eval_kenlm.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 transformers import AutoFeatureExtractor, pipeline, Wav2Vec2ForCTC, Wav2Vec2Processor, set_seed
from pyctcdecode import build_ctcdecoder
from multiprocessing import Pool

class KenLM:
    def __init__(self, tokenizer, model_name, unigrams=None, num_workers=8, beam_width=128):
        self.num_workers = num_workers
        self.beam_width = beam_width
        vocab_dict = tokenizer.get_vocab()
        self.vocabulary = [x[0] for x in sorted(vocab_dict.items(), key=lambda x: x[1], reverse=False)]
        self.vocabulary = self.vocabulary[:-1]
        self.decoder = build_ctcdecoder(self.vocabulary, model_name, unigrams=unigrams)

    @staticmethod
    def lm_postprocess(text):
        return ' '.join([x if len(x) > 1 else "" for x in text.split()]).strip()

    def decode(self, logits):
        probs = logits.cpu().numpy()
        # probs = logits.numpy()
        with Pool(self.num_workers) as pool:
            text = self.decoder.decode_batch(pool, probs)
            text = [KenLM.lm_postprocess(x) for x in text]
        return text

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 = '[,?.!-;:""%\'"\'\'`…’»«‘“”�éû]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

    text = re.sub(chars_to_ignore_regex, "", text.lower())

    # 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, data_dir=args.data_dir, 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 = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    set_seed(42)  # set the random seed to have reproducible result.
    processor = Wav2Vec2Processor.from_pretrained(args.model_id)
    model = Wav2Vec2ForCTC.from_pretrained(args.model_id)
    model.to(args.device)
    kenlm = KenLM(processor.tokenizer, "language_model/5gram.bin", unigrams="language_model/unigrams.txt")

    # map function to decode audio
    def map_to_pred(batch):
        inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt", padding=True)
        with torch.no_grad():
            logits = model(inputs.input_values.to(args.device), attention_mask=inputs.attention_mask.to(args.device)).logits
        prediction = kenlm.decode(logits)

        batch["prediction"] = prediction
        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("--data_dir", type=str, required=False, default=None,
                        help="The directory contains the dataset")
    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)