éèëê]', 'e', text)
        text = re.sub('[íìïî]', 'i', text)
        text = re.sub('[óòöô]', 'o', text)
        text = re.sub('[ö]', 'ø', text)
        text = re.sub('[ç]', 'c', text)
        text = re.sub('[úùüû]', 'u', text)
        text = re.compile(r"-?[1-9][\d.]*").sub(lambda x: n2w(x.group(0), lang="no"), text)
        text = re.sub('\s+', ' ', text)
    text = re.sub("<ee(eh)?>", "e", text)
    text = re.sub("<mmm?>", "m", text)
    text = re.sub("<qq>", "q", text)
    text = re.sub("<inaudible>", "i", 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 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
    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)

    model_instance = AutoModelForCTC.from_pretrained(args.model_id)
    if args.use_lm:
        processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
        decoder = processor.decoder
    else:
        processor = Wav2Vec2Processor.from_pretrained(args.model_id)
        decoder = None
    asr = pipeline(
        "automatic-speech-recognition",
        model=model_instance,
        tokenizer=processor.tokenizer, 
        feature_extractor=processor.feature_extractor,
        decoder=decoder,
        device=args.device
    )

    # feature_extractor_dict, _ = Wav2Vec2FeatureExtractor.get_feature_extractor_dict(args.model_id)
    # feature_extractor_dict["processor_class"] = "Wav2Vec2Processor" if not args.use_lm else "Wav2Vec2ProcessorWithLM"
    # feature_extractor = Wav2Vec2FeatureExtractor.from_dict(feature_extractor_dict)

    # asr = pipeline("automatic-speech-recognition", model=args.model_id, feature_extractor=feature_extractor, device=args.device, decoder=BeamSearchDecoderCTC.load_from_dir("./"))

    # 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[args.text_column]
        batch["target"] = normalize_text(args.text_column, args.dataset)
        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(
        "--text_column", type=str, default="text", help="Column name containing the transcription."
    )
    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.",
    )
    parser.add_argument(
        "--use_lm", action="store_true", help="If defined, use included language model as the decoder."
    )
    args = parser.parse_args()

    main(args)